Medical Profile Model: Scientific and Practical Applications in Healthcare.

The paper researches the problem of representation learning for electronic health records. We present the patient histories as temporal sequences of diseases for which embeddings are learned in an unsupervised setup with a transformer-based neural network model. Additionally the embedding space includes demographic parameters which allow the creation of generalized patient profiles and successful transfer of medical knowledge to other domains. The training of such a medical profile model has been performed on a dataset of more than one million patients. Detailed model analysis and its comparison with the state-of-the-art method show its clear advantage in the diagnosis prediction task. Further, we show two applications based on the developed profile model. First, a novel Harbinger Disease Discovery method allowing to reveal disease associated hypotheses and potentially are beneficial in the design of epidemiological studies. Second, the patient embeddings extracted from the profile model applied to the insurance scoring task allow significant improvement in the performance metrics.

Z-flipon variants reveal the many roles of Z-DNA and Z-RNA in health and disease.

Identifying roles for Z-DNA remains challenging given their dynamic nature. Here, we perform genome-wide interrogation with the DNABERT transformer algorithm trained on experimentally identified Z-DNA forming sequences (Z-flipons). The algorithm yields large performance enhancements (F1 = 0.83) over existing approaches and implements computational mutagenesis to assess the effects of base substitution on Z-DNA formation. We show Z-flipons are enriched in promoters and telomeres, overlapping quantitative trait loci for RNA expression, RNA editing, splicing, and disease-associated variants. We cross-validate across a number of orthogonal databases and define BZ junction motifs. Surprisingly, many effects we delineate are likely mediated through Z-RNA formation. A shared Z-RNA motif is identified in SCARF2, SMAD1, and CACNA1 transcripts, whereas other motifs are present in noncoding RNAs. We provide evidence for a Z-RNA fold that promotes adaptive immunity through alternative splicing of KRAB domain zinc finger proteins. An analysis of OMIM and presumptive gnomAD loss-of-function datasets reveals an overlap of Z-flipons with disease-causing variants in 8.6% and 2.9% of Mendelian disease genes, respectively, greatly extending the range of phenotypes mapped to Z-flipons.

Project Achoo: A Practical Model and Application for COVID-19 Detection From Recordings of Breath, Voice, and Cough.

The COVID-19 pandemic created significant interest and demand for infection detection and monitoring solutions. In this paper, we propose a machine learning method to quickly detect COVID-19 using audio recordings made on consumer devices. The approach combines signal processing and noise removal methods with an ensemble of fine-tuned deep learning networks and enables COVID detection on coughs. We have also developed and deployed a mobile application that uses a symptoms checker together with voice, breath, and cough signals to detect COVID-19 infection. The application showed robust performance on both openly sourced datasets and the noisy data collected during beta testing by the end users.