Latest AI and machine learning research in information technology for healthcare professionals.
BACKGROUND: Mobile health (MH) technologies including clinical decision support systems (CDSS) provide an efficient method for patient monitoring and treatment. A mobile CDSS is based on real-time sensor data and historical electronic health record (EHR) data. Raw sensor data have no semantics of their own; therefore, a computer system cannot interpret these data automatically. In addition, the in...
Automatic ICD-10 coding is an unresolved challenge in terms of Machine Learning tasks. Despite hospitals generating an enormous amount of clinical documents, data is considerably sparse, associated with a very skewed and unbalanced code distribution, what entails reduced interoperability. In addition, in some languages the availability of coded documents is very limited. This paper proposes a cros...
As a widely used imaging modality in the medical field, ultrasound has been applied in community medicine, rural medicine, and even telemedicine in re...
Current guidelines for treatment decision making largely rely on data from randomized controlled trials (RCTs) studying average treatment effects. The...
BACKGROUND: Physical activity data provides important information on disease onset, progression, and treatment outcomes. Although analyzing physical a...
BACKGROUND: Because of the strong link between childhood obesity and adulthood obesity comorbidities, and the difficulty in decreasing body mass index...
OBJECTIVE: Electronic medical records (EMRs) are manually annotated by healthcare professionals and specialized medical coders with a standardized set...
BACKGROUND: Electronic medical records (EMRs) contain a variety of valuable medical concepts and relations. The ability to recognize relations between...
This article examines the history of the telemedicine intensive care unit (tele-ICU), the current state of clinical decision support systems (CDSS) in...
BACKGROUND AND OBJECTIVE: Deep learning techniques have been successfully applied to tackle several image classification problems in bioimaging. Howev...
Acute patient treatment can heavily profit from AI-based assistive and decision support systems, in terms of improved patient outcome as well as incre...
BACKGROUND: Disease prediction based on Electronic Health Records (EHR) has become one hot research topic in biomedical community. Existing work mainl...
The use of Electronic Health Records (EHR) for translational research can be challenging due to difficulty in extracting accurate disease phenotype da...
BACKGROUND: While doctors should analyze a large amount of electronic medical record (EMR) data to conduct clinical research, the analyzing process re...
BACKGROUND: Existing prediction models for acute respiratory distress syndrome (ARDS) require manual chart abstraction and have only fair performance-...
There is a growing notion that artificial general intelligence (AGI) will replace some of the work done by trained professionals, including physicians...
The combination of big data and deep learning is a world-shattering technology that can make a great impact on any industry if used in a proper way. W...
The feasibility of integrating remote presence technology within a simulation scenario for psychiatric-mental health nursing (PMHN) students to develo...
BACKGROUND: Smoking is an established risk factor for oral diseases and, therefore, dental clinicians routinely assess and record their patients' deta...
BACKGROUND: Social isolation is an important social determinant that impacts health outcomes and mortality among patients. The National Academy of Med...