Latest AI and machine learning research in information technology for healthcare professionals.
Continuous physiological monitoring using consumer-grade wearables offers a transformative opportunity for clinical care and research, yet integration remains hindered by device heterogeneity, proprietary data formats, and strict regulatory requirements. We present an event-driven, cloud-native system designed to ingest, normalize, and analyze high-frequency vital signs from wearables at scale and...
Perioperative risk prediction models are often limited by narrow surgical populations, incomplete intraoperative data, poor calibration, and limited interpretability. We present a domain-structured ensemble framework for perioperative outcome prediction using routinely collected electronic health record (EHR) data. Predictors are organized into patient-related, surgery-related, and anesthetics-rel...
Researchers increasingly need to explore hypotheses that span multimodal data across different scales, organisms, and domains. In practice, this requi...
Artificial intelligence (AI) has the potential to transform healthcare, with advanced multimodal approaches showing great promise in leveraging divers...
Hospitals are rapidly adopting artificial intelligence for triage, imaging, scheduling etc., yet most deployments remain isolated point solutions lock...
Electronic health record (EHR) feature engineering is a major bottleneck in clinical research and AI, accounting for 39-45% of data scientists' worklo...
Feature selection is a critical step in electronic health record (EHR)-based predictive modeling, where input variables are often high-dimensional, sp...
Emergency triage requires reliable decisions within a short time period. However, the available electronic health record (EHR) data, including structu...
Access to holistic, multimodal data improves the performance of Artificial Intelligence (AI) in medical classification tasks compared to utilizing sin...
Problem Authentic patient encounters are the raw material of clinical learning, yet the educational resources learners receive are rarely keyed to the...
Electronic Health Records (EHRs) are widely used for clinical risk prediction using machine learning. International Classification of Diseases (ICD) c...
AI-generated videos are becoming increasingly realistic and difficult to distinguish from authentic ones, which facilitates malicious misuse and poses...
Postoperative adverse events, including mortality and morbidity, remain a major global burden, many of which are preventable through early identificat...
Background: Interoperable clinical decision support system (CDSS) rules provide a pathway to interoperability, a well-recognized challenge in health i...
Foundation models trained on electronic healthcare records (EHRs) have gained traction with the aim to transform personalised medicine. However, their...
Brain tumors such as glioma, meningioma, and pituitary adenoma alter the mechanical behavior of soft brain tissue, yet common diagnostic methods rely ...
Objective: Genetic disease is common in Level IV Neonatal Intensive Care Units (NICUs), yet clinicians often struggle to identify infants who would be...
Autoregressive foundation models for electronic health records (EHRs) typically inherit pretraining methods from language modeling, where patient traj...
Autoregressive foundation models trained on tokenized electronic health records (EHRs) can support zero-shot clinical prediction, yet most operate on ...
Clinical decisions for determining optimal patient-specific interventions are complicated prediction tasks that rely on health care professionals' und...