RESUMEN
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. Clinical notes and portal messages of 200 age and sex-stratified patients were sampled for annotation of unmet social needs. Systems: Two NLP solutions were developed and compared. The first solution employed similarity-based classification on top of sentences represented as semantic embedding vectors. The second solution involved designing of terms and patterns for identifying each domain of unmet social needs in the clinical text. Measures: Precision, recall, and f1-score of the NLP solutions. Results: A total of 5675 clinical notes and 475 portal messages were annotated, with an inter-annotator agreement of 0.938. The best NLP solution achieved an f1-score of 0.95 and was applied to the entire CHW-referred cohort (n=1103), of whom >80% had at least 1 unmet social need within the 6 months before the first CHW referral. Financial strain and health literacy were the top 2 domains of unmet social needs across most of the sex and age strata. Conclusion: Clinical text contains rich information about patients' unmet social needs. The NLP can achieve good performance in identifying those needs for CHW referral and facilitate data-driven research on social determinants of health.
RESUMEN
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. RESULTS: For optimization of tools and parameters, MAP-RSeq was validated using both simulated and real datasets. MAP-RSeq workflow consists of six major modules such as alignment of reads, quality assessment of reads, gene expression assessment and exon read counting, identification of expressed single nucleotide variants (SNVs), detection of fusion transcripts, summarization of transcriptomics data and final report. This workflow is available for Human transcriptome analysis and can be easily adapted and used for other genomes. Several clinical and research projects at the Mayo Clinic have applied the MAP-RSeq workflow for RNA-Seq studies. The results from MAP-RSeq have thus far enabled clinicians and researchers to understand the transcriptomic landscape of diseases for better diagnosis and treatment of patients. CONCLUSIONS: Our software provides gene counts, exon counts, fusion candidates, expressed single nucleotide variants, mapping statistics, visualizations, and a detailed research data report for RNA-Seq. The workflow can be executed on a standalone virtual machine or on a parallel Sun Grid Engine cluster. The software can be downloaded from http://bioinformaticstools.mayo.edu/research/maprseq/.
