Latest AI and machine learning research in information technology for healthcare professionals.
BACKGROUND: Identifying children at high risk with complex health needs (CCHN) who have intersecting medical and social needs is challenging. This study's objectives were to (1) develop and evaluate an electronic health record (EHR)-based clinical predictive model ("model") for identifying high-risk CCHN and (2) compare the model's performance as a clinical decision support (CDS) to other CDS tool...
BACKGROUND: Falls in older people are common and morbid. Prediction models can help identifying individuals at higher fall risk. Electronic health records (EHR) offer an opportunity to develop automated prediction tools that may help to identify fall-prone individuals and lower clinical workload. However, existing models primarily utilise structured EHR data and neglect information in unstructured...
The artificial intelligence (AI) chatbot ChatGPT has generated both huge interest and deep concern since its launch in November 2022.1 ChatGPT, a larg...
From basic research to the bedside, precise terminology is key to advancing medicine and ensuring optimal and appropriate patient care. However, the w...
The NHGRI-EBI GWAS Catalog (www.ebi.ac.uk/gwas) is a FAIR knowledgebase providing detailed, structured, standardised and interoperable genome-wide ass...
BACKGROUND: To effectively monitor medical insurance funds in the era of big data, the study tries to construct an inpatient cost rationality judgemen...
Advancements in high-throughput sequencing have yielded vast amounts of genomic data, which are studied using genome-wide association study (GWAS)/phe...
BACKGROUND: Malnutrition is a serious health risk facing older people living in residential aged care facilities. Aged care staff record observations ...
ChatGPT is a virtual assistant with artificial intelligence (AI) that uses natural language to communicate, i.e., it holds conversations as those that...
The adoption of electronic health records (EHRs) and digitization of health data over the past decade is ushering in the next generation of digital he...
The advancement of healthcare towards P5 medicine requires communication and cooperation between all actors and institutions involved. Interoperabilit...
Similar to managing software packages, managing the ontology life cycle involves multiple complex workflows such as preparing releases, continuous qua...
PURPOSE: In patients with ophthalmic disorders, psychosocial risk factors play an important role in morbidity and mortality. Proper and early psychiat...
Chronic wounds have significant impacts on patient health-related quality of life (HRQoL) and the healthcare expenditures. Various complex decision-ma...
Electronic Medical Record (EMR) is the data basis of intelligent diagnosis. The diagnosis results of an EMR are multi-disease, including normal diagno...
PURPOSE: The advancement of natural language processing (NLP) has promoted the use of detailed textual data in electronic health records (EHRs) to sup...
Medical practices are engaged and motivated by new technologies and methods to enhance patient care as efficiently as possible. These new methods and ...
Most screening tests for Diabetes Mellitus (DM) in use today were developed using electronically collected data from Electronic Health Record (EHR). H...
With an increasing number of biomedical ontologies being evolved independently, matching these ontologies to solve the interoperability problem has be...
A significant portion of data in Electronic Health Records is only available as unstructured text, such as surgical or finding reports, clinical notes...