[go: up one dir, main page]

Shah et al., 2025 - Google Patents

Methods for phenotyping adult patients with acute kidney injury: a systematic review

Shah et al., 2025

View PDF
Document ID
7023901474235511747
Author
Shah A
Snead W
Daga A
Uddin R
Adiyeke E
Loftus T
Bihorac A
Ren Y
Ozrazgat-Baslanti T
Publication year
Publication venue
Journal of Nephrology

External Links

Snippet

Background Acute kidney injury (AKI) is a multifaceted disease characterized by diverse clinical presentations and mechanisms. Advances in artificial intelligence have propelled the identification of AKI subphenotypes, enhancing our capacity to customize treatments and …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/345Medical expert systems, neural networks or other automated diagnosis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/32Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • G06F19/322Management of patient personal data, e.g. patient records, conversion of records or privacy aspects
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/10Services
    • G06Q50/22Health care, e.g. hospitals; Social work
    • G06Q50/24Patient record management
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/36Computer-assisted acquisition of medical data, e.g. computerised clinical trials or questionnaires
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/28Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for programming tools or database systems, e.g. ontologies, heterogeneous data integration, data warehousing or computing architectures
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/48Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/80Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving blood groups or blood types or red blood cells
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/10Office automation, e.g. computer aided management of electronic mail or groupware; Time management, e.g. calendars, reminders, meetings or time accounting
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F15/00Digital computers in general; Data processing equipment in general
    • G06F15/18Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00Detection or diagnosis of diseases

Similar Documents

Publication Publication Date Title
Yuan et al. The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit
Dagliati et al. Machine learning methods to predict diabetes complications
US6266645B1 (en) Risk adjustment tools for analyzing patient electronic discharge records
Perlstein et al. Red blood cell distribution width and mortality risk in a community-based prospective cohort
Sun et al. Early prediction of acute kidney injury in critical care setting using clinical notes and structured multivariate physiological measurements
Ceriotti et al. Reference intervals: the way forward
Zhao et al. Detecting time-evolving phenotypic topics via tensor factorization on electronic health records: Cardiovascular disease case study
Kraus et al. Big data and precision medicine: challenges and strategies with healthcare data
Abraham et al. Machine learning prediction of kidney stone composition using electronic health record-derived features
Wang et al. Construction of machine learning diagnostic models for cardiovascular pan-disease based on blood routine and biochemical detection data
Wang et al. Veterans Affairs patient database (VAPD 2014–2017): building nationwide granular data for clinical discovery
Cai et al. Predicting acute kidney injury risk in acute myocardial infarction patients: an artificial intelligence model using medical information mart for intensive care databases
Zhou et al. Interpretable machine learning model for early prediction of 28-day mortality in ICU patients with sepsis-induced coagulopathy: development and validation
Varghese et al. Diagnostic utility of serial microscopic examination of the urinary sediment in acute kidney injury
Shah et al. Methods for phenotyping adult patients with acute kidney injury: a systematic review
Al-Ani et al. Artificial intelligence guidance of advanced heart failure therapies: A systematic scoping review
Xu et al. Machine learning-based derivation and external validation of a tool to predict death and development of organ failure in hospitalized patients with COVID-19
Miller et al. Risk stratification: a practical guide for clinicians
de Kok et al. Deep embedded clustering generalisability and adaptation for integrating mixed datatypes: two critical care cohorts
Nagy et al. Predicting pediatric cardiac surgery-associated acute kidney injury using machine learning
Brar et al. Processes of care after hospital discharge for survivors of acute kidney injury: a population-based cohort study
Li et al. Development and validation of outcome prediction models for acute kidney injury patients undergoing continuous renal replacement therapy
Ozdemir et al. Interpretation of acid–base metabolism on arterial blood gas samples via machine learning algorithms
Sauer et al. Qualification of translational safety biomarkers
Wu et al. Temporal dynamics of clinical risk predictors for hospital-acquired acute kidney injury under different forecast time windows