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.

Motivation Viruses, the most abundant biological entities on earth, are important components of microbial communities, and as major human pathogens, they are responsible for human mortality and morbidity. The identification of viral sequences from metagenomes is critical for viral analysis. As massive quantities of short sequences are generated by next-generation sequencing (NGS), most methods utilize discrete and sparse one-hot vectors to encode nucleotide sequences, which are usually ineffective in viral identification. Results In this paper, Virtifier, a deep learning-based viral identifier for sequences from metagenomic data, is proposed. It includes a meaningful nucleotide sequence encoding method named Seq2Vec and a variant viral sequence predictor with an attention-based Long Short-Term Memory (LSTM) network. By utilizing a fully trained embedding matrix to encode codons, Seq2Vec can efficiently extract the relationships among those codons in a nucleotide sequence. Combined with an attention layer, the LSTM neural network can further analyze the codon relationships and sift the parts that contribute to the final features. Experimental results of three datasets have shown that Virtifier can accurately identify short viral sequences (< 500 bp) from metagenomes, surpassing three widely used methods, VirFinder, DeepVirFinder and PPR-Meta. Meanwhile, a comparable performance was achieved by Virtifier at longer lengths (> 5,000bp). Availability A Python implementation of Virtifier and the Python code developed for this study have been provided on Github https://github.com/crazyinter/Seq2Vec. Supplementary information Supplementary data are available at Bioinformatics online.

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Background Atrial fibrillation (AF) is common in intensive care unit (ICU) patients and is associated with poor outcomes. Different management strategies exist, but the evidence is limited and derived from non‐ICU patients. This international survey of ICU doctors evaluated the preferred management of acute AF in ICU patients. Method We conducted an international online survey of ICU doctors with 27 questions about the preferred management of acute AF in the ICU, including antiarrhythmic therapy in hemodynamically stable and unstable patients and use of anticoagulant therapy. Results A total of 910 respondents from 70 ICUs in 14 countries participated in the survey with 24%–100% of doctors from sites responding. Most ICUs (80%) did not have a local guideline for the management of acute AF. The preferred first‐line strategy for the management of hemodynamically stable patients with acute AF was observation (95% of respondents), rhythm control (3%), or rate control (2%). For hemodynamically unstable patients, the preferred strategy was observation (48%), rhythm control (48%), or rate control (4%). Overall, preferred antiarrhythmic interventions included amiodarone, direct current cardioversion, beta‐blockers other than sotalol, and magnesium in that order. A total of 67% preferred using anticoagulant therapy in ICU patients with AF, among whom 61% preferred therapeutic dose anticoagulants and 39% prophylactic dose anticoagulants. Conclusion This international survey indicated considerable practice variation among ICU doctors in the clinical management of acute AF, including the overall management strategies and the use of antiarrhythmic interventions and anticoagulants.

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