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      Development of a predictive model for retention in HIV care using natural language processing of clinical notes

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          Abstract

          Objective

          Adherence to a treatment plan from HIV-positive patients is necessary to decrease their mortality and improve their quality of life, however some patients display poor appointment adherence and become lost to follow-up (LTFU). We applied natural language processing (NLP) to analyze indications towards or against LTFU in HIV-positive patients’ notes.

          Materials and Methods

          Unstructured lemmatized notes were labeled with an LTFU or Retained status using a 183-day threshold. An NLP and supervised machine learning system with a linear model and elastic net regularization was trained to predict this status. Prevalence of characteristics domains in the learned model weights were evaluated.

          Results

          We analyzed 838 LTFU vs 2964 Retained notes and obtained a weighted F1 mean of 0.912 via nested cross-validation; another experiment with notes from the same patients in both classes showed substantially lower metrics. “Comorbidities” were associated with LTFU through, for instance, “HCV” (hepatitis C virus) and likewise “Good adherence” with Retained, represented with “Well on ART” (antiretroviral therapy).

          Discussion

          Mentions of mental health disorders and substance use were associated with disparate retention outcomes, however history vs active use was not investigated. There remains further need to model transitions between LTFU and being retained in care over time.

          Conclusion

          We provided an important step for the future development of a model that could eventually help to identify patients who are at risk for falling out of care and to analyze which characteristics could be factors for this. Further research is needed to enhance this method with structured electronic medical record fields.

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          Most cited references31

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          PhysioBank, PhysioToolkit, and PhysioNet

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            Scikit-learn: Machine learning in Python

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              Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications.

              We aim to build and evaluate an open-source natural language processing system for information extraction from electronic medical record clinical free-text. We describe and evaluate our system, the clinical Text Analysis and Knowledge Extraction System (cTAKES), released open-source at http://www.ohnlp.org. The cTAKES builds on existing open-source technologies-the Unstructured Information Management Architecture framework and OpenNLP natural language processing toolkit. Its components, specifically trained for the clinical domain, create rich linguistic and semantic annotations. Performance of individual components: sentence boundary detector accuracy=0.949; tokenizer accuracy=0.949; part-of-speech tagger accuracy=0.936; shallow parser F-score=0.924; named entity recognizer and system-level evaluation F-score=0.715 for exact and 0.824 for overlapping spans, and accuracy for concept mapping, negation, and status attributes for exact and overlapping spans of 0.957, 0.943, 0.859, and 0.580, 0.939, and 0.839, respectively. Overall performance is discussed against five applications. The cTAKES annotations are the foundation for methods and modules for higher-level semantic processing of clinical free-text.
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                Author and article information

                Journal
                Journal of the American Medical Informatics Association
                Oxford University Press (OUP)
                1527-974X
                January 2021
                January 15 2021
                November 05 2020
                January 2021
                January 15 2021
                November 05 2020
                : 28
                : 1
                : 104-112
                Affiliations
                [1 ]Center for Research Informatics, University of Chicago, Chicago, Illinois, USA
                [2 ]Department of Medicine, University of Chicago, Chicago, Illinois, USA
                [3 ]Chicago Center for HIV Elimination, University of Chicago, Chicago, Illinois, USA
                Article
                10.1093/jamia/ocaa220
                33150369
                b02f2759-4317-44a2-bb53-a2b70e98bb60
                © 2020

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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