Latest AI and machine learning research in information technology for healthcare professionals.
In this work, a flexible and extensive digital platform for Smart Homes is presented, exploiting the most advanced technologies of the Internet of Things, such as Radio Frequency Identification, wearable electronics, Wireless Sensor Networks, and Artificial Intelligence. Thus, the main novelty of the paper is the system-level description of the platform flexibility allowing the interoperability of...
Comprehensive reviews of syndromic surveillance in animal health have highlighted the hindrances to integration and interoperability among systems when data emerge from different sources. Discussions with syndromic surveillance experts in the fields of animal and public health, as well as computer scientists from the field of information management, have led to the conclusion that a major componen...
The term Artificial Intelligence (AI) was coined by John McCarthy in 1956 during a conference held on this subject. However, the possibility of machin...
Electronic health record (EHR) data provide promising opportunities to explore personalized treatment regimes and to make clinical predictions. Compar...
BACKGROUND: Rapid antibiotic administration is known to improve sepsis outcomes, however early diagnosis remains challenging due to complex presentati...
An unprecedented amount of clinical information is now available via electronic health records (EHRs). These massive data sets have stimulated opportu...
The emerging demographic trends toward an aging population, demand new ways and solutions to improve the quality of elderly life. These include, prolo...
BACKGROUND: Existing resources to assist the diagnosis of rare diseases are usually curated from the literature that can be limited for clinical use. ...
The diagnosis of type 2 diabetes (T2D) at an early stage has a key role for an adequate T2D integrated management system and patient's follow-up. Rece...
This study trained long short-term memory (LSTM) recurrent neural networks (RNNs) incorporating an attention mechanism to predict daily sepsis, myocar...
Artificial intelligence (AI)-based methods have emerged as powerful tools to transform medical care. Although machine learning classifiers (MLCs) have...
Widely-prescribed prodrug opioids (e.g., hydrocodone) require conversion by liver enzyme CYP-2D6 to exert their analgesic effects. The most commonly p...
PURPOSE: Accurate image-based ophthalmic diagnosis relies on fundus image clarity. This has important implications for the quality of ophthalmic diagn...
Use of prescription opioids and problems of abuse and addiction have increased over the past decade. Claims-based studies have documented substantial ...
OBJECTIVE: Radiology reports are rich resources for biomedical researchers. Before utilization of radiology reports, experts must manually review thes...
Current approaches to predicting a cardiovascular disease (CVD) event rely on conventional risk factors and cross-sectional data. In this study, we ap...
BACKGROUND: A Cardiac-centered Frailty Ontology can be an important foundation for using NLP to assess patient frailty. Frailty is an important consid...
Operating room (OR) utilization is a significant determinant of hospital profitability. One aspect of this is surgical scheduling, which depends on ac...
BACKGROUND AND OBJECTIVE: Electronic Health Record (EHR) data often include observation records that are unlikely to represent the "truth" about a pat...
BACKGROUND: The optical accessibility of the eye and technological advances in ophthalmic diagnostics have put ophthalmology at the forefront of data-...