Development and application of a high throughput natural language processing architecture to convert all clinical documents in a clinical data warehouse into standardized medical vocabularies
Majid Afshar,Dmitriy Dligach,Brihat Sharma,Xiaoyuan Cai,Jason Boyda,Steven Birch,Daniel Valdez,Suzan Zelisko,Cara Joyce,François Modave,Ron Price +10 more
TLDR
A health system's high throughput NLP architecture may serve as a benchmark for large-scale clinical research using a CUI-based approach and present a predictive model use case for predicting 30-day hospital readmission.About:
This article is published in Journal of the American Medical Informatics Association.The article was published on 2019-11-01 and is currently open access. It has received 18 citations till now. The article focuses on the topics: Data architecture & Unified Medical Language System.read more
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Desiderata for delivering NLP to accelerate healthcare AI advancement and a Mayo Clinic NLP-as-a-service implementation.
Andrew Wen,Sunyang Fu,Sungrim Moon,Mohamed El Wazir,Andrew N. Rosenbaum,Vinod C. Kaggal,Sijia Liu,Sunghwan Sohn,Hongfang Liu,Jungwei Fan +9 more
TL;DR: Several desiderata pertaining to development and usage of NLP systems, derived from two decades of experience implementing clinical NLP at the Mayo Clinic, are shared to inform the healthcare AI community.
Journal ArticleDOI
Natural language processing algorithms for mapping clinical text fragments onto ontology concepts: a systematic review and recommendations for future studies
TL;DR: A list of sixteen recommendations regarding the usage of NLP systems and algorithms, usage of data, evaluation and validation, presentation of results, and generalizability of results was developed and believe will increase the reproducibility and reusability of future studies and NLP algorithms in medicine.
Journal ArticleDOI
Publicly available machine learning models for identifying opioid misuse from the clinical notes of hospitalized patients
Brihat Sharma,Dmitriy Dligach,Kristin Swope,Elizabeth Salisbury-Afshar,Niranjan S. Karnik,Cara Joyce,Majid Afshar,Majid Afshar +7 more
TL;DR: Good test characteristics for an opioid misuse computable phenotype that is void of any PHI and performs similarly to models that use PHI are demonstrated.
Journal ArticleDOI
Validation of an alcohol misuse classifier in hospitalized patients
TL;DR: An approach using the clinical notes with NLP and supervised machine learning may better identify alcohol misuse cases than conventional methods solely relying on billing diagnostic codes.
Journal ArticleDOI
Use of unstructured text in prognostic clinical prediction models: a systematic review
Tom Seinen,Egill A. Fridgeirsson,Solomon Ioannou,Daniel Jeannetot,Luis H. John,Jan A. Kors,Aniek F. Markus,Vijaya K. Pera,Alexandros Rekkas,S.R. Williams,C. Yang,Elise van Mulligen,Peter R. Rijnbeek +12 more
TL;DR: The use of unstructured clinical text data in the development of prognostic prediction models has been found beneficial in addition to structured data in most studies, and a future focus on explainability and external validation of the developed models is suggested.
References
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Journal Article
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Posted Content
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Journal ArticleDOI
Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications
Guergana Savova,James J. Masanz,Philip V. Ogren,Jiaping Zheng,Sunghwan Sohn,Karin C Kipper-Schuler,Christopher G. Chute +6 more
TL;DR: The cTAKES annotations are the foundation for methods and modules for higher-level semantic processing of clinical free-text, and its components, specifically trained for the clinical domain, create rich linguistic and semantic annotations.
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Extracting information from textual documents in the electronic health record: a review of recent research.
TL;DR: Performance of information extraction systems with clinical text has improved since the last systematic review in 1995, but they are still rarely applied outside of the laboratory they have been developed in.
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Extracting information from the text of electronic medical records to improve case detection: a systematic review.
TL;DR: Text in EMRs is accessible, especially with open source information extraction algorithms, and significantly improves case detection when combined with codes, and more harmonization of reporting within EMR studies is needed, particularly standardized reporting of algorithm accuracy metrics.