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The objective of this study is to provide an overview of the current landscape of individualized treatment effects (ITE) estimation, specifically focusing on methodologies proposed for time-series electronic health records (EHRs). We aim to identify gaps in the literature, discuss challenges, and propose future research directions to advance the field of personalized medicine.

This study addresses the significant challenges posed by emerging SARS-CoV-2 variants, particularly in developing diagnostics and therapeutics. Drug repurposing is investigated by identifying critical regulatory proteins impacted by the virus, providing rapid and effective therapeutic solutions for better disease management.

Report the development of the patient-centered myAURA application and suite of methods designed to aid epilepsy patients, caregivers, and clinicians in making decisions about self-management and care.

We aimed to develop a highly interpretable and effective, machine learning (ML)-based risk prediction algorithm to predict in-hospital mortality, intubation, and adverse cardiovascular events in patients hospitalized with coronavirus disease 2019 (COVID-19) in Australia (AUS-COVID Score).

A proof-of-concept study aimed at designing and implementing Visual & Interactive Engagement With Electronic Records (VIEWER), a versatile toolkit for visual analytics of clinical data, and systematically evaluating its effectiveness across various clinical applications while gathering feedback for iterative improvements.

To develop a framework that models the impact of electronic health record (EHR) systems on healthcare professionals' well-being and their relationships with patients, using interdisciplinary insights to guide machine learning in identifying value patterns important to healthcare professionals in EHR systems.

This study introduces Smart Imitator (SI), a 2-phase reinforcement learning (RL) solution enhancing personalized treatment policies in healthcare, addressing challenges from imperfect clinician data and complex environments.

To address the challenges of data heterogeneity and manual feature engineering in clinical predictive modeling, we introduce FHIR-Former, an open-source framework integrating Fast Healthcare Interoperability Resources (FHIR) with large language models (LLMs) to automate and standardize clinical prediction tasks.

To evaluate the accuracy, computational cost, and portability of a new natural language processing (NLP) method for extracting medication information from clinical narratives.