Latest AI and machine learning research in information technology for healthcare professionals.
The collection and use of large-scale medical information for developing artificial intelligence engines are actively ongoing. In Japan, collection systems have been built to collect data for medical image analysis and disease repositories. In the experimental project for the next generation medical infrastructure law, a centrally integrated basic system was developed, and standardized electronic ...
Electronic healthcare records data promises to improve the efficiency of patient eligibility screening, which is an important factor in the success of clinical trials and observational studies. To bridge the sociotechnical gap in cohort identification by end-users, who are clinicians or researchers unfamiliar with underlying EHR databases, we previously developed a natural language query interface...
Data imbalance is a well-known challenge in the development of machine learning models. This is particularly relevant when the minority class is the c...
OBJECTIVE: We aimed to develop a data-driven machine learning model for predicting critical deterioration events from routinely collected EHR data in ...
BACKGROUND: Substance misuse is a heterogeneous and complex set of behavioural conditions that are highly prevalent in hospital settings and frequentl...
The development of an ontology facilitates the organization of the variety of concepts used to describe different terms in different resources. The pr...
The World Health Organization defines, that high quality health services should be effective, safe, people-centered, timely, equitable, integrated, an...
The wide adoption of Electronic Health Records (EHR) in hospitals provides unique opportunities for high throughput phenotyping of patients. The pheno...
Telemedicine, which integrates medicine, communication, engineering, information and other disciplines, is a hot emerging cross field in recent years....
With the continuing shortage and unequal distribution of medical resources, our objective is to develop a general diagnosis framework that utilizes a...
UNLABELLED: To meet the increasing demand for data sharing, data reuse and meta-analysis in the immunology research community, we have developed the d...
We interviewed six clinicians to learn about their lived experience using electronic health records (EHR, Allscripts users) using a semi-structured in...
Named entities are the main carriers of relevant medical knowledge in Electronic Medical Records (EMR). Clinical electronic medical records lead to pr...
Artificial Intelligence has the potential to disrupt the way clinical radiology is practiced globally. However, there are barriers that radiologists s...
INTRODUCTION: Patient outcome prediction models are underused in clinical practice because of lack of integration with real-time patient data. The ele...
Digital technologies have emerged in various dimensions of human life, ranging from education to professional services to well-being. In particular, h...
Patient safety event (PSE) reports are a useful lens to understand hazards and patient safety risks in healthcare systems. However, patient safety off...
How can an agency like the U.S. Food & Drug Administration ("FDA") effectively regulate software that is constantly learning and adapting to real-worl...
Apart from the tremendous increase in the demand for telemedicine during the COVID-19 pandemic, the use of telemedical technology offers many advantag...
OBJECTIVE: One important concept in informatics is data which meets the principles of Findability, Accessibility, Interoperability and Reusability (FA...