Automated Identification of Patients' Unmet Social Needs in Clinical Text Using Natural Language Processing.

Objective: To develop natural language processing (NLP) solutions for identifying patients' unmet social needs to enable timely intervention.

Patients And Methods: Design: A retrospective cohort study with review and annotation of clinical notes to identify unmet social needs, followed by using the annotations to develop and evaluate NLP solutions.

Participants: A total of 1103 primary care patients seen at a large academic medical center from June 1, 2019, to May 31, 2021 and referred to a community health worker (CHW) program.

MAP-RSeq: Mayo Analysis Pipeline for RNA sequencing.

Background: Although the costs of next generation sequencing technology have decreased over the past years, there is still a lack of simple-to-use applications, for a comprehensive analysis of RNA sequencing data. There is no one-stop shop for transcriptomic genomics. We have developed MAP-RSeq, a comprehensive computational workflow that can be used for obtaining genomic features from transcriptomic sequencing data, for any genome.