AIMC Topic: Electronic Health Records
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Personalised prediction of maintenance dialysis initiation in patients with chronic kidney disease stages 3-5: a multicentre study using the machine learning approach.
BMJ health & care informatics
Apr 27, 2024
BACKGROUND: Optimal timing for initiating maintenance dialysis in patients with chronic kidney disease (CKD) stages 3-5 is challenging. This study aimed to develop and validate a machine learning (ML) model for early personalised prediction of mainte...
Predictive model and risk analysis for coronary heart disease in people living with HIV using machine learning.
BMC medical informatics and decision making
Apr 25, 2024
OBJECTIVE: This study aimed to construct a coronary heart disease (CHD) risk-prediction model in people living with human immunodeficiency virus (PLHIV) with the help of machine learning (ML) per electronic medical records (EMRs).
Machine learning algorithms to predict colistin-induced nephrotoxicity from electronic health records in patients with multidrug-resistant Gram-negative infection.
International journal of antimicrobial agents
Apr 19, 2024
OBJECTIVES: Colistin-induced nephrotoxicity prolongs hospitalisation and increases mortality. The study aimed to construct machine learning models to predict colistin-induced nephrotoxicity in patients with multidrug-resistant Gram-negative infection...
Causal machine learning for predicting treatment outcomes.
Nature medicine
Apr 19, 2024
Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs. A key benefit of causal ML is that it allows for estimating in...
Quality of Answers of Generative Large Language Models Versus Peer Users for Interpreting Laboratory Test Results for Lay Patients: Evaluation Study.
Journal of medical Internet research
Apr 17, 2024
BACKGROUND: Although patients have easy access to their electronic health records and laboratory test result data through patient portals, laboratory test results are often confusing and hard to understand. Many patients turn to web-based forums or q...
Natural language processing (NLP) to facilitate abstract review in medical research: the application of BioBERT to exploring the 20-year use of NLP in medical research.
Systematic reviews
Apr 15, 2024
BACKGROUND: Abstract review is a time and labor-consuming step in the systematic and scoping literature review in medicine. Text mining methods, typically natural language processing (NLP), may efficiently replace manual abstract screening. This stud...
Identifying social determinants of health from clinical narratives: A study of performance, documentation ratio, and potential bias.
Journal of biomedical informatics
Apr 14, 2024
OBJECTIVE: To develop a natural language processing (NLP) package to extract social determinants of health (SDoH) from clinical narratives, examine the bias among race and gender groups, test the generalizability of extracting SDoH for different dise...
Validation of a novel Artificial Pharmacology Intelligence (API) system for the management of patients with polypharmacy.
Research in social & administrative pharmacy : RSAP
Apr 10, 2024
OBJECTIVE: Medication management of patients with polypharmacy is highly complex. We aimed to validate a novel Artificial Pharmacology Intelligence (API) algorithm to optimize the medication review process in a comprehensive, personalized, and scalab...
Multistep validation of a post-ERCP pancreatitis prediction system integrating multimodal data: a multicenter study.
Gastrointestinal endoscopy
Apr 5, 2024
BACKGROUND AND AIMS: The impact of various categories of information on the prediction of post-ERCP pancreatitis (PEP) remains uncertain. We comprehensively investigated the risk factors associated with PEP by constructing and validating a model inco...
Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review.
Thrombosis and haemostasis
Apr 4, 2024
BACKGROUND:  Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific clinical prediction models (CPMs) have been used to assist physicians in decision-making but have several limitations....
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