Latest AI and machine learning research in information technology for healthcare professionals.
In recent years, there is an increasing demand for sharing and integration of medical data in biomedical research. In order to improve a health care system, it is required to support the integration of data by facilitating semantic interoperability systems and practices. Semantic interoperability is difficult to achieve in these systems as the conceptual models underlying datasets are not fully ex...
INTRODUCTION: Clinical deterioration (ICU transfer and cardiac arrest) occurs during approximately 5-10% of hospital admissions. Existing prediction models have a high false positive rate, leading to multiple false alarms and alarm fatigue. We used routine vital signs and laboratory values obtained from the electronic medical record (EMR) along with a machine learning algorithm called a neural net...
OBJECTIVE: Phenotyping algorithms applied to electronic health record (EHR) data enable investigators to identify large cohorts for clinical and genom...
This paper presents a new study based on a machine learning technique, specifically an artificial neural network, for predicting systolic blood pressu...
BACKGROUND: Longitudinal data sources, such as electronic health records (EHRs), are very valuable for monitoring adverse drug events (ADEs). However,...
OBJECTIVE: The objective of this study is to develop an algorithm to accurately identify children with severe early onset childhood obesity (ages 1-5....
OBJECTIVES: This paper presents a remote triage support algorithm as a part of a complex military telemedicine system which provides continuous monito...
BACKGROUND: The high costs involved in the development of Clinical Decision Support Systems (CDSS) make it necessary to share their functionality acro...
BACKGROUND: The Cell Ontology (CL) is an OBO Foundry candidate ontology covering the domain of canonical, natural biological cell types. Since its inc...
OBJECTIVE: The combination of phenomic data from electronic health records (EHR) and clinical data repositories with dense biological data has enabled...
Electronic Health Record (EHR) use in India is generally poor, and structured clinical information is mostly lacking. This work is the first attempt a...
Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summ...
The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how inv...
OBJECTIVE: Electronic medical records (EMRs) are increasingly repurposed for activities beyond clinical care, such as to support translational researc...
BACKGROUND: Identifying partial mappings between two terminologies is of special importance when one terminology is finer-grained than the other, as i...
Telemedicine helps to deliver health services electronically to patients with the advancement of communication systems and health informatics. Chronic...
BACKGROUND: Application of novel machine learning approaches to electronic health record (EHR) data could provide valuable insights into disease proce...
BACKGROUND: The gap between a large growing number of genetic tests and a suboptimal clinical workflow of incorporating these tests into regular clini...
Analysis of data from Electronic Health Records (EHR) presents unique challenges, in particular regarding nonuniform temporal resolution of longitudin...
Using longitudinal data in electronic health records (EHRs) for post-marketing adverse drug event (ADE) detection allows for monitoring patients throu...