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Real-world data from vendor-aggregated health information exchanges represent a powerful resource for retrospective clinical research. Epic's Cosmos platform, a large-scale centralized data warehouse, enables querying deidentified electronic health record data.We describe the iterative development of a generalizable clinical informatics workflow for retrospective studies using Cosmos.We applied the Plan-Do-Study-Act cycle to iteratively refine a collaborative research process using Cosmo's SlicerDicer interface, focusing on setting, team, technical preparation, and workflow.We identified multiple [...]

Emerging efforts to identify patients at risk of suicide have focused on the development of predictive algorithms for use in healthcare settings. We address a major challenge in effective risk modeling in healthcare settings with insufficient data with which to create and apply risk models. This study aimed to improve risk prediction using transfer learning or data fusion by incorporating risk information from external data sources to augment the data [...]

The integration of predictive models into live clinical care requires scientific testing before implementation to ensure patient safety. We built and technically implemented a model that predicts which patients require an electrocardiogram (ECG) to screen for heart attacks within 10 minutes of their arrival to the Emergency Department. We developed a structured framework for the in vitro to in vivo translation of the model through implementation as clinical decision support [...]

This study aims to enhance the diagnostic process for rare diseases using case-based reasoning (CBR). CBR compares new cases with historical data, utilizing both structured and unstructured clinical data.

Extracting social determinants of health (SDoHs) from medical notes depends heavily on labor-intensive annotations, which are typically task-specific, hampering reusability and limiting sharing. Here, we introduce SDoH-GPT, a novel framework leveraging few-shot learning large language models (LLMs) to automate the extraction of SDoH from unstructured text, aiming to improve both efficiency and generalizability.

Electronic Health Records (EHRs) sampled from different populations can introduce unwanted biases, limit individual-level data sharing, and make the data and fitted model hardly transferable across different population groups. In this context, our main goal is to design an effective method to transfer knowledge between population groups, with computable guarantees for suitability, and that can be applied to quantify treatment disparities.

Accurately measuring patient similarity is essential for precision medicine, enabling personalized predictive modeling, disease subtyping, and individualized treatment by identifying patients with similar characteristics to an index patient. This study aims to develop an electronic health record-based patient similarity estimation framework to enhance personalized predictive modeling for Acute Kidney Injury (AKI), a complex and life-threatening condition where accurate prediction is critical for timely intervention.

To improve prediction of chronic kidney disease (CKD) progression to end-stage renal disease (ESRD) using machine learning (ML) and deep learning (DL) models applied to integrated clinical and claims data with varying observation windows, supported by explainable artificial intelligence (AI) to enhance interpretability and reduce bias.

Clinicians currently make decisions about placing an intracranial pressure (ICP) monitor in children with traumatic brain injury (TBI) without the benefit of an accurate clinical decision support tool. The goal of this study was to develop and validate a model that predicts placement of an ICP monitor and updates as new information becomes available.

The integration of large language models (LLMs) into clinical diagnostics presents significant challenges regarding their accuracy and reliability.This study aimed to evaluate the performance of DeepSeek R1, an open-source reasoning model, alongside two other LLMs, GPT-4.1 and Claude 3.5 Sonnet, across multiple-choice clinical cases.A dataset of complex medical cases representative of real-world clinical practice was selected.For efficiency, models were accessed via application programming interfaces (APIs) and assessed using standardized prompts [...]