CTRL: Closed-Loop Transcription to an LDR via Minimaxing Rate Reduction.

This work proposes a new computational framework for learning a structured generative model for real-world datasets. In particular, we propose to learn between a multi-class, multi-dimensional data distribution and a () in the feature space that consists of multiple independent multi-dimensional linear subspaces. In particular, we argue that the optimal encoding and decoding mappings sought can be formulated as a for the learned representation.

COVID-19 outcomes in patients with cancer: Findings from the University of California health system database.

Background: The interaction between cancer diagnoses and COVID-19 infection and outcomes is unclear. We leveraged a state-wide, multi-institutional database to assess cancer-related risk factors for poor COVID-19 outcomes.

Methods: We conducted a retrospective cohort study using the University of California Health COVID Research Dataset, which includes electronic health data of patients tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at 17 California medical centers.

Dynamic modeling of hospitalized COVID-19 patients reveals disease state-dependent risk factors.

Objective: The study sought to investigate the disease state-dependent risk profiles of patient demographics and medical comorbidities associated with adverse outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections.

Materials And Methods: A covariate-dependent, continuous-time hidden Markov model with 4 states (moderate, severe, discharged, and deceased) was used to model the dynamic progression of COVID-19 during the course of hospitalization. All model parameters were estimated using the electronic health records of 1362 patients from ProMedica Health System admitted between March 20, 2020 and December 29, 2020 with a positive nasopharyngeal PCR test for SARS-CoV-2.