Inferring multimodal latent topics from electronic health records

Electronic health records (EHR) are rich heterogeneous collections of patient health information, whose broad adoption provides clinicians and researchers unprecedented opportunities for health informatics, disease-risk prediction, actionable clinical recommendations, and precision medicine. However...

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Veröffentlicht in:	Nature communications Jg. 11; H. 1; S. 2536 - 17
Hauptverfasser:	Li, Yue, Nair, Pratheeksha, Lu, Xing Han, Wen, Zhi, Wang, Yuening, Dehaghi, Amir Ardalan Kalantari, Miao, Yan, Liu, Weiqi, Ordog, Tamas, Biernacka, Joanna M., Ryu, Euijung, Olson, Janet E., Frye, Mark A., Liu, Aihua, Guo, Liming, Marelli, Ariane, Ahuja, Yuri, Davila-Velderrain, Jose, Kellis, Manolis
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 21.05.2020 Nature Publishing Group Nature Portfolio
Schlagworte:	631/114/1305 639/705/1042 692/700 Bayes Theorem Bayesian analysis Bipolar disorder Cardiovascular diseases Coronary artery disease Data integration Databases, Factual Diagnostic systems Disease Electronic health records Electronic Health Records - classification Electronic Health Records - statistics & numerical data Electronic medical records Heart diseases Humanities and Social Sciences Humans Informatics Laboratory tests Machine Learning Mathematical models Medical Informatics - methods Missing data Modelling Models, Statistical multidisciplinary Phenotype Precision medicine Science Science (multidisciplinary)
ISSN:	2041-1723, 2041-1723
Online-Zugang:	Volltext
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