Latest AI and machine learning research in information technology for healthcare professionals.
BACKGROUND AND OBJECTIVE: Electronic medical records (EMRs) contain an amount of medical knowledge which can be used for clinical decision support. We attempt to integrate this medical knowledge into a complex network, and then implement a diagnosis model based on this network.
Exponential surge in health care data, such as longitudinal data from electronic health records (EHR), sensor data from intensive care unit (ICU), etc., is providing new opportunities to discover meaningful data-driven characteristics and patterns ofdiseases. Recently, deep learning models have been employedfor many computational phenotyping and healthcare prediction tasks to achieve state-of-the-...
An electronic health record (EHR) can assist the delivery of high-quality patient care, in part by providing the capability for a broad range of clini...
Natural Language Processing (NLP) is essential for concept extraction from narrative text in electronic health records (EHR). To extract numerous and ...
Manual Chart Review (MCR) is an important but labor-intensive task for clinical research and quality improvement. In this study, aiming to accelerate ...
PURPOSE: Telemedicine is increasingly utilized in the evaluation of critically ill patients, including those with decreased level of consciousness (LO...
OBJECTIVE: This retrospective case series study of the effectiveness of electroconvulsive therapy (ECT) augmentation on clozapine-resistant schizophre...
Interoperability across data sets is a key challenge for quantitative histopathological imaging. There is a need for an ontology that can support effe...
Leveraging large historical data in electronic health record (EHR), we developed Doctor AI, a generic predictive model that covers observed medical co...
Medical research is experiencing a paradigm shift from "one-size-fits-all" strategy to a precision medicine approach where the right therapy, for the ...
OBJECTIVE: To use natural language processing (NLP) in conjunction with the electronic medical record (EMR) to accurately identify patients with cereb...
Pre-eclampsia (PE) is a clinical syndrome characterized by new-onset hypertension and proteinuria at ≥20 weeks of gestation, and is a leading cause of...
Purpose To demonstrate the feasibility of contrast material-enhanced ulrasonographic (US) nephrostograms to assess ureteral patency after percutaneous...
The ability to predict psychiatric readmission would facilitate the development of interventions to reduce this risk, a major driver of psychiatric he...
The Q-UEL language of XML-like tags and the associated software applications are providing a valuable toolkit for Evidence Based Medicine (EBM). In th...
Patient interactions with health care providers result in entries to electronic health records (EHRs). EHRs were built for clinical and billing purpos...
OBJECTIVE: To discover diverse genotype-phenotype associations affiliated with Type 2 Diabetes Mellitus (T2DM) via genome-wide association study (GWAS...
Biomedical literature articles and narrative content from Electronic Health Records (EHRs) both constitute rich sources of disease-phenotype informati...
BACKGROUND: Disease and diagnosis have been the subject of much ontological inquiry. However, the insights gained therein have not yet been well enoug...
UNLABELLED: The lack of controlled terminology and ontology usage leads to incomplete search results and poor interoperability between databases. One ...