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To present a general framework providing high-level guidance to developers of computable algorithms for identifying patients with specific clinical conditions (phenotypes) through a variety of approaches, including but not limited to machine learning and natural language processing methods to incorporate rich electronic health record data.

To introduce quantum computing technologies as a tool for biomedical research and highlight future applications within healthcare, focusing on its capabilities, benefits, and limitations.

Heart failure (HF) impacts millions of patients worldwide, yet the variability in treatment responses remains a major challenge for healthcare professionals. The current treatment strategies, largely derived from population based evidence, often fail to consider the unique characteristics of individual patients, resulting in suboptimal outcomes. This study aims to develop computational models that are patient-specific in predicting treatment outcomes, by utilizing a large Electronic Health Records (EHR) database. The goal [...]

This study aims to investigate the feasibility of using Large Language Models (LLMs) to engage with patients at the time they are drafting a question to their healthcare providers, and generate pertinent follow-up questions that the patient can answer before sending their message, with the goal of ensuring that their healthcare provider receives all the information they need to safely and accurately answer the patient's question, eliminating back-and-forth messaging, and [...]

To use workflow execution models to highlight new considerations for patient-centered clinical decision support policies (PC CDS), processes, procedures, technology, and expertise required to support new workflows.

The current medical paradigm of evidence-based medicine relies on clinical guidelines derived from randomized clinical trials (RCTs), but these guidelines often overlook individual variations in treatment effects. Approaches have been proposed to develop models predicting the effects of individualized management, such as predictive allocation, individualizing treatment allocation. It is currently unknown whether widespread implementation of predictive allocation could result in better population-level outcomes over guideline-based therapy. We sought to simulate [...]

To explore the feasibility and challenges of mapping between SNOMED CT and the ICD-11 Foundation in both directions, SNOMED International and the World Health Organization conducted a pilot mapping project between September 2021 and August 2022.

The aim of this project was to create time-aware, individual-level risk score models for adverse drug events related to multiple sclerosis disease-modifying therapy and to provide interpretable explanations for model prediction behavior.