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

Global efforts aimed at ending human immunodeficiency virus (HIV) incidence have adapted and evolved since the turn of the century. The utilization of machine learning incorporated into an electronic health record (EHR) can be refined into prediction models that identify when an individual is at greater HIV infection risk. This can create a novel and innovative approach to identifying patients eligible for preventative therapy.

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 review pediatric artificial intelligence (AI) implementation studies from 2010-2021 and analyze reported performance measures.

Cerebral aneurysms (CAs) affect 3-5% of the general population, with saccular aneurysms being the most common type. Despite advances in treatment, patient understanding of CAs and associated procedures remains limited, impacting informed consent and treatment outcomes.

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.

Objective To develop a measure of Health Information Exchange (HIE) for characterizing the density of inter-hospital HIE connections and identify regional characteristics associated with HIE network density Materials and Methods HIE network density was measured as the proportion of hospital pairs within a region that are connected through HIE. The 2022 American Hospital Association's Information Technology Supplement survey was used to calculate HIE network density for US hospital referral regions [...]

The "Cancer Risk Calculator" mobile application aims to inform patients about their personal risks of cancer and their risk factors influencingsaid risks. The present analysis examines the responses to a questionnaire submitted by oncology patients treated with radiotherapy or their family members.

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.

Engagement of clinicians who understand clinical workflows and technology constraints can accelerate the development and implementation of better electronic health record (EHR) designs that improve quality and reduce burnout. Provider builder programs can accelerate clinical informatics education for a broader coalition of clinical specialties.