Abstract: Single cell ribonucleic acid sequencing data has provided numerous avenues to monitor and study organism more thoroughly at a cellular level, including spatial arrangement of cells. An approach to predicting cellular immune response based on cellular spatial features may be presented herein. The approach may include utilizing ribonucleic acid sequence data for a single cell (“scRNA-seq”) or cell from a tissue. The approach may also include extracting spatial features of the single cell using the scRNA-seq data including cell-to-cell interactions and relative distance between cells. The approach may include predicting an immune response of a cell or cells based on the extracted spatial features.

Abstract: This disclosure relates to a method, a system and a computer program product for synthesizing risk prediction models to generate a generalized risk prediction model for a particular disease. The method comprises retrieving a plurality of literatures from one or more databases. Each of the plurality of literatures defines a risk prediction model for a same disease. The method further comprises extracting study features from each of the plurality of literatures. The method further comprises extracting weights of risk factors in the risk prediction model defined by each of the plurality of literatures from the plurality of literatures. The method further comprises calculating adjusted weights of risk factors based on the extracted study features and the extracted weights of risk factors, to form an adjusted risk prediction model.

Abstract: A model learning and sample value generating framework includes a system and method to comprehensively integrate encoding, decoding and value predicting, and optimizing functions to reconstruct as accurate as possible an original input sample data space. The system leverages a variational autoencoder model to generate as realistic samples of that data space as possible. The system learns a value prediction function to achieve a target outcome based on the latent feature data instead of the original input data. Further, the system solves the optimization problem in the latent space without constraints to avoid the difficulty in optimizing in the original sample data space. The generated optimal samples are as similar as possible to the real-world input samples. The system provides a flexible data generation mechanism which is suitable for various kinds of target outcome specifications.

Abstract: In an approach for identifying disease progression hazard ratios for given disease against diseases from an EHR database to determine top comorbidities to the given disease, a processor receives raw EHR data. A processor identifies, from the raw EHR data, a set of diseases and associated diagnosis information for each disease of the set of diseases. A processor calculates a hazard ratio for each disease pair of a set of disease pairs producing a set of hazard ratios, wherein the set of disease pairs comprises a given disease paired with each disease of the set of diseases. A processor ranks the set of hazard ratios for the given disease. A processor selects a pre-defined number of top comorbidities of the set of hazard ratios for the given disease based on the ranking. A processor outputs the pre-defined number of top comorbidities for the given disease.