Latest AI and machine learning research in information technology for healthcare professionals.
Automated face recognition has made rapid strides over the past decade due to the unprecedented rise of deep neural network (DNN) models that can be trained for domain-specific tasks. At the same time, foundation models that are pretrained on broad vision or vision-language tasks have shown impressive generalization across diverse domains, including biometrics. This raises an important question: D...
Clinicians lack precision medicine tools to estimate individualized treatment effects for patients with heart failure (HF). Causal machine learning leveraging electronic health records can estimate both average and individualized treatment effects, enabling estimation of treatment heterogeneity. Using Stony Brook University Hospital data, we compared the effectiveness of glucagon-like peptide-1 re...
Objective: To develop a workflow that transforms electronic health record data into machine learning-ready features for molecular endotype assignment ...
Automated recognition of autistic behaviors in children is essential for early intervention and objective clinical assessment. However, the developmen...
The rapid adoption of the Internet of Medical Things (IoMT) is transforming healthcare by enabling seamless connectivity among medical devices, system...
High inpatient opioid exposure is associated with increased risk of persistent opioid use. Early identification of high-risk patients may improve opio...
Objective: Behavioral and social factors (BSFs) substantially influence the risk, onset, and progression of Alzheimer disease and related dementias (A...
Neonates requiring intensive care are at increased risk for long-term neuropsychiatric disorders. However, clinical adoption of risk prediction models...
This study presents a fully automated methodology for early prediction studies in clinical settings, leveraging information extracted from unstructure...
Computational methods have advanced the analysis of animal behavior, yet significant challenges remain in data standardization, analytical reproducibi...
Longitudinal electronic health records (EHRs) form irregular event sequences that mix multiple clinical coding systems and care settings. Learning tra...
Background: Experiences of violence are reported frequently by mental health service users, victims of violence are at a greater risk of mental health...
Background: Electronic health records (EHRs) enable machine learning for diagnosis, prognosis, and clinical decision support. However, EHR standards v...
Background: Manual chart abstraction is a major bottleneck in clinical research. In oncology, important outcomes such as disease recurrence and the tr...
Background: Electronic health records (EHRs) enable machine learning for diagnosis, prognosis, and clinical decision support. However, EHR standards v...
While large-scale pretraining has revolutionized language modeling, its potential remains underexplored in healthcare with structured electronic healt...
Electronic health records (EHRs) are invaluable for clinical research, yet privacy concerns severely restrict data sharing. Synthetic data generation ...
Electronic health records (EHRs) and other real-world clinical data are essential for clinical research, medical artificial intelligence, and life sci...
Modern clinical practice increasingly depends on reasoning over heterogeneous, evolving, and incomplete patient data. Although recent advances in mult...
Unstructured Electronic Health Record (EHR) data, such as clinical notes, contain clinical contextual observations that are not directly reflected in ...