AIMC Topic: Electronic Health Records
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Developing deep learning-based strategies to predict the risk of hepatocellular carcinoma among patients with nonalcoholic fatty liver disease from electronic health records.
Journal of biomedical informatics
Mar 22, 2024
OBJECTIVE: The accuracy of deep learning models for many disease prediction problems is affected by time-varying covariates, rare incidence, covariate imbalance and delayed diagnosis when using structured electronic health records data. The situation...
De-identification of clinical free text using natural language processing: A systematic review of current approaches.
Artificial intelligence in medicine
Mar 20, 2024
BACKGROUND: Electronic health records (EHRs) are a valuable resource for data-driven medical research. However, the presence of protected health information (PHI) makes EHRs unsuitable to be shared for research purposes. De-identification, i.e. the p...
Practical use case of natural language processing for observational clinical research data retrieval from electronic health records: AssistMED project.
Polish archives of internal medicine
Mar 19, 2024
INTRODUCTION: Electronic health records (EHRs) contain data valuable for clinical research. However, they are in textual format and require manual encoding to databases, which is a lengthy and costly process. Natural language processing (NLP) is a co...
A matching-based machine learning approach to estimating optimal dynamic treatment regimes with time-to-event outcomes.
Statistical methods in medical research
Mar 19, 2024
Observational data (e.g. electronic health records) has become increasingly important in evidence-based research on dynamic treatment regimes, which tailor treatments over time to patients based on their characteristics and evolving clinical history....
Development of a Machine Learning Model to Predict the Use of Surgery in Patients With Rheumatoid Arthritis.
Arthritis care & research
Mar 19, 2024
OBJECTIVE: One in five patients with rheumatoid arthritis (RA) rely on surgery to restore joint function. However, variable response to disease-modifying antirheumatic drugs (DMARDs) complicates surgical planning, and it is difficult to predict which...
Early identification of suspected serious infection among patients afebrile at initial presentation using neural network models and natural language processing: A development and external validation study in the emergency department.
The American journal of emergency medicine
Mar 16, 2024
OBJECTIVE: To develop and externally validate models based on neural networks and natural language processing (NLP) to identify suspected serious infections in emergency department (ED) patients afebrile at initial presentation.
Improving the performance of machine learning penicillin adverse drug reaction classification with synthetic data and transfer learning.
Internal medicine journal
Mar 14, 2024
BACKGROUND: Machine learning may assist with the identification of potentially inappropriate penicillin allergy labels. Strategies to improve the performance of existing models for this task include the use of additional training data, synthetic data...
Continuing Medical Education Questions: August 2024.
The American journal of gastroenterology
Mar 14, 2024
Article Title: Machine Learning Models for Pancreatic Cancer Risk Prediction Using Electronic Health Record Data-A Systematic Review and Assessment.
Harnessing the Power of Generative AI for Clinical Summaries: Perspectives From Emergency Physicians.
Annals of emergency medicine
Mar 12, 2024
STUDY OBJECTIVE: The workload of clinical documentation contributes to health care costs and professional burnout. The advent of generative artificial intelligence language models presents a promising solution. The perspective of clinicians may contr...
Computational Approaches for Predicting Preterm Birth and Newborn Outcomes.
Clinics in perinatology
Mar 8, 2024
Preterm birth (PTB) and its associated morbidities are a leading cause of infant mortality and morbidity. Accurate predictive models and a better biological understanding of PTB-associated morbidities are critical in reducing their adverse effects. I...
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