OBJECTIVES: Analyze papers published in 2019 within the medical natural language processing (NLP) domain in order to select the best works of the field.
OBJECTIVES: Clinical Research Informatics (CRI) declares its scope in its name, but its content, both in terms of the clinical research it supports-and sometimes initiates-and the methods it has developed over time, reach much further than the name s...
OBJECTIVES: To select, present, and summarize the most relevant papers published in 2018 and 2019 in the field of Ontologies and Knowledge Representation, with a particular focus on the intersection between Ontologies and Machine Learning.
Electronic health records (EHRs) contain rich documentation regarding disease symptoms and progression, but EHR data is challenging to use for diagnosis prediction due to its high dimensionality, relative scarcity, and substantial level of noise. We ...
BACKGROUND: A key challenge for improving the quality of health care is to be able to use a common framework to work with patient information acquired in any of the health and life science disciplines. Patient information collected during dental care...
BACKGROUND: Mental health supported housing services are a key component in the rehabilitation of people with severe and complex needs. They are implemented widely in the UK and other deinstitutionalised countries but there have been few empirical st...
This paper considers the problems of modeling and predicting a long-term and "blurry" relapse that occurs after a medical act, such as a surgery. We do not consider a short-term complication related to the act itself, but a long-term relapse that cli...
BACKGROUND: A novel disease poses special challenges for informatics solutions. Biomedical informatics relies for the most part on structured data, which require a preexisting data or knowledge model; however, novel diseases do not have preexisting k...
Diagnosis prediction is an important predictive task in health care that aims to predict the patient future diagnosis based on their historical medical records. A crucial requirement for this task is to effectively model the high-dimensional, noisy, ...