BMC medical informatics and decision making
Sep 7, 2020
BACKGROUND: Automated systems that use machine learning to estimate a patient's risk of death are being developed to influence care. There remains sparse transparent reporting of model generalizability in different subpopulations especially for imple...
IMPORTANCE: Deep learning, a family of machine learning models that use artificial neural networks, has achieved great success at predicting outcomes in nonmedical domains.
Mobile health (m-health) is the term of monitoring the health using mobile phones and patient monitoring devices etc. It has been often deemed as the substantial breakthrough in technology in this modern era. Recently, artificial intelligence (AI) an...
BMC medical informatics and decision making
Aug 28, 2020
BACKGROUNDS: Knowledge discovery from breast cancer treatment records has promoted downstream clinical studies such as careflow mining and therapy analysis. However, the clinical treatment text from electronic health data might be recorded by differe...
Journal of pain and symptom management
Aug 25, 2020
CONTEXT: Goals-of-care discussions are an important quality metric in palliative care. However, goals-of-care discussions are often documented as free text in diverse locations. It is difficult to identify these discussions in the electronic health r...
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 ...