Identifying clinical phenotypes of frontotemporal dementia in post-9/11 era veterans using natural language processing
IntroductionFrontotemporal dementia (FTD) encompasses a clinically and pathologically diverse group of neurodegenerative disorders, yet little work has quantified the unique phenotypic clinical presentations of FTD among post-9/11 era veterans. To identify phenotypes of FTD using natural language pr...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-02-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2024.1270688/full |
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| author | Samin Panahi Samin Panahi Jamie Mayo Jamie Mayo Eamonn Kennedy Eamonn Kennedy Lee Christensen Lee Christensen Sreekanth Kamineni Sreekanth Kamineni Hari Krishna Raju Sagiraju Tyler Cooper Tyler Cooper David F. Tate David F. Tate Randall Rupper Mary Jo Pugh Mary Jo Pugh |
| author_facet | Samin Panahi Samin Panahi Jamie Mayo Jamie Mayo Eamonn Kennedy Eamonn Kennedy Lee Christensen Lee Christensen Sreekanth Kamineni Sreekanth Kamineni Hari Krishna Raju Sagiraju Tyler Cooper Tyler Cooper David F. Tate David F. Tate Randall Rupper Mary Jo Pugh Mary Jo Pugh |
| author_sort | Samin Panahi |
| collection | DOAJ |
| description | IntroductionFrontotemporal dementia (FTD) encompasses a clinically and pathologically diverse group of neurodegenerative disorders, yet little work has quantified the unique phenotypic clinical presentations of FTD among post-9/11 era veterans. To identify phenotypes of FTD using natural language processing (NLP) aided medical chart reviews of post-9/11 era U.S. military Veterans diagnosed with FTD in Veterans Health Administration care.MethodsA medical record chart review of clinician/provider notes was conducted using a Natural Language Processing (NLP) tool, which extracted features related to cognitive dysfunction. NLP features were further organized into seven Research Domain Criteria Initiative (RDoC) domains, which were clustered to identify distinct phenotypes.ResultsVeterans with FTD were more likely to have notes that reflected the RDoC domains, with cognitive and positive valence domains showing the greatest difference across groups. Clustering of domains identified three symptom phenotypes agnostic to time of an individual having FTD, categorized as Low (16.4%), Moderate (69.2%), and High (14.5%) distress. Comparison across distress groups showed significant differences in physical and psychological characteristics, particularly prior history of head injury, insomnia, cardiac issues, anxiety, and alcohol misuse. The clustering result within the FTD group demonstrated a phenotype variant that exhibited a combination of language and behavioral symptoms. This phenotype presented with manifestations indicative of both language-related impairments and behavioral changes, showcasing the coexistence of features from both domains within the same individual.DiscussionThis study suggests FTD also presents across a continuum of severity and symptom distress, both within and across variants. The intensity of distress evident in clinical notes tends to cluster with more co-occurring conditions. This examination of phenotypic heterogeneity in clinical notes indicates that sensitivity to FTD diagnosis may be correlated to overall symptom distress, and future work incorporating NLP and phenotyping may help promote strategies for early detection of FTD. |
| first_indexed | 2024-03-08T00:49:52Z |
| format | Article |
| id | doaj.art-49de94e3239a42c4a9ea740a4fbb4668 |
| institution | Directory Open Access Journal |
| issn | 1664-2295 |
| language | English |
| last_indexed | 2024-03-08T00:49:52Z |
| publishDate | 2024-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj.art-49de94e3239a42c4a9ea740a4fbb46682024-02-15T05:11:52ZengFrontiers Media S.A.Frontiers in Neurology1664-22952024-02-011510.3389/fneur.2024.12706881270688Identifying clinical phenotypes of frontotemporal dementia in post-9/11 era veterans using natural language processingSamin Panahi0Samin Panahi1Jamie Mayo2Jamie Mayo3Eamonn Kennedy4Eamonn Kennedy5Lee Christensen6Lee Christensen7Sreekanth Kamineni8Sreekanth Kamineni9Hari Krishna Raju Sagiraju10Tyler Cooper11Tyler Cooper12David F. Tate13David F. Tate14Randall Rupper15Mary Jo Pugh16Mary Jo Pugh17VA Salt Lake City Health Care System, Informatics, Decision-Enhancement and Analytic Sciences Center, Salt Lake City, UT, United StatesDivision of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, United StatesVA Salt Lake City Health Care System, Informatics, Decision-Enhancement and Analytic Sciences Center, Salt Lake City, UT, United StatesDivision of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, United StatesVA Salt Lake City Health Care System, Informatics, Decision-Enhancement and Analytic Sciences Center, Salt Lake City, UT, United StatesDivision of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, United StatesVA Salt Lake City Health Care System, Informatics, Decision-Enhancement and Analytic Sciences Center, Salt Lake City, UT, United StatesDivision of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, United StatesVA Salt Lake City Health Care System, Informatics, Decision-Enhancement and Analytic Sciences Center, Salt Lake City, UT, United StatesDivision of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, United StatesPreventive Oncology, All India Institute of Medical Sciences, New Delhi, IndiaVA Salt Lake City Health Care System, Informatics, Decision-Enhancement and Analytic Sciences Center, Salt Lake City, UT, United StatesDivision of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, United StatesVA Salt Lake City Health Care System, Informatics, Decision-Enhancement and Analytic Sciences Center, Salt Lake City, UT, United StatesDivision of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, United StatesVA Salt Lake City Health Care System, Geriatric Research, Education and Clinical Center, Salt Lake City, UT, United StatesVA Salt Lake City Health Care System, Informatics, Decision-Enhancement and Analytic Sciences Center, Salt Lake City, UT, United StatesDivision of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, United StatesIntroductionFrontotemporal dementia (FTD) encompasses a clinically and pathologically diverse group of neurodegenerative disorders, yet little work has quantified the unique phenotypic clinical presentations of FTD among post-9/11 era veterans. To identify phenotypes of FTD using natural language processing (NLP) aided medical chart reviews of post-9/11 era U.S. military Veterans diagnosed with FTD in Veterans Health Administration care.MethodsA medical record chart review of clinician/provider notes was conducted using a Natural Language Processing (NLP) tool, which extracted features related to cognitive dysfunction. NLP features were further organized into seven Research Domain Criteria Initiative (RDoC) domains, which were clustered to identify distinct phenotypes.ResultsVeterans with FTD were more likely to have notes that reflected the RDoC domains, with cognitive and positive valence domains showing the greatest difference across groups. Clustering of domains identified three symptom phenotypes agnostic to time of an individual having FTD, categorized as Low (16.4%), Moderate (69.2%), and High (14.5%) distress. Comparison across distress groups showed significant differences in physical and psychological characteristics, particularly prior history of head injury, insomnia, cardiac issues, anxiety, and alcohol misuse. The clustering result within the FTD group demonstrated a phenotype variant that exhibited a combination of language and behavioral symptoms. This phenotype presented with manifestations indicative of both language-related impairments and behavioral changes, showcasing the coexistence of features from both domains within the same individual.DiscussionThis study suggests FTD also presents across a continuum of severity and symptom distress, both within and across variants. The intensity of distress evident in clinical notes tends to cluster with more co-occurring conditions. This examination of phenotypic heterogeneity in clinical notes indicates that sensitivity to FTD diagnosis may be correlated to overall symptom distress, and future work incorporating NLP and phenotyping may help promote strategies for early detection of FTD.https://www.frontiersin.org/articles/10.3389/fneur.2024.1270688/fullmilitary healthfrontotemporal dementiaphenotypingveteransnatural language processingtraumatic brain injury |
| spellingShingle | Samin Panahi Samin Panahi Jamie Mayo Jamie Mayo Eamonn Kennedy Eamonn Kennedy Lee Christensen Lee Christensen Sreekanth Kamineni Sreekanth Kamineni Hari Krishna Raju Sagiraju Tyler Cooper Tyler Cooper David F. Tate David F. Tate Randall Rupper Mary Jo Pugh Mary Jo Pugh Identifying clinical phenotypes of frontotemporal dementia in post-9/11 era veterans using natural language processing Frontiers in Neurology military health frontotemporal dementia phenotyping veterans natural language processing traumatic brain injury |
| title | Identifying clinical phenotypes of frontotemporal dementia in post-9/11 era veterans using natural language processing |
| title_full | Identifying clinical phenotypes of frontotemporal dementia in post-9/11 era veterans using natural language processing |
| title_fullStr | Identifying clinical phenotypes of frontotemporal dementia in post-9/11 era veterans using natural language processing |
| title_full_unstemmed | Identifying clinical phenotypes of frontotemporal dementia in post-9/11 era veterans using natural language processing |
| title_short | Identifying clinical phenotypes of frontotemporal dementia in post-9/11 era veterans using natural language processing |
| title_sort | identifying clinical phenotypes of frontotemporal dementia in post 9 11 era veterans using natural language processing |
| topic | military health frontotemporal dementia phenotyping veterans natural language processing traumatic brain injury |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2024.1270688/full |
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