• How can natural language proce...

Treffer: How can natural language processing help model informed drug development?: a review.

Title:
How can natural language processing help model informed drug development?: a review.
Authors:
Bhatnagar R; Data Science, Data Collaboration Center, Critical Path Institute, Tucson, Arizona, USA., Sardar S; Quantitative Medicine, Critical Path Institute, Tucson, Arizona, USA., Beheshti M; Quantitative Medicine, Critical Path Institute, Tucson, Arizona, USA., Podichetty JT; Quantitative Medicine, Critical Path Institute, Tucson, Arizona, USA.
Source:
JAMIA open [JAMIA Open] 2022 Jun 11; Vol. 5 (2), pp. ooac043. Date of Electronic Publication: 2022 Jun 11 (Print Publication: 2022).
Publication Type:
Journal Article; Review
Language:
English
Journal Info:
Publisher: Oxford University Press on behalf of the American Medical Informatics Association Country of Publication: United States NLM ID: 101730643 Publication Model: eCollection Cited Medium: Internet ISSN: 2574-2531 (Electronic) Linking ISSN: 25742531 NLM ISO Abbreviation: JAMIA Open Subsets: PubMed not MEDLINE
Imprint Name(s):
Original Publication: [Cary, NC] : Oxford University Press on behalf of the American Medical Informatics Association, [2018]-
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Contributed Indexing:
Keywords: NLP; deep learning; drug development; machine learning
Entry Date(s):
Date Created: 20220615 Latest Revision: 20220716
Update Code:
20250114
PubMed Central ID:
PMC9188322
DOI:
10.1093/jamiaopen/ooac043
PMID:
35702625
Database:
MEDLINE

Weitere Informationen

Objective: To summarize applications of natural language processing (NLP) in model informed drug development (MIDD) and identify potential areas of improvement.
Materials and Methods: Publications found on PubMed and Google Scholar, websites and GitHub repositories for NLP libraries and models. Publications describing applications of NLP in MIDD were reviewed. The applications were stratified into 3 stages: drug discovery, clinical trials, and pharmacovigilance. Key NLP functionalities used for these applications were assessed. Programming libraries and open-source resources for the implementation of NLP functionalities in MIDD were identified.
Results: NLP has been utilized to aid various processes in drug development lifecycle such as gene-disease mapping, biomarker discovery, patient-trial matching, adverse drug events detection, etc. These applications commonly use NLP functionalities of named entity recognition, word embeddings, entity resolution, assertion status detection, relation extraction, and topic modeling. The current state-of-the-art for implementing these functionalities in MIDD applications are transformer models that utilize transfer learning for enhanced performance. Various libraries in python, R, and Java like huggingface, sparkNLP, and KoRpus as well as open-source platforms such as DisGeNet, DeepEnroll, and Transmol have enabled convenient implementation of NLP models to MIDD applications.
Discussion: Challenges such as reproducibility, explainability, fairness, limited data, limited language-support, and security need to be overcome to ensure wider adoption of NLP in MIDD landscape. There are opportunities to improve the performance of existing models and expand the use of NLP in newer areas of MIDD.
Conclusions: This review provides an overview of the potential and pitfalls of current NLP approaches in MIDD.
(© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association.)