1
|
Liu P, Wang Z, Liu N, Peres MA. A scoping review of the clinical application of machine learning in data-driven population segmentation analysis. J Am Med Inform Assoc 2023; 30:1573-1582. [PMID: 37369006 PMCID: PMC10436153 DOI: 10.1093/jamia/ocad111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 06/08/2023] [Accepted: 06/16/2023] [Indexed: 06/29/2023] Open
Abstract
OBJECTIVE Data-driven population segmentation is commonly used in clinical settings to separate the heterogeneous population into multiple relatively homogenous groups with similar healthcare features. In recent years, machine learning (ML) based segmentation algorithms have garnered interest for their potential to speed up and improve algorithm development across many phenotypes and healthcare situations. This study evaluates ML-based segmentation with respect to (1) the populations applied, (2) the segmentation details, and (3) the outcome evaluations. MATERIALS AND METHODS MEDLINE, Embase, Web of Science, and Scopus were used following the PRISMA-ScR criteria. Peer-reviewed studies in the English language that used data-driven population segmentation analysis on structured data from January 2000 to October 2022 were included. RESULTS We identified 6077 articles and included 79 for the final analysis. Data-driven population segmentation analysis was employed in various clinical settings. K-means clustering is the most prevalent unsupervised ML paradigm. The most common settings were healthcare institutions. The most common targeted population was the general population. DISCUSSION Although all the studies did internal validation, only 11 papers (13.9%) did external validation, and 23 papers (29.1%) conducted methods comparison. The existing papers discussed little validating the robustness of ML modeling. CONCLUSION Existing ML applications on population segmentation need more evaluations regarding giving tailored, efficient integrated healthcare solutions compared to traditional segmentation analysis. Future ML applications in the field should emphasize methods' comparisons and external validation and investigate approaches to evaluate individual consistency using different methods.
Collapse
Affiliation(s)
- Pinyan Liu
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
| | - Ziwen Wang
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
| | - Nan Liu
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
- Institute of Data Science, National University of Singapore, Singapore, Singapore
| | - Marco Aurélio Peres
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
- National Dental Research Institute Singapore, National Dental Centre Singapore, Singapore, Singapore
| |
Collapse
|
2
|
Gonzalez-Martinez A, Pagán J, Sanz-García A, García-Azorín D, Rodriguez Vico JS, Jaimes A, Gómez García A, Díaz de Terán J, González-García N, Quintas S, Belascoaín R, Casas Limón J, Latorre G, Calle de Miguel C, Sierra Á, Guerrero-Peral ÁL, Trevino-Peinado C, Gago-Veiga AB. Machine-learning based approach to predict anti-CGRP response in patients with migraine: multicenter Spanish study. Eur J Neurol 2022; 29:3102-3111. [PMID: 35726393 DOI: 10.1111/ene.15458] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 06/13/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND To date, several variables have been associated with anti-CGRP receptor or ligand-antibody response with disparate results. Our objective is to determine whether machine learning (ML)-based models can predict 6, 9 and 12 months response to anti-CGRP receptor or ligand therapies among migraine patients. METHODS We performed a multicenter analysis of a prospectively collected data cohort of patients with migraine receiving anti-CGRP therapies. Demographic and clinical variables were collected. Response rate defined in the 30% to 50% range -or at least 30%-, in the 50% to 75% range -or at least 50%-, and response rate over 75% reduction in the number of headache days per month at 6, 9 and 12 months. A sequential forward feature selector was used for variable selection and ML-based predictive models response to anti-CGRP therapies at 6, 9 and 12 months, with models' accuracy not less than 70%, were generated. RESULTS A total of 712 patients were included, 93% women, aged 48 years (SD=11.7). Eighty-three percent had chronic migraine. ML models using headache days/month, migraine days/month and HIT-6 variables yielded predictions with a F1 score range of 0.70-0.97 and AUC (area under the receiver operating curve) score range of 0.87-0.98. SHAP (SHapley Additive exPlanations) summary plots and dependence plots were generated to evaluate the relevance of the factors associated with the prediction of the above-mentioned response rates. CONCLUSIONS According to our study, ML models can predict anti-CGRP response at 6, 9 and 12 months. This study provides a predictive tool to be used in a real-world setting.
Collapse
Affiliation(s)
- Alicia Gonzalez-Martinez
- Headache Unit, Neurology Department, Hospital Universitario de la Princesa & Instituto de Investigación Sanitaria La Princesa, Madrid, Spain
| | - Josué Pagán
- Universidad Politécnica de Madrid and Center for Computational Simulation of Universidad Politécnica de Madrid, Madrid, Spain
| | - Ancor Sanz-García
- Headache Unit, Neurology Department, Hospital Universitario de la Princesa & Instituto de Investigación Sanitaria La Princesa, Madrid, Spain
| | - David García-Azorín
- Headache Unit, Neurology Department, Department of Medicine, University of Valladolid, Hospital Clínico Universitario de Valladolid, Valladolid, Spain
| | | | - Alex Jaimes
- Headache Unit, Neurology Department, Fundación Jiménez Díaz, Madrid, Spain
| | | | - Javier Díaz de Terán
- Headache Unit, Neurology Department, Hospital Universitario La Paz, Madrid, Spain
| | - Nuria González-García
- Headache Unit, Neurology Department, Hospital Universitario Clínico San Carlos, Madrid, Spain
| | - Sonia Quintas
- Headache Unit, Neurology Department, Hospital Universitario de la Princesa & Instituto de Investigación Sanitaria La Princesa, Madrid, Spain
| | - Rocio Belascoaín
- Headache Unit, Neurology Department, Hospital Universitario de la Princesa & Instituto de Investigación Sanitaria La Princesa, Madrid, Spain
| | - Javier Casas Limón
- Headache Unit Neurology Department, Hospital Universitario Fundación de Alcorcón, Alcorcón, Spain
| | - Germán Latorre
- Headache Unit, Neurology Department, Hospital Universitario de Fuenlabrada, Madrid, Spain
| | - Carlos Calle de Miguel
- Headache Unit, Neurology Department, Hospital Universitario de Fuenlabrada, Madrid, Spain
| | - Álvaro Sierra
- Headache Unit, Neurology Department, Department of Medicine, University of Valladolid, Hospital Clínico Universitario de Valladolid, Valladolid, Spain
| | - Ángel Luis Guerrero-Peral
- Headache Unit, Neurology Department, Department of Medicine, University of Valladolid, Hospital Clínico Universitario de Valladolid, Valladolid, Spain
| | | | - Ana Beatriz Gago-Veiga
- Headache Unit, Neurology Department, Hospital Universitario de la Princesa & Instituto de Investigación Sanitaria La Princesa, Madrid, Spain
| |
Collapse
|