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To pioneer the first artificial intelligence system integrating radiological and objective clinical data, simulating the clinical reasoning process, for the early prediction of high-risk influenza patients.

Machine learning applications for longitudinal electronic health records often forecast the risk of events at fixed time points, whereas survival analysis achieves dynamic risk prediction by estimating time-to-event distributions. Here, we propose a novel conditional variational autoencoder-based method, DySurv, which uses a combination of static and longitudinal measurements from electronic health records to estimate the individual risk of death dynamically.

To reflect on the achievements of the Agency for Healthcare Research and Quality's (AHRQ) Digital Healthcare Research Program over the past 20 years, evaluate its impact on US healthcare quality and safety, and outline current and future priorities for digital healthcare research and innovation.

To explore home healthcare (HHC) clinicians' needs for Clinical Decision Support Systems (CDSS) information delivery for early risk warning within HHC workflows.

Unplanned readmissions following a hospitalization remain common despite significant efforts to curtail these. Wearable devices may offer help identify patients at high risk for an unplanned readmission.

Accurate record linkage (RL) enables consolidation and de-duplication of data from disparate datasets, resulting in more comprehensive and complete patient data. However, conducting RL with low quality or unfit data can waste institutional resources on poor linkage results. We aim to evaluate data linkability to enhance the effectiveness of record linkage.

Clinical Data Warehouses (CDW) are the designated infrastructures to enable access and analysis of large quantities of electronic health record data. Building and managing such systems implies extensive "data work" and coordination between multiple stakeholders. Our study focuses on the challenges these stakeholders face when designing, operating, and ensuring the durability of CDWs for research.

This article describes the design and evaluation of MS Pattern Explorer, a novel visual tool that uses interactive machine learning to analyze fitness wearables' data. Applied to a clinical study of multiple sclerosis (MS) patients, the tool addresses key challenges: managing activity signals, accelerating insight generation, and rapidly contextualizing identified patterns. By analyzing sensor measurements, it aims to enhance understanding of MS symptomatology and improve the broader problem of clinical [...]

Integrating artificial intelligence (AI) in healthcare settings has the potential to benefit clinical decision-making. Addressing challenges such as ensuring trustworthiness, mitigating bias, and maintaining safety is paramount. The lack of established methodologies for pre- and post-deployment evaluation of AI tools regarding crucial attributes such as transparency, performance monitoring, and adverse event reporting makes this situation challenging.