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, EHRs present several modeling challenges, including highly sparse data matrices, noisy irregular clinical notes, arbitrary biases in billing code assignment, diagnosis-driven lab tests, and heterogeneous data types. To address these challenges, we present MixEHR, a multi-view Bayesian topic model. We demonstrate MixEHR on MIMIC-III, Mayo Clinic Bipolar Disorder, and Quebec Congenital Heart Disease EHR datasets. Qualitatively, MixEHR disease topics reveal meaningful combinations of clinical features across heterogeneous data types. Quantitatively, we observe superior prediction accuracy of diagnostic codes and lab test imputations compared to the state-of-art methods. We leverage the inferred patient topic mixtures to classify target diseases and predict mortality of patients in critical conditions. In all comparison, MixEHR confers competitive performance and reveals meaningful disease-related topics.

Word associations are a common tool in research on the mental lexicon. Studies report that bilinguals produce different word associations in their non-native language than monolinguals, and propose at least three mechanisms responsible for this difference: bilinguals may rely on their native associations (through translation), on collocational patterns, and on the phonological similarity between words. In this paper, we first test the differences between monolingual and bilingual responses, showing that these differences are consistent and significant. Second, we present a computational model of bilingual word associations, implemented as a semantic network paired with a retrieval mechanism. Our model predicts bilingual word associations better than monolingual baselines, and translation is the main mechanism explaining its success, while collocational and phonological associations do not improve the model.