Using Hypothesis-Led Machine Learning and Hierarchical Cluster Analysis to Identify Disease Pathways Prior to Dementia: Longitudinal Cohort Study
Dementia development is a complex process in which the occurrence and sequential relationships of different diseases or conditions may construct specific patterns leading to incident dementia. This study aimed to identify patterns of disease or symptom clusters and their sequences prior to incident...
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| Published in | Journal of Medical Internet Research Vol. 25; no. 1; p. e41858 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Canada
JMIR Publications Inc
26.07.2023
Gunther Eysenbach MD MPH, Associate Professor JMIR Publications |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Dementia development is a complex process in which the occurrence and sequential relationships of different diseases or conditions may construct specific patterns leading to incident dementia.
This study aimed to identify patterns of disease or symptom clusters and their sequences prior to incident dementia using a novel approach incorporating machine learning methods.
Using Taiwan's National Health Insurance Research Database, data from 15,700 older people with dementia and 15,700 nondementia controls matched on age, sex, and index year (n=10,466, 67% for the training data set and n=5234, 33% for the testing data set) were retrieved for analysis. Using machine learning methods to capture specific hierarchical disease triplet clusters prior to dementia, we designed a study algorithm with four steps: (1) data preprocessing, (2) disease or symptom pathway selection, (3) model construction and optimization, and (4) data visualization.
Among 15,700 identified older people with dementia, 10,466 and 5234 subjects were randomly assigned to the training and testing data sets, and 6215 hierarchical disease triplet clusters with positive correlations with dementia onset were identified. We subsequently generated 19,438 features to construct prediction models, and the model with the best performance was support vector machine (SVM) with the by-group LASSO (least absolute shrinkage and selection operator) regression method (total corresponding features=2513; accuracy=0.615; sensitivity=0.607; specificity=0.622; positive predictive value=0.612; negative predictive value=0.619; area under the curve=0.639). In total, this study captured 49 hierarchical disease triplet clusters related to dementia development, and the most characteristic patterns leading to incident dementia started with cardiovascular conditions (mainly hypertension), cerebrovascular disease, mobility disorders, or infections, followed by neuropsychiatric conditions.
Dementia development in the real world is an intricate process involving various diseases or conditions, their co-occurrence, and sequential relationships. Using a machine learning approach, we identified 49 hierarchical disease triplet clusters with leading roles (cardio- or cerebrovascular disease) and supporting roles (mental conditions, locomotion difficulties, infections, and nonspecific neurological conditions) in dementia development. Further studies using data from other countries are needed to validate the prediction algorithms for dementia development, allowing the development of comprehensive strategies to prevent or care for dementia in the real world. |
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| AbstractList | Dementia development is a complex process in which the occurrence and sequential relationships of different diseases or conditions may construct specific patterns leading to incident dementia.BACKGROUNDDementia development is a complex process in which the occurrence and sequential relationships of different diseases or conditions may construct specific patterns leading to incident dementia.This study aimed to identify patterns of disease or symptom clusters and their sequences prior to incident dementia using a novel approach incorporating machine learning methods.OBJECTIVEThis study aimed to identify patterns of disease or symptom clusters and their sequences prior to incident dementia using a novel approach incorporating machine learning methods.Using Taiwan's National Health Insurance Research Database, data from 15,700 older people with dementia and 15,700 nondementia controls matched on age, sex, and index year (n=10,466, 67% for the training data set and n=5234, 33% for the testing data set) were retrieved for analysis. Using machine learning methods to capture specific hierarchical disease triplet clusters prior to dementia, we designed a study algorithm with four steps: (1) data preprocessing, (2) disease or symptom pathway selection, (3) model construction and optimization, and (4) data visualization.METHODSUsing Taiwan's National Health Insurance Research Database, data from 15,700 older people with dementia and 15,700 nondementia controls matched on age, sex, and index year (n=10,466, 67% for the training data set and n=5234, 33% for the testing data set) were retrieved for analysis. Using machine learning methods to capture specific hierarchical disease triplet clusters prior to dementia, we designed a study algorithm with four steps: (1) data preprocessing, (2) disease or symptom pathway selection, (3) model construction and optimization, and (4) data visualization.Among 15,700 identified older people with dementia, 10,466 and 5234 subjects were randomly assigned to the training and testing data sets, and 6215 hierarchical disease triplet clusters with positive correlations with dementia onset were identified. We subsequently generated 19,438 features to construct prediction models, and the model with the best performance was support vector machine (SVM) with the by-group LASSO (least absolute shrinkage and selection operator) regression method (total corresponding features=2513; accuracy=0.615; sensitivity=0.607; specificity=0.622; positive predictive value=0.612; negative predictive value=0.619; area under the curve=0.639). In total, this study captured 49 hierarchical disease triplet clusters related to dementia development, and the most characteristic patterns leading to incident dementia started with cardiovascular conditions (mainly hypertension), cerebrovascular disease, mobility disorders, or infections, followed by neuropsychiatric conditions.RESULTSAmong 15,700 identified older people with dementia, 10,466 and 5234 subjects were randomly assigned to the training and testing data sets, and 6215 hierarchical disease triplet clusters with positive correlations with dementia onset were identified. We subsequently generated 19,438 features to construct prediction models, and the model with the best performance was support vector machine (SVM) with the by-group LASSO (least absolute shrinkage and selection operator) regression method (total corresponding features=2513; accuracy=0.615; sensitivity=0.607; specificity=0.622; positive predictive value=0.612; negative predictive value=0.619; area under the curve=0.639). In total, this study captured 49 hierarchical disease triplet clusters related to dementia development, and the most characteristic patterns leading to incident dementia started with cardiovascular conditions (mainly hypertension), cerebrovascular disease, mobility disorders, or infections, followed by neuropsychiatric conditions.Dementia development in the real world is an intricate process involving various diseases or conditions, their co-occurrence, and sequential relationships. Using a machine learning approach, we identified 49 hierarchical disease triplet clusters with leading roles (cardio- or cerebrovascular disease) and supporting roles (mental conditions, locomotion difficulties, infections, and nonspecific neurological conditions) in dementia development. Further studies using data from other countries are needed to validate the prediction algorithms for dementia development, allowing the development of comprehensive strategies to prevent or care for dementia in the real world.CONCLUSIONSDementia development in the real world is an intricate process involving various diseases or conditions, their co-occurrence, and sequential relationships. Using a machine learning approach, we identified 49 hierarchical disease triplet clusters with leading roles (cardio- or cerebrovascular disease) and supporting roles (mental conditions, locomotion difficulties, infections, and nonspecific neurological conditions) in dementia development. Further studies using data from other countries are needed to validate the prediction algorithms for dementia development, allowing the development of comprehensive strategies to prevent or care for dementia in the real world. Dementia development is a complex process in which the occurrence and sequential relationships of different diseases or conditions may construct specific patterns leading to incident dementia. This study aimed to identify patterns of disease or symptom clusters and their sequences prior to incident dementia using a novel approach incorporating machine learning methods. Using Taiwan's National Health Insurance Research Database, data from 15,700 older people with dementia and 15,700 nondementia controls matched on age, sex, and index year (n=10,466, 67% for the training data set and n=5234, 33% for the testing data set) were retrieved for analysis. Using machine learning methods to capture specific hierarchical disease triplet clusters prior to dementia, we designed a study algorithm with four steps: (1) data preprocessing, (2) disease or symptom pathway selection, (3) model construction and optimization, and (4) data visualization. Among 15,700 identified older people with dementia, 10,466 and 5234 subjects were randomly assigned to the training and testing data sets, and 6215 hierarchical disease triplet clusters with positive correlations with dementia onset were identified. We subsequently generated 19,438 features to construct prediction models, and the model with the best performance was support vector machine (SVM) with the by-group LASSO (least absolute shrinkage and selection operator) regression method (total corresponding features=2513; accuracy=0.615; sensitivity=0.607; specificity=0.622; positive predictive value=0.612; negative predictive value=0.619; area under the curve=0.639). In total, this study captured 49 hierarchical disease triplet clusters related to dementia development, and the most characteristic patterns leading to incident dementia started with cardiovascular conditions (mainly hypertension), cerebrovascular disease, mobility disorders, or infections, followed by neuropsychiatric conditions. Dementia development in the real world is an intricate process involving various diseases or conditions, their co-occurrence, and sequential relationships. Using a machine learning approach, we identified 49 hierarchical disease triplet clusters with leading roles (cardio- or cerebrovascular disease) and supporting roles (mental conditions, locomotion difficulties, infections, and nonspecific neurological conditions) in dementia development. Further studies using data from other countries are needed to validate the prediction algorithms for dementia development, allowing the development of comprehensive strategies to prevent or care for dementia in the real world. BackgroundDementia development is a complex process in which the occurrence and sequential relationships of different diseases or conditions may construct specific patterns leading to incident dementia. ObjectiveThis study aimed to identify patterns of disease or symptom clusters and their sequences prior to incident dementia using a novel approach incorporating machine learning methods. MethodsUsing Taiwan’s National Health Insurance Research Database, data from 15,700 older people with dementia and 15,700 nondementia controls matched on age, sex, and index year (n=10,466, 67% for the training data set and n=5234, 33% for the testing data set) were retrieved for analysis. Using machine learning methods to capture specific hierarchical disease triplet clusters prior to dementia, we designed a study algorithm with four steps: (1) data preprocessing, (2) disease or symptom pathway selection, (3) model construction and optimization, and (4) data visualization. ResultsAmong 15,700 identified older people with dementia, 10,466 and 5234 subjects were randomly assigned to the training and testing data sets, and 6215 hierarchical disease triplet clusters with positive correlations with dementia onset were identified. We subsequently generated 19,438 features to construct prediction models, and the model with the best performance was support vector machine (SVM) with the by-group LASSO (least absolute shrinkage and selection operator) regression method (total corresponding features=2513; accuracy=0.615; sensitivity=0.607; specificity=0.622; positive predictive value=0.612; negative predictive value=0.619; area under the curve=0.639). In total, this study captured 49 hierarchical disease triplet clusters related to dementia development, and the most characteristic patterns leading to incident dementia started with cardiovascular conditions (mainly hypertension), cerebrovascular disease, mobility disorders, or infections, followed by neuropsychiatric conditions. ConclusionsDementia development in the real world is an intricate process involving various diseases or conditions, their co-occurrence, and sequential relationships. Using a machine learning approach, we identified 49 hierarchical disease triplet clusters with leading roles (cardio- or cerebrovascular disease) and supporting roles (mental conditions, locomotion difficulties, infections, and nonspecific neurological conditions) in dementia development. Further studies using data from other countries are needed to validate the prediction algorithms for dementia development, allowing the development of comprehensive strategies to prevent or care for dementia in the real world. Background:Dementia development is a complex process in which the occurrence and sequential relationships of different diseases or conditions may construct specific patterns leading to incident dementia.Objective:This study aimed to identify patterns of disease or symptom clusters and their sequences prior to incident dementia using a novel approach incorporating machine learning methods.Methods:Using Taiwan’s National Health Insurance Research Database, data from 15,700 older people with dementia and 15,700 nondementia controls matched on age, sex, and index year (n=10,466, 67% for the training data set and n=5234, 33% for the testing data set) were retrieved for analysis. Using machine learning methods to capture specific hierarchical disease triplet clusters prior to dementia, we designed a study algorithm with four steps: (1) data preprocessing, (2) disease or symptom pathway selection, (3) model construction and optimization, and (4) data visualization.Results:Among 15,700 identified older people with dementia, 10,466 and 5234 subjects were randomly assigned to the training and testing data sets, and 6215 hierarchical disease triplet clusters with positive correlations with dementia onset were identified. We subsequently generated 19,438 features to construct prediction models, and the model with the best performance was support vector machine (SVM) with the by-group LASSO (least absolute shrinkage and selection operator) regression method (total corresponding features=2513; accuracy=0.615; sensitivity=0.607; specificity=0.622; positive predictive value=0.612; negative predictive value=0.619; area under the curve=0.639). In total, this study captured 49 hierarchical disease triplet clusters related to dementia development, and the most characteristic patterns leading to incident dementia started with cardiovascular conditions (mainly hypertension), cerebrovascular disease, mobility disorders, or infections, followed by neuropsychiatric conditions.Conclusions:Dementia development in the real world is an intricate process involving various diseases or conditions, their co-occurrence, and sequential relationships. Using a machine learning approach, we identified 49 hierarchical disease triplet clusters with leading roles (cardio- or cerebrovascular disease) and supporting roles (mental conditions, locomotion difficulties, infections, and nonspecific neurological conditions) in dementia development. Further studies using data from other countries are needed to validate the prediction algorithms for dementia development, allowing the development of comprehensive strategies to prevent or care for dementia in the real world. |
| Author | Pei-Jung Chen Li-Ning Peng Tsung-Hsien Tsai Fei-Yuan Hsiao Shih-Tsung Huang Liang-Kung Chen |
| AuthorAffiliation | 4 School of Pharmacy College of Medicine National Taiwan University Taipei Taiwan 2 Center for Healthy Longevity and Aging Sciences National Yang Ming Chiao Tung University Taipei Taiwan 5 Department of Pharmacy National Taiwan University Hospital Taipei Taiwan 7 Center for Geriatrics and Gerontology Taipei Veterans General Hospital Taipei Taiwan 6 Advanced Tech Business Unit Acer New Taipei City Taiwan 8 Taipei Municipal Gan-Dau Hospital (Managed by Taipei Veterans General Hospital) Taipei Taiwan 1 Department of Pharmacy National Yang Ming Chiao Tung University Taipei Taiwan 3 Graduate Institute of Clinical Pharmacy, College of Medicine National Taiwan University Taipei Taiwan |
| AuthorAffiliation_xml | – name: 1 Department of Pharmacy National Yang Ming Chiao Tung University Taipei Taiwan – name: 7 Center for Geriatrics and Gerontology Taipei Veterans General Hospital Taipei Taiwan – name: 8 Taipei Municipal Gan-Dau Hospital (Managed by Taipei Veterans General Hospital) Taipei Taiwan – name: 4 School of Pharmacy College of Medicine National Taiwan University Taipei Taiwan – name: 5 Department of Pharmacy National Taiwan University Hospital Taipei Taiwan – name: 3 Graduate Institute of Clinical Pharmacy, College of Medicine National Taiwan University Taipei Taiwan – name: 2 Center for Healthy Longevity and Aging Sciences National Yang Ming Chiao Tung University Taipei Taiwan – name: 6 Advanced Tech Business Unit Acer New Taipei City Taiwan |
| Author_xml | – sequence: 1 givenname: Shih-Tsung orcidid: 0000-0002-1738-5571 surname: Huang fullname: Huang, Shih-Tsung – sequence: 2 givenname: Fei-Yuan orcidid: 0000-0003-1660-9120 surname: Hsiao fullname: Hsiao, Fei-Yuan – sequence: 3 givenname: Tsung-Hsien orcidid: 0000-0002-8975-7151 surname: Tsai fullname: Tsai, Tsung-Hsien – sequence: 4 givenname: Pei-Jung orcidid: 0009-0006-3448-7592 surname: Chen fullname: Chen, Pei-Jung – sequence: 5 givenname: Li-Ning orcidid: 0000-0003-1308-9553 surname: Peng fullname: Peng, Li-Ning – sequence: 6 givenname: Liang-Kung orcidid: 0000-0002-2387-8508 surname: Chen fullname: Chen, Liang-Kung |
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| CitedBy_id | crossref_primary_10_3390_brainsci14070722 crossref_primary_10_1016_j_asoc_2025_112754 crossref_primary_10_1016_j_jbi_2025_104799 crossref_primary_10_1016_j_archger_2023_105258 crossref_primary_10_1016_j_aggp_2023_100001 crossref_primary_10_1016_j_jagp_2024_10_016 crossref_primary_10_1002_cnr2_2045 |
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| Copyright | Shih-Tsung Huang, Fei-Yuan Hsiao, Tsung-Hsien Tsai, Pei-Jung Chen, Li-Ning Peng, Liang-Kung Chen. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 26.07.2023. 2023. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Shih-Tsung Huang, Fei-Yuan Hsiao, Tsung-Hsien Tsai, Pei-Jung Chen, Li-Ning Peng, Liang-Kung Chen. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 26.07.2023. 2023 |
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| Keywords | development infection mobility disease data neuropsychiatric data set cardiovascular machine learning symptoms condition mental conditions dementia cluster analysis |
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| License | Shih-Tsung Huang, Fei-Yuan Hsiao, Tsung-Hsien Tsai, Pei-Jung Chen, Li-Ning Peng, Liang-Kung Chen. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 26.07.2023. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included. |
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| Snippet | Dementia development is a complex process in which the occurrence and sequential relationships of different diseases or conditions may construct specific... Background:Dementia development is a complex process in which the occurrence and sequential relationships of different diseases or conditions may construct... BackgroundDementia development is a complex process in which the occurrence and sequential relationships of different diseases or conditions may construct... |
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| SubjectTerms | Aged Algorithms Beneficiaries Cerebrovascular disease Cerebrovascular Disorders Classification Cluster Analysis Clustering Cohort analysis Cohort Studies Comorbidity Computer applications to medicine. Medical informatics Data visualization Datasets Dementia Disease prevention Humans Hypertension Hypotheses Infections Locomotion Longitudinal Studies Machine Learning Medical coding Mobility National health insurance Neurological disorders Older people Optimization Original Paper Population Prediction models Psychotropic drugs Public aspects of medicine R858-859.7 RA1-1270 Regression analysis Risk factors Sequences Support vector machines Visualization |
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| Title | Using Hypothesis-Led Machine Learning and Hierarchical Cluster Analysis to Identify Disease Pathways Prior to Dementia: Longitudinal Cohort Study |
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