CN120910838A - Intelligent nursing remote data processing method and system based on intelligent medical treatment - Google Patents

Abstract

The application discloses an intelligent nursing remote data processing method and system based on intelligent medical treatment, and relates to the technical field of data processing. The method comprises the steps of collecting all nursing data of a patient, analyzing the data quality of the nursing data, screening to obtain basic nursing data, obtaining information of a user viewing the basic nursing data, recording the information as user information, judging to obtain the viewing purpose of the user according to the user information, extracting user identity according to the user information, selecting to obtain primary nursing data, obtaining a history reading record of the nursing data of the user, processing the primary nursing data according to user preferences to obtain intermediate nursing data, obtaining the experience of the user, performing language conversion on the intermediate nursing data to obtain the advanced nursing data, opening the viewing authority of the advanced nursing data for the user to view, and closing the viewing authority of the non-advanced nursing data. The intelligent nursing remote data processing method and the intelligent nursing remote data processing system improve safety of intelligent nursing remote data processing based on intelligent medical treatment.

Description

Intelligent nursing remote data processing method and system based on intelligent medical treatment

Technical Field

The application relates to the technical field of data processing, in particular to an intelligent nursing remote data processing method and system based on intelligent medical treatment.

Background

Intelligent care remote data processing refers to the process of collecting, processing, analyzing and applying remote patient care data by combining computer technology with remote communication technology through intelligent equipment and systems. The technology brings remarkable innovation and innovation for nursing service, and improves the quality and efficiency of the nursing service. Remote data processing involves the transmission and storage of large amounts of sensitive data, such as patient personal information, health data, and the like. Once compromised or improperly used, such data can pose serious privacy risks and security threats to the patient. In the prior art, the privacy disclosure risk is still higher, and danger is easily brought to a patient, so that the safety of intelligent nursing remote data can be further improved.

Disclosure of Invention

The invention aims to provide an intelligent nursing remote data processing method and system based on intelligent medical treatment, which are used for solving the problems in the background technology.

In a first aspect, the intelligent care remote data processing method based on intelligent medical treatment provided by the application adopts the following technical scheme:

Collecting all nursing data of a patient, analyzing the data quality of the nursing data, and screening all the nursing data according to the data quality to obtain basic nursing data;

acquiring information of a user viewing basic nursing data and marking the information as user information, judging whether the user is a medical staff according to the user information, judging that the viewing purpose of the user is diagnostic treatment if the user is the medical staff, and judging that the viewing purpose is non-diagnostic treatment if the user is not the medical staff;

Extracting user identity according to the user information, and selecting primary care data from the basic care data by combining the user identity and the view purpose;

acquiring a history reading record of nursing data of a user, extracting user preference according to the history reading record, and processing primary nursing data according to the user preference to obtain intermediate nursing data;

Acquiring the reading experience of a user, and performing language conversion on the medium-level nursing data according to the reading experience to obtain high-level nursing data;

And opening the viewing authority of the advanced nursing data for a user to view, and closing the viewing authority of the non-advanced nursing data.

Preferably, the step of collecting all nursing data of the patient, analyzing the data quality of the nursing data, and screening all the nursing data according to the data quality to obtain basic nursing data comprises the following specific steps:

collecting nursing data of a patient, recording the nursing data as real-time nursing data, and obtaining historical nursing data of the patient;

Comparing the real-time nursing data with the historical nursing data, searching the nursing data generating abnormality and marking the abnormal nursing data;

Extracting the number of influence factors influencing the abnormal nursing data, and counting the abnormal frequency of the abnormal nursing data;

combining the number of the influence factors and the abnormal frequency to obtain the fluctuation probability of the abnormal nursing data;

and obtaining the influence degree of the abnormal nursing data on the health judgment of the patient, and obtaining basic nursing data by combining with fluctuation probability screening.

Preferably, the step of obtaining the influence degree of the abnormal nursing data on the health judgment of the patient and combining with the fluctuation probability screening to obtain the basic nursing data specifically includes:

Acquiring health information of a patient, extracting basic illness state of the patient in the health information, and judging whether abnormal nursing data are related to the basic illness state of the patient;

If the abnormal nursing data is associated with the basic illness state of the patient, combining the health information to search for the illness probability corresponding to the abnormal nursing data;

Deleting the abnormal nursing data of which the illness probability does not reach a preset illness probability threshold value to obtain basic nursing data;

If the abnormal nursing data are not associated with the basic illness state of the patient, a fluctuation probability threshold value is set, and the abnormal data reaching the fluctuation probability threshold value are screened and removed to obtain the basic nursing data.

Preferably, the step of extracting the user identity according to the user information and selecting the primary care data from the basic care data in combination with the user identity and the view purpose specifically includes:

If the checking purpose is diagnosis and treatment, extracting a user identity according to the user information, and extracting a user function according to the user identity;

Extracting basic nursing data used by a user according to the function of the user to form primary nursing data;

if the checking purpose is non-medical diagnosis, extracting user identity according to the user information, and judging the intimacy of the user and the patient according to the user identity;

Setting the data security level of the basic nursing data, searching according to the affinity to obtain the corresponding data security level and marking the data security level as the user security level;

and screening according to the user safety level to obtain basic nursing data which is not more than the user safety level as primary nursing data.

Preferably, if the viewing purpose is non-medical diagnosis, extracting a user identity according to user information, and judging the affinity between the user and the patient according to the user identity, wherein the method specifically comprises the following steps:

Judging whether the user is a patient guardian, if not, judging whether the user and the patient have a blood relationship;

If the user has the blood relationship with the patient, obtaining the blood relationship affinity of the user and the patient according to the blood relationship spectrum;

Acquiring the contact frequency of a user and a patient and the watching frequency of the user watching the patient, respectively setting the weight ratio of the contact frequency and the watching frequency, and calculating to obtain the user attention according to the contact frequency, the watching frequency and the corresponding weight ratio;

overlapping the affinity of the blood relationship and the user concern to obtain the affinity of the user and the patient;

If the user has no blood relationship with the patient, the user interest degree is taken as the intimacy degree of the user and the patient;

if the user is not the patient guardian, the intimacy between the user and the patient is the highest intimacy value.

Preferably, the step of setting the data security level of the basic care data, searching according to the affinity to obtain the corresponding data security level and marking the data security level as the user security level specifically includes:

Judging the content of personal information of a patient contained in the basic nursing data and marking the content as the content of the patient information;

counting the frequency of the basic nursing data revealed when the patient communicates with other people and recording the frequency as the revealed frequency;

the sensitivity of the basic nursing data is obtained by combining the information content and the exposure frequency of the patient, and the data security level of the basic nursing data is set according to the sensitivity;

And searching and obtaining the corresponding data security level by utilizing the intimacy according to a preset intimacy-security level table, and marking the data security level as the user security level.

Preferably, the step of obtaining the history reading record of the nursing data of the user, extracting the user preference according to the history reading record, and obtaining the intermediate nursing data after processing the primary nursing data according to the user preference comprises the following specific steps:

distinguishing primary care data into normal data and abnormal data according to health information of a patient;

Respectively extracting average time length of reading normal data and abnormal data of a user according to the history reading record, and respectively recording the average time length as normal time length and abnormal time length;

calculating the time length ratio of the normal time length to the abnormal time length, and judging whether the user needs normal data or not according to the time length ratio;

if the user does not need normal data, extracting abnormal data as intermediate-grade nursing data;

if the user needs normal data, the time length of reading different types of normal data by the user is extracted and recorded as the type time length, and meanwhile, the primary care data is taken as the medium care data.

Preferably, the step of obtaining the experience of the user, and performing language conversion on the medium-level care data according to the experience of the user to obtain the high-level care data specifically includes:

judging whether normal data exist in the medium-level nursing data, and if the normal data do not exist, acquiring the reading experience of the user;

Judging whether the user understands the abnormal data according to the experience of reading, and if the user understands the abnormal data, acquiring the estimated reading time of the abnormal data;

Judging whether the predicted reading time is longer than the abnormal time, if so, simplifying the abnormal data to obtain advanced nursing data;

If the user does not understand the abnormal data, summarizing the abnormal data to obtain a plurality of conclusions, and selecting the conclusions understood by the user as advanced care data;

if the normal data exist, the high-grade nursing data are obtained by combining the abnormal data after the normal data are processed according to the type duration.

Preferably, if there is normal data, the step of obtaining advanced care data by combining abnormal data after processing the normal data according to type duration specifically includes:

If the normal data exist, judging whether the user understands the normal data according to the experience of reading, screening to obtain the normal data which the user does not understand, and recording the normal data as first data;

judging whether the user calls the first data, if the user does not call the first data, deleting the first data from the normal data to obtain the second data, otherwise, not deleting the first data to obtain the second data;

according to the type duration, searching to obtain the duration of different types of data in the second data and recording the duration as the second duration;

setting a second time length threshold value, and not processing normal data reaching the second time length threshold value;

Summarizing the normal data which does not reach the threshold value of the second time length to obtain a plurality of conclusions, and selecting the conclusions corresponding to the second time length as normal conclusions;

the processed normal data and abnormal data are combined as advanced care data.

In a second aspect, the intelligent care remote data processing system based on intelligent medical treatment provided by the application adopts the following technical scheme:

intelligent care remote data processing system based on wisdom medical treatment includes:

the basic nursing module is used for collecting all nursing data of a patient, analyzing the data quality of the nursing data, and screening all the nursing data according to the data quality to obtain basic nursing data;

the checking purpose module is used for acquiring information of a user checking the basic nursing data and recording the information as user information, judging whether the user is a medical person according to the user information, judging that the checking purpose of the user is diagnosis treatment if the user is the medical person, and judging that the checking purpose is non-diagnosis treatment if the user is not the medical person;

the primary nursing module extracts the user identity according to the user information, and combines the user identity with the view purpose to select primary nursing data from the basic nursing data;

The medium-level nursing module is used for acquiring a history reading record of nursing data of a user, extracting user preference according to the history reading record, and processing the primary nursing data according to the user preference to obtain medium-level nursing data;

the advanced nursing module is used for acquiring the reading experience of the user and carrying out language conversion on the intermediate nursing data according to the reading experience to obtain advanced nursing data;

And the view permission module opens the view permission of the advanced nursing data for a user to view and closes the view permission of the non-advanced nursing data.

In summary, the present application includes at least one of the following beneficial technical effects:

1. Nursing data of a patient are collected, and nursing data with poor quality are screened and removed according to the data quality of the nursing data, so that basic nursing data are obtained. And judging the view purpose of the user according to the user information, and screening according to the view purpose and the user identity to obtain primary care data. The primary nursing data are processed according to user preferences to obtain the intermediate nursing data, the intermediate nursing data are converted by using the experience of the user to obtain the advanced nursing data, and the authority of the advanced nursing data is opened for the user to check, so that the leakage risk of the nursing data is reduced, and the safety of intelligent nursing remote data processing based on intelligent medical treatment is improved.

2. When the user views the care data for the purpose of diagnosing the treatment, the primary care data is selected according to the necessity of the user's need for the care data. If the purpose of checking the nursing data by the user is not diagnosis and treatment, comprehensively obtaining the affinity between the user and the patient according to whether the user is a patient guardian, the blood relationship between the user and the patient and the interest degree of the user on the patient, and obtaining basic nursing data which accords with the safety level of the user according to the affinity degree to serve as primary nursing data. The intelligent nursing remote data processing method has the advantages that nursing data are stolen by irrelevant personnel, convenience in checking the nursing data by users such as relatives and friends of patients is met, and convenience in intelligent nursing remote data processing based on intelligent medical treatment is improved.

3. Judging whether the user understands the nursing data according to the reading experience of the user, and respectively deleting, simplifying, summarizing and the like the normal data and the abnormal data by combining the reading time of the user on the nursing data, so that the efficiency of the user for reading the nursing data is improved, meanwhile, the requirement of the user on the nursing data is met, the habit, experience and preference of the user are more fitted, and the intelligence of intelligent remote data processing of intelligent nursing based on intelligent medical treatment is improved.

Drawings

FIG. 1 is a schematic diagram showing the specific steps of an embodiment of a smart care remote data processing method based on smart medical treatment according to the present invention.

FIG. 2 is a schematic diagram of the modular connections of an embodiment of the smart care remote data processing system based on smart medical treatment of the present invention.

Detailed Description

The present invention will be described in further detail with reference to examples and fig. 1 to 2, but embodiments of the present invention are not limited thereto.

The invention discloses an intelligent nursing remote data processing method based on intelligent medical treatment, which specifically comprises the following steps:

step S1, collecting all nursing data of a patient, analyzing the data quality of the nursing data, and screening all the nursing data according to the data quality to obtain basic nursing data.

Step S2, acquiring information of a user viewing the basic nursing data, recording the information as user information, judging whether the user is a medical person according to the user information, judging that the viewing purpose of the user is diagnosis treatment if the user is the medical person, and otherwise, judging that the viewing purpose is non-diagnosis treatment.

And S3, extracting the user identity according to the user information, and selecting primary care data from the basic care data by combining the user identity and the view purpose.

And S4, acquiring a history reading record of the nursing data of the user, extracting user preferences according to the history reading record, and processing the primary nursing data according to the user preferences to obtain the intermediate nursing data.

And S5, acquiring the reading experience of the user, and performing language conversion on the medium-level nursing data according to the reading experience to obtain the high-level nursing data.

And S6, opening the viewing authority of the advanced nursing data for the user to view, and closing the viewing authority of the non-advanced nursing data.

And simultaneously opening the authority of the corresponding medium-level nursing data before conversion for the converted high-level nursing data.

In practical use, the nursing data refers to various information related to the health condition, nursing measures and nursing effect of the patient, which are collected in the nursing process. These data are of great significance for assessing the health of a patient, planning care, and evaluating the effectiveness of care. In general, in remote data processing, care data is often used by medical staff for diagnosis, adjustment of care plan, and the like. In fact, family members, friends and the like of the patient also need to view nursing data, and under the condition that the family members of the patient view the nursing data, only certain family members can view all data, but other people cannot view the data, and only data transmission can be performed through the family members, so that the safety of the patient data is low, and the privacy of the patient is easy to reveal. Therefore, in intelligent nursing remote data processing, data with different authorities are opened according to user information of the checked data, and meanwhile, the data are processed, so that the user can understand information in nursing data more conveniently and quickly, and the safety of nursing remote data processing is improved.

Collecting all nursing data of a patient, analyzing the data quality of the nursing data, screening all the nursing data according to the data quality, and obtaining basic nursing data, wherein the method specifically comprises the following steps of:

Step S11, nursing data of the patient are collected and recorded as real-time nursing data, and historical nursing data of the patient are obtained.

And step S12, comparing the real-time nursing data with the historical nursing data, searching the abnormal nursing data and recording the abnormal nursing data as abnormal nursing data.

And S13, extracting the number of influence factors influencing the abnormal nursing data, and counting the abnormal frequency of the abnormal nursing data.

And S14, combining the number of the influence factors and the abnormal frequency to obtain the fluctuation probability of the abnormal nursing data.

And respectively setting the number of the influence factors and the weight ratio of the abnormal frequency, and calculating to obtain the fluctuation probability according to the number of the influence factors, the abnormal frequency and the corresponding weight ratio.

And S15, acquiring the influence degree of the abnormal nursing data on the health judgment of the patient, and screening by combining the fluctuation probability to obtain basic nursing data.

In practical application, in the process of collecting patient nursing data, some nursing data with low quality can not play any role on the health of a patient, and can interfere with the judgment of doctors so as to bring negative effects to the patient. Therefore, some nursing data with low quality needs to be screened and removed, normal records are needed for normal data, and whether acquisition records are necessary for abnormal data or not needs to be analyzed according to the situation of the abnormal data. Some data are very high in abnormal frequency and are easily influenced by different factors, so that the data are sometimes not abnormal in health of a patient, but are only abnormal due to influence, and a doctor is easily disturbed to make inaccurate judgment on the patient. For example, the patient measures the heart beat frequency after the movement, the heart beat frequency is abnormal, and the heart beat frequency data is obviously due to the fluctuation caused by the movement. And the heartbeat frequency is influenced by various factors such as exercise, emotion and the like, and is easy to generate fluctuation, so that the heartbeat frequency after running has no effective effect and can be deleted.

The method comprises the steps of obtaining the influence degree of abnormal nursing data on health judgment of a patient and obtaining basic nursing data by combining fluctuation probability screening, and specifically comprises the following steps:

step S151, health information of the patient is obtained, basic illness state of the patient in the health information is extracted, and whether abnormal nursing data are related to the basic illness state of the patient is judged.

Step S152, if the abnormal nursing data is associated with the basic illness state of the patient, the illness probability corresponding to the abnormal nursing data is obtained by combining the health information.

In the case of different patients with the same care data, the probability of illness is also different because of the difference in health information. For example, a patient has helicobacter pylori, but older patients have a greater probability of having gastric cancer. And searching and obtaining the corresponding disease probability under the condition according to medical patient history, age and other information.

And step S153, deleting the abnormal nursing data of which the illness probability does not reach a preset illness probability threshold value to obtain basic nursing data.

And step S154, if the abnormal nursing data is not related to the basic illness state of the patient, setting a fluctuation probability threshold, screening and removing the abnormal data reaching the fluctuation probability threshold, and obtaining the basic nursing data.

If the abnormal nursing data is not related to the basic condition of the patient, judging according to the fluctuation probability of the abnormal nursing data. The greater the number of influencing factors and the greater the frequency of abnormality, the more changeable the abnormal nursing data is in the daily acquisition process. Thus, the variable and patient condition-independent abnormal care data does not have a significant effect on patient health diagnosis and can be deleted. The fluctuation probability threshold is set according to the history data, for example, the heartbeat frequency is affected by many factors, and abnormality is easy to generate, so that the fluctuation probability is 70%. If the patient's previous heart beat frequency is normal, the fluctuation probability threshold may be set to 80% at which time the heart beat frequency may be deleted, as the first occurrence may be affected by other factors. If the patient has previously had several anomalies in the heart beat frequency, the fluctuation probability threshold is adjusted down to 50% at which point the anomaly care data is needed. And meanwhile, the first few heartbeat frequencies are searched in the deletion storage database, and the basic nursing data are added.

In practical applications, it is first required whether abnormal data is related to the health condition of a patient, for example, if the patient has heart disease, the heart beat frequency has a close relationship with the diagnosis result of the patient. If the heart rate of the patient is judged to be possibly heart failure according to the heart rate, the probability of heart failure of the patient is judged according to other information of the patient, such as age, physique, sex and the like, and if the probability does not reach the preset disease probability, the heart rate is considered to be caused by short-term influence of other factors, so that the heart rate can be deleted, the diagnosis interference to doctors is reduced, and the workload of the doctors is reduced. The preset probability of illness can be set at will, and when the patient is cautious, the probability of illness can be set to 0, so that the data can not be deleted as long as the probability of illness exists.

Extracting user identity according to user information, and selecting primary care data from basic care data by combining the user identity and a view purpose, wherein the primary care data comprises the following steps:

step S31, if the checking purpose is diagnosis and treatment, the user identity is extracted according to the user information, and the user function is extracted according to the user identity.

Step S32, basic nursing data used by the user are extracted according to the functions of the user, and primary nursing data are formed.

Step S33, if the checking purpose is non-medical diagnosis, the user identity is extracted according to the user information, and the affinity between the user and the patient is judged according to the user identity.

Step S34, setting the data security level of the basic nursing data, searching according to the affinity to obtain the corresponding data security level and marking the data security level as the user security level.

Step S35, screening and obtaining basic nursing data which is not more than the user safety level according to the user safety level as primary nursing data.

In practical use, if care data is to be checked for diagnosis and treatment, user intelligence is required. For example, nurses and doctors are different in function, so that nurses do not need to know all information of patients, but only data corresponding to the functions. And the doctor can diagnose the illness state of the patient, and the nursing data required by the doctor can be obtained. For example, the thickness of the blood vessel is not necessary for a doctor to diagnose the condition, and the doctor is not required to be given the blood vessel. For nurses, this data is needed during the patient insertion process and is then placed into the primary care data for nurses. For users who view non-diagnostic treatments, it is necessary to confirm the basic care data that the patient can view according to the relationship between the user and the patient. For example, if the user is not in close relationship with the patient, the core confidential data of the patient cannot be viewed. Assuming that strangers view the patient's care data, it is obviously impossible to view it, but friends of the patient view it, a portion of the data may be viewed according to intimacy. In the prior art, patient friends can only view nursing data through patients, which is troublesome.

If the checking purpose is non-medical diagnosis, extracting user identity according to the user information, and judging the affinity between the user and the patient according to the user identity, wherein the method specifically comprises the following steps:

Step S331, judging whether the user is a patient guardian, if not, judging whether the user and the patient have a blood relationship.

In step S332, if the user has a blood relationship with the patient, the blood relationship affinity between the user and the patient is obtained according to the blood relationship spectrum.

Step S333, obtaining the contact frequency between the user and the patient and the watching frequency of the user watching the patient, respectively setting the weight ratio of the contact frequency and the watching frequency, and calculating the user attention according to the contact frequency, the watching frequency and the corresponding weight ratio.

Step S334, the affinity of the blood relationship and the user interest are overlapped to obtain the affinity of the user and the patient.

In step S335, if the user has no relationship with the patient, the user interest is taken as the affinity between the user and the patient.

In step S336, if the user is not the patient 'S guardian, the user' S intimacy with the patient is the highest value.

In practice, if the user is the patient's guardian, the user can view the patient's care data as prescribed, so that the affinity is the highest value. If the user is not the guardian of the patient, judging whether the user and the patient have the blood relationship, and if so, obtaining the blood relationship affinity. From a biological perspective, the relatedness of blood relationships, such as the distinction of direct and collateral relatives, can be distinguished from algebraic and also from the relationship of blood relationships. And when the user has no blood relationship with the patient, the affinity is obtained according to the degree of interest of the user on the patient. When the user is a patient friend, the user has a good relationship with the patient, and then part of nursing data can be also checked, so that the user is beneficial to caring the patient, and the user also plays a role in supervising the nursing scheme.

Setting the data security level of basic nursing data, searching according to the affinity to obtain the corresponding data security level and marking the data security level as the user security level, wherein the method specifically comprises the following steps:

in step S341, the content of the patient personal information contained in the basic care data is determined and recorded as the content of the patient information.

In step S342, the frequency of the basic care data revealed when the patient communicates with other people is counted and recorded as the revealed frequency.

In step S343, the sensitivity of the basic care data is obtained by combining the patient information content and the exposure frequency, and the data security level of the basic care data is set according to the sensitivity.

The weight ratio of the patient information content and the exposure frequency is respectively set, and the sensitivity is calculated according to the patient information content, the exposure frequency and the corresponding weight ratio, wherein the lower the exposure frequency is, the higher the sensitivity is.

In step S344, according to the preset affinity-security level table, the corresponding data security level is obtained by using the affinity lookup and is recorded as the user security level.

The higher the affinity of the user to the patient, the higher the user security level.

In practice, care data typically includes highly sensitive information such as personal physical information, health status, diagnosis results, treatment plan, etc. of a patient. Once revealed, this information may have a serious influence on the physical and mental health of the patient. Therefore, the safety level of different nursing data is also different, and the more the personal information content contained in the nursing data is, the more easily the personal safety is caused for the patient when the personal information is leaked. Meanwhile, although some data contains little personal information, the patient is not willing to disclose to others for the reasons of self-esteem protection and the like, and if leakage can affect the psychological health of the patient, the safety level should also be set higher. And obtaining data sensitivity according to the information content of the patient and the exposure rate of the nursing data, setting the data security level according to the data sensitivity, wherein the higher the sensitivity is, the higher the security level is, and the user can only view the nursing data within the allowable range of the security level.

Acquiring a history reading record of nursing data of a user, extracting user preference according to the history reading record, and processing primary nursing data according to the user preference to obtain intermediate nursing data, wherein the method specifically comprises the following steps of:

step S41, the primary care data is divided into normal data and abnormal data according to the health information of the patient.

According to the actual condition of the patient, whether the nursing data is abnormal or not is distinguished, for example, the patient suffers from hypertension, so that the blood pressure value is higher, the blood pressure value is higher for the patient and belongs to normal data, and the blood pressure value is lower and belongs to abnormal data.

Step S42, average time lengths of normal data and abnormal data read by the user are respectively extracted according to the history reading records and are respectively recorded as normal time lengths and abnormal time lengths.

Step S43, calculating the time length ratio of the normal time length to the abnormal time length, and judging whether the user needs normal data or not according to the time length ratio.

Setting a time length ratio threshold value, wherein the time length ratio is larger than the time length ratio threshold value, and normal data are needed, or else, normal data are not needed.

In step S44, if the user does not need normal data, abnormal data is extracted as intermediate care data.

In step S45, if the user needs normal data, the time length for reading different types of normal data by the user is extracted and recorded as the type time length, and meanwhile, the primary care data is used as the intermediate care data.

In practical application, nursing data of a patient are quite large, the requirements of different users for reading the nursing data are different, and some users can neglect to view normal data due to time tension and other reasons, and because the data normally indicate that the health of the patient is not too big, the users only pay attention to abnormal data. Some users are more happy, and normal data can be checked. If the user has low requirements on normal data, the normal data can be deleted, so that the reading time of the user is reduced, and the efficiency of the user for checking the nursing data is improved. If the user checks the normal data, the normal data is also provided for the user, so that the user requirement is met.

Obtaining the reading experience of a user, and performing language conversion on the medium-level nursing data according to the reading experience to obtain the high-level nursing data, wherein the method specifically comprises the following steps of:

Step S51, judging whether normal data exist in the medium-level nursing data, and if the normal data do not exist, acquiring the reading experience of the user.

Step S52, judging whether the user understands the abnormal data according to the experience of reading, and if the user understands the abnormal data, acquiring the expected reading time of the abnormal data.

Step S53, judging whether the expected reading time is longer than the abnormal time, if so, simplifying the abnormal data to obtain the advanced nursing data.

And step S54, if the user does not understand the abnormal data, summarizing the abnormal data to obtain a plurality of conclusions, and selecting the conclusions understood by the user as the advanced care data.

And step S55, if the normal data exist, processing the normal data according to the type duration, and then combining the abnormal data to obtain the advanced care data.

In practical applications, nursing data often involves relatively specialized medical data, and not all data can be understood by a user. The user's experience in reading includes, but is not limited to, academic, work experience, engaging in industry, daily hobbies, etc., and it is determined whether the user can understand the care data based on the user's experience in reading. If the user touches the data, the user is considered to be understandable. For abnormal data, the user needs to be presented to the user so that the user can know the problems in the nursing process, and the user can simplify the data according to the time length of the user if the user understands the data. And by means of synonym replacement and the like, the reading time required by nursing data is shortened, and the reading requirement of a user is met. And if the user does not understand, summarizing, and selecting the user understand conclusion to present. For example, the nursing data is "the lesser curvature side of the stomach sees a circular ulcer, the diameter is about 1.0cm, the surface is covered with yellow and white fur, the surrounding mucous membrane is engorged with blood and edema, the edge is the gastric ulcer A1 phase", wherein the patient in the gastric ulcer A1 phase is not understood, and the nursing data can be summarized as "the early stage of the active phase of the gastric ulcer, the ulcer range is not large, and the illness state is not serious".

If normal data exist, processing the normal data according to type duration, and then combining abnormal data to obtain advanced nursing data, wherein the method specifically comprises the following steps:

In step S551, if there is normal data, whether the user understands the normal data is determined according to the experience of reading, and the normal data that the user does not understand is obtained by screening and is recorded as the first data.

Step S552, judging whether the user calls the first data, if the user does not call the first data, deleting the first data from the normal data to obtain the second data, otherwise, not deleting the second data.

Step S553, according to the type duration, searching to obtain the duration of different types of data in the second data and recording the duration as the second duration.

In step S554, a second duration threshold is set, and normal data up to the second duration threshold is not processed.

And step 555, summarizing the normal data which does not reach the threshold value of the second duration, obtaining a plurality of conclusions, and selecting the conclusions corresponding to the second duration as the normal conclusions.

Step S556, combining the processed normal data and abnormal data as advanced care data.

In practical use, the processing of normal data is different from the processing of abnormal data, and the normal data reflects the health of the patient because the abnormal data reflects the problems of the patient. Therefore, if the user does not understand the normal data, the side surface states that the user does not pay attention to the data of the patient in daily life, and the data can be directly deleted, so that the efficiency of reading the nursing data of the patient is improved. And for the normal data exceeding the reading time threshold, the method directly summarizes, because no important information is obtained from the normal data, a conclusion is selected to improve the reading efficiency of the user.

The intelligent care remote data processing system based on intelligent medical treatment comprises the following steps of:

And the basic nursing module is used for collecting all nursing data of the patient, analyzing the data quality of the nursing data, and screening all the nursing data according to the data quality to obtain basic nursing data.

The checking purpose module is used for acquiring information of a user checking the basic nursing data and recording the information as user information, judging whether the user is a medical person according to the user information, judging that the checking purpose of the user is diagnosis treatment if the user is the medical person, and judging that the checking purpose is non-diagnosis treatment if the user is not the medical person.

And the primary nursing module extracts the user identity according to the user information, and combines the user identity and the view purpose to select primary nursing data from the basic nursing data.

And the medium-grade nursing module is used for acquiring a history reading record of nursing data of a user, extracting user preference according to the history reading record, and processing the primary nursing data according to the user preference to obtain medium-grade nursing data.

And the advanced nursing module is used for acquiring the reading experience of the user and carrying out language conversion on the intermediate nursing data according to the reading experience to obtain the advanced nursing data.

And the view permission module opens the view permission of the advanced nursing data for a user to view and closes the view permission of the non-advanced nursing data.

In practical application, in the process of intelligent nursing remote data processing, firstly data are acquired, and basic nursing data are obtained by screening the acquired data according to whether nursing data are related to illness state of a patient, illness probability of the patient and the like. And judging the view purpose of the user for viewing the nursing data according to the user information, and screening according to the view purpose and the identity of the patient to obtain primary nursing data meeting the user requirement. And deleting the data by using user preference to obtain medium-grade nursing data, and converting the medium-grade nursing data into high-grade nursing data which is more suitable for the user by combining the experience of the user. Only the authority of the advanced nursing data is opened for the user to check, so that the leakage risk of the nursing data is reduced, and the safety of intelligent nursing remote data processing is improved.

The above embodiments are not intended to limit the scope of the application, so that the equivalent changes of the structure, shape and principle of the application are covered by the scope of the application.

Claims (10)

1. The intelligent nursing remote data processing method based on intelligent medical treatment is characterized by comprising the following steps of:

Collecting all nursing data of a patient, analyzing the data quality of the nursing data, and screening all the nursing data according to the data quality to obtain basic nursing data;

acquiring information of a user viewing basic nursing data and marking the information as user information, judging whether the user is a medical staff according to the user information, judging that the viewing purpose of the user is diagnostic treatment if the user is the medical staff, and judging that the viewing purpose is non-diagnostic treatment if the user is not the medical staff;

Extracting user identity according to the user information, and selecting primary care data from the basic care data by combining the user identity and the view purpose;

acquiring a history reading record of nursing data of a user, extracting user preference according to the history reading record, and processing primary nursing data according to the user preference to obtain intermediate nursing data;

Acquiring the reading experience of a user, and performing language conversion on the medium-level nursing data according to the reading experience to obtain high-level nursing data;

And opening the viewing authority of the advanced nursing data for a user to view, and closing the viewing authority of the non-advanced nursing data.

2. The intelligent care remote data processing method based on intelligent medical treatment according to claim 1, wherein the steps of collecting all care data of a patient, analyzing data quality of the care data, screening all care data according to the data quality, and obtaining basic care data are specifically as follows:

collecting nursing data of a patient, recording the nursing data as real-time nursing data, and obtaining historical nursing data of the patient;

Comparing the real-time nursing data with the historical nursing data, searching the nursing data generating abnormality and marking the abnormal nursing data;

Extracting the number of influence factors influencing the abnormal nursing data, and counting the abnormal frequency of the abnormal nursing data;

combining the number of the influence factors and the abnormal frequency to obtain the fluctuation probability of the abnormal nursing data;

and obtaining the influence degree of the abnormal nursing data on the health judgment of the patient, and obtaining basic nursing data by combining with fluctuation probability screening.

3. The intelligent care remote data processing method based on intelligent medical treatment according to claim 2, wherein the step of obtaining the basic care data by combining the influence degree of the abnormal care data on the health judgment of the patient and the fluctuation probability screening is specifically as follows:

Acquiring health information of a patient, extracting basic illness state of the patient in the health information, and judging whether abnormal nursing data are related to the basic illness state of the patient;

If the abnormal nursing data is associated with the basic illness state of the patient, combining the health information to search for the illness probability corresponding to the abnormal nursing data;

Deleting the abnormal nursing data of which the illness probability does not reach a preset illness probability threshold value to obtain basic nursing data;

If the abnormal nursing data are not associated with the basic illness state of the patient, a fluctuation probability threshold value is set, and the abnormal data reaching the fluctuation probability threshold value are screened and removed to obtain the basic nursing data.

4. The intelligent care remote data processing method based on intelligent medical treatment according to claim 3, wherein the step of extracting the user identity according to the user information and selecting primary care data from the basic care data in combination with the user identity and the view purpose comprises the following steps:

If the checking purpose is diagnosis and treatment, extracting a user identity according to the user information, and extracting a user function according to the user identity;

Extracting basic nursing data used by a user according to the function of the user to form primary nursing data;

if the checking purpose is non-medical diagnosis, extracting user identity according to the user information, and judging the intimacy of the user and the patient according to the user identity;

Setting the data security level of the basic nursing data, searching according to the affinity to obtain the corresponding data security level and marking the data security level as the user security level;

and screening according to the user safety level to obtain basic nursing data which is not more than the user safety level as primary nursing data.

5. The intelligent care remote data processing method based on intelligent medical treatment according to claim 4, wherein if the view purpose is non-medical diagnosis, the step of extracting the user identity according to the user information and judging the affinity between the user and the patient according to the user identity is specifically as follows:

Judging whether the user is a patient guardian, if not, judging whether the user and the patient have a blood relationship;

If the user has the blood relationship with the patient, obtaining the blood relationship affinity of the user and the patient according to the blood relationship spectrum;

Acquiring the contact frequency of a user and a patient and the watching frequency of the user watching the patient, respectively setting the weight ratio of the contact frequency and the watching frequency, and calculating to obtain the user attention according to the contact frequency, the watching frequency and the corresponding weight ratio;

overlapping the affinity of the blood relationship and the user concern to obtain the affinity of the user and the patient;

If the user has no blood relationship with the patient, the user interest degree is taken as the intimacy degree of the user and the patient;

if the user is not the patient guardian, the intimacy between the user and the patient is the highest intimacy value.

6. The intelligent care remote data processing method based on intelligent medical treatment according to claim 5, wherein the step of setting the data security level of the basic care data, obtaining the corresponding data security level according to the affinity lookup and marking the data security level as the user security level comprises the following steps:

Judging the content of personal information of a patient contained in the basic nursing data and marking the content as the content of the patient information;

counting the frequency of the basic nursing data revealed when the patient communicates with other people and recording the frequency as the revealed frequency;

the sensitivity of the basic nursing data is obtained by combining the information content and the exposure frequency of the patient, and the data security level of the basic nursing data is set according to the sensitivity;

And searching and obtaining the corresponding data security level by utilizing the intimacy according to a preset intimacy-security level table, and marking the data security level as the user security level.

7. The intelligent care remote data processing method based on intelligent medical treatment according to claim 6, wherein the step of obtaining the history reading record of the care data of the user, extracting the user preference according to the history reading record, and processing the primary care data according to the user preference to obtain the intermediate care data comprises the following steps:

distinguishing primary care data into normal data and abnormal data according to health information of a patient;

Respectively extracting average time length of reading normal data and abnormal data of a user according to the history reading record, and respectively recording the average time length as normal time length and abnormal time length;

calculating the time length ratio of the normal time length to the abnormal time length, and judging whether the user needs normal data or not according to the time length ratio;

if the user does not need normal data, extracting abnormal data as intermediate-grade nursing data;

if the user needs normal data, the time length of reading different types of normal data by the user is extracted and recorded as the type time length, and meanwhile, the primary care data is taken as the medium care data.

8. The intelligent care remote data processing method based on intelligent medical treatment according to claim 7, wherein the step of obtaining the experience of the user and performing language conversion on the medium-level care data according to the experience of the user to obtain the high-level care data comprises the following steps:

judging whether normal data exist in the medium-level nursing data, and if the normal data do not exist, acquiring the reading experience of the user;

Judging whether the user understands the abnormal data according to the experience of reading, and if the user understands the abnormal data, acquiring the estimated reading time of the abnormal data;

Judging whether the predicted reading time is longer than the abnormal time, if so, simplifying the abnormal data to obtain advanced nursing data;

If the user does not understand the abnormal data, summarizing the abnormal data to obtain a plurality of conclusions, and selecting the conclusions understood by the user as advanced care data;

if the normal data exist, the high-grade nursing data are obtained by combining the abnormal data after the normal data are processed according to the type duration.

9. The intelligent care remote data processing method based on intelligent medical treatment according to claim 8, wherein if there is normal data, the step of obtaining advanced care data by combining abnormal data after processing the normal data according to type duration is specifically as follows:

If the normal data exist, judging whether the user understands the normal data according to the experience of reading, screening to obtain the normal data which the user does not understand, and recording the normal data as first data;

judging whether the user calls the first data, if the user does not call the first data, deleting the first data from the normal data to obtain the second data, otherwise, not deleting the first data to obtain the second data;

according to the type duration, searching to obtain the duration of different types of data in the second data and recording the duration as the second duration;

setting a second time length threshold value, and not processing normal data reaching the second time length threshold value;

Summarizing the normal data which does not reach the threshold value of the second time length to obtain a plurality of conclusions, and selecting the conclusions corresponding to the second time length as normal conclusions;

the processed normal data and abnormal data are combined as advanced care data.

10. Smart care telemedicine-based smart care telemedicine data processing system, characterized by comprising, by applying the smart care telemedicine-based smart care telemedicine data processing method as claimed in any one of claims 1 to 9:

the basic nursing module is used for collecting all nursing data of a patient, analyzing the data quality of the nursing data, and screening all the nursing data according to the data quality to obtain basic nursing data;

the checking purpose module is used for acquiring information of a user checking the basic nursing data and recording the information as user information, judging whether the user is a medical person according to the user information, judging that the checking purpose of the user is diagnosis treatment if the user is the medical person, and judging that the checking purpose is non-diagnosis treatment if the user is not the medical person;

the primary nursing module extracts the user identity according to the user information, and combines the user identity with the view purpose to select primary nursing data from the basic nursing data;

The medium-level nursing module is used for acquiring a history reading record of nursing data of a user, extracting user preference according to the history reading record, and processing the primary nursing data according to the user preference to obtain medium-level nursing data;

the advanced nursing module is used for acquiring the reading experience of the user and carrying out language conversion on the intermediate nursing data according to the reading experience to obtain advanced nursing data;

And the view permission module opens the view permission of the advanced nursing data for a user to view and closes the view permission of the non-advanced nursing data.