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In acute chest pain management, risk stratification tools, including medical history, are recommended. We compared the fraction of patients with sufficient clinical data obtained using computerized history taking software (CHT) versus physician-acquired medical history to calculate established risk scores and assessed the patient-by-patient agreement between these 2 ways of obtaining medical history information.

This study evaluates regularization variants in logistic regression (L1, L2, ElasticNet, Adaptive L1, Adaptive ElasticNet, Broken adaptive ridge [BAR], and Iterative hard thresholding [IHT]) for discrimination and calibration performance, focusing on both internal and external validation.

Linking information on Japanese pharmaceutical products to global knowledge bases (KBs) would enhance international collaborative research and yield valuable insights. However, public access to mappings of Japanese pharmaceutical products that use international controlled vocabularies remains limited. This study mapped YJ codes to RxNorm ingredient classes, providing new insights by comparing Japanese and international drug-drug interaction (DDI) information using a case study methodology.

Error analysis plays a crucial role in clinical concept extraction, a fundamental subtask within clinical natural language processing (NLP). The process typically involves a manual review of error types, such as contextual and linguistic factors contributing to their occurrence, and the identification of underlying causes to refine the NLP model and improve its performance. Conducting error analysis can be complex, requiring a combination of NLP expertise and domain-specific knowledge. Due [...]

The transition to digital tools prompted by the pandemic made evident digital disparities. To address digital literacy gaps, we implemented a system-wide digital navigation program.

Social media-based public health research is crucial for epidemic surveillance, but most studies identify relevant corpora with keyword-matching. This study develops a system to streamline the process of curating colloquial medical dictionaries. We demonstrate the pipeline by curating a Unified Medical Language System (UMLS)-colloquial symptom dictionary from COVID-19-related tweets as proof of concept.

Leverage electronic health record (EHR) audit logs to develop a machine learning (ML) model that predicts which notes a clinician wants to review when seeing oncology patients.

Medical research faces substantial challenges from noisy labels attributed to factors like inter-expert variability and machine-extracted labels. Despite this, the adoption of label noise management remains limited, and label noise is largely ignored. To this end, there is a critical need to conduct a scoping review focusing on the problem space. This scoping review aims to comprehensively review label noise management in deep learning-based medical prediction problems, which includes label [...]

Synthesizing and evaluating inconsistent medical evidence is essential in evidence-based medicine. This study aimed to employ ChatGPT as a sophisticated scientific reasoning engine to identify conflicting clinical evidence and summarize unresolved questions to inform further research.