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Electronic Health Records

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A matching-based machine learning approach to estimating optimal dynamic treatment regimes with time-to-event outcomes.

Statistical methods in medical research
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
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...

Improving the performance of machine learning penicillin adverse drug reaction classification with synthetic data and transfer learning.

Internal medicine journal
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
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
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
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...

Algorithmic Identification of Treatment-Emergent Adverse Events From Clinical Notes Using Large Language Models: A Pilot Study in Inflammatory Bowel Disease.

Clinical pharmacology and therapeutics
Outpatient clinical notes are a rich source of information regarding drug safety. However, data in these notes are currently underutilized for pharmacovigilance due to methodological limitations in text mining. Large language models (LLMs) like Bidir...

Systematic evaluation of common natural language processing techniques to codify clinical notes.

PloS one
Proper codification of medical diagnoses and procedures is essential for optimized health care management, quality improvement, research, and reimbursement tasks within large healthcare systems. Assignment of diagnostic or procedure codes is a tediou...

Deep learning model for personalized prediction of positive MRSA culture using time-series electronic health records.

Nature communications
Methicillin-resistant Staphylococcus aureus (MRSA) poses significant morbidity and mortality in hospitals. Rapid, accurate risk stratification of MRSA is crucial for optimizing antibiotic therapy. Our study introduced a deep learning model, PyTorch_E...