Latest AI and machine learning research in information technology for healthcare professionals.
Decision support tools increasingly integrate clinical knowledge such as medication indications and contraindications with electronic health record (EHR) data to support clinical care and patient safety. The availability of this encoded information and patient data provides an opportunity to develop measures of clinical decision complexity that may be of value for quality improvement and research ...
The secondary use of EHR data for research is expected to improve health outcomes for patients, but the benefits will only be realized if the data in the EHR is of sufficient quality to support these uses. A data quality (DQ) ontology was developed to rigorously define concepts and enable automated computation of data quality measures. The healthcare data quality literature was mined for the impor...
Electronic medical records (EMRs) are capturing increasing amounts of data per patient. For clinicians to efficiently and accurately understand a pati...
BACKGROUND AND AIMS: Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease worldwide. Risk factors for NAFLD dise...
Machine translation is evolving quite rapidly in terms of quality. Nowadays, we have several machine translation systems available in the web, which p...
The 2014 i2b2/UTHealth natural language processing shared task featured a track focused on the de-identification of longitudinal medical records. For ...
Identifying populations of heart failure (HF) patients is paramount to research efforts aimed at developing strategies to effectively reduce the burde...
The present-day health data ecosystem comprises a wide array of complex heterogeneous data sources. A wide range of clinical, health care, social and ...
BACKGROUND: In order to proactively manage congestive heart failure (CHF) patients, an effective CHF case finding algorithm is required to process bot...
Current medical information systems are too complex to be meaningfully exploited. Hence there is a need to develop new strategies for maximising the e...
The 2014 i2b2/UTHealth natural language processing shared task featured a track focused on identifying risk factors for heart disease (specifically, C...
BACKGROUND: Electronic medical record (EMR) systems have become widely used throughout the world to improve the quality of healthcare and the efficien...
BACKGROUND: The aim of this study was to examine the impact of a telemedical robot on trauma intensive care unit (TICU) clinician teamwork (i.e., team...
OBJECTIVES: This review examines work on automated summarization of electronic health record (EHR) data and in particular, individual patient record s...
In Electronic Health Records (EHRs), much of valuable information regarding patients' conditions is embedded in free text format. Natural language pro...
As the core of health information technology (HIT), electronic medical record (EMR) systems have been changing to meet health care demands. To constru...
INTRODUCTION: The semantic interoperability of electronic healthcare records (EHRs) systems is a major challenge in the medical informatics area. Inte...
BACKGROUND: In order to retrieve useful information from scientific literature and electronic medical records (EMR) we developed an ontology specific ...
BACKGROUND: Ontologies represent powerful tools in information technology because they enhance interoperability and facilitate, among other things, th...
The Internet of Things (IoT) allows machines and devices in the world to connect with each other and generate a huge amount of data, which has a great...