Latest AI and machine learning research in information technology for healthcare professionals.
Early identification of resource needs is instrumental in promoting efficient hospital resource management. Hospital information systems, and electronic health records (EHR) in particular, collect valuable demographic and clinical patient data from the moment patients are admitted, which can help predict expected resource needs in early stages of patient episodes. To this end, this article propose...
Effective and timely disease surveillance systems have the potential to help public health officials design interventions to mitigate the effects of disease outbreaks. Currently, healthcare-based disease monitoring systems in France offer influenza activity information that lags real-time by one to three weeks. This temporal data gap introduces uncertainty that prevents public health officials fro...
HL7 Fast Healthcare Interoperability Resources (FHIR) is one of the current data standards for enabling electronic healthcare information exchange. Pr...
The wide adoption of Electronic Health Records (EHR) has resulted in large amounts of clinical data becoming available, which promises to support serv...
Lack of standardized representation of natural language processing (NLP) components in phenotyping algorithms hinders portability of the phenotyping a...
It is well known that information technology (IT) can play a pivotal role in enhancing healthcare quality and patient safety. The use of computational...
Hypertrophic Cardiomyopathy (HCM) is the most common genetic heart disease in the US and is known to cause sudden death (SCD) in young adults. While s...
Maintaining a high quality of conversation between doctors and patients is essential in telehealth services, where efficient and competent communicati...
Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-lear...
Digital data sources have become ubiquitous in modern culture in the era of digital technology but often tend to be under-researched because of restr...
Sepsis is a major cause of mortality among hospitalized patients worldwide. Shorter time to administration of broad-spectrum antibiotics is associated...
Electronic health records (EHR) contain large volumes of unstructured text, requiring the application of information extraction (IE) technologies to e...
Machine learning (ML) and deep learning (DL) can successfully predict high prevalence events in very large databases (big data), but the value of this...
Coronary artery disease (CAD) is the major cause of human death worldwide. The development of new CAD early diagnosis methods based on medical big dat...
OBJECTIVE: Machine learning (ML) algorithms are now widely used in predicting acute events for clinical applications. While most of such prediction ap...
Decision-making in fertility care is on the cusp of a significant frameshift. Online tools to integrate artificial intelligence into the decision-maki...
The field of immunology is rapidly progressing toward a systems-level understanding of immunity to tackle complex infectious diseases, autoimmune cond...
Understanding a patient's medical history, such as how long symptoms last or when a procedure was performed, is vital to diagnosing problems and provi...
Understanding patient accumulation of comorbidities can facilitate healthcare strategy and personalized preventative care. We applied a directed netwo...
Rising rates of NCDs threaten fragile healthcare systems in low- and middle-income countries. Fortunately, new digital technology provides tools to mo...