Integrating Clinical Data and Patient-Reported Outcomes for Analyzing Gender Differences and Progression in Multiple Sclerosis Using Machine Learning...The European Federation for Medical Informatics (EFMI) Special Topic Conference (STC), November 27-29, 2024, Timisoara, Romania.
Multiple sclerosis (MS) is a complex neurodegenerative disease with a variable prognosis that complicates effective management and treatment. This study leverages machine learning (ML) to enhance the understanding of disease progression and uncover gender-based differences in MS by analyzing clinica...
| Publicado en: | Studies in Health Technology & Informatics Vol. 321; pp. 17 - 22 |
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| Autores principales: | , , , |
| Formato: | proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=181103792&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181103792 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2024 vid: 321 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 181103792 181103792 181103792 10.3233/SHTI241053 181103792 ppf: 17 ppct: 5 formats: tig: atl: Integrating Clinical Data and Patient-Reported Outcomes for Analyzing Gender Differences and Progression in Multiple Sclerosis Using Machine Learning...The European Federation for Medical Informatics (EFMI) Special Topic Conference (STC), November 27-29, 2024, Timisoara, Romania. aug: au: VIGUERA MORENO, Minerva MARZO SOLA, Maria Eugenia SANCHEZ DE MADARIAGA, Ricardo MARTIN-SANCHEZ, Fernando affil: Programa de Doctorado en Ciencias Biomédicas y Salud Pública UNED-IMIENS, Universidad Nacional de Educación a Distancia (UNED), 28015 Madrid, Spain sug: subj: Patient-Reported Outcomes Sex Factors Disease Progression Risk Factors Multiple Sclerosis Machine Learning Utilization Prediction Algorithms Utilization Congresses and Conferences Romania Human Spain Outpatients Prospective Studies Secondary Health Care Data Management Decision Trees Random Forest Support Vector Machine Scales Quality of Life Linear Regression T-Tests Depression Fatigue Prediction Models Individualized Medicine Machine Learning Algorithms Classification Algorithms Questionnaires Psychological Tests Romania ab: Multiple sclerosis (MS) is a complex neurodegenerative disease with a variable prognosis that complicates effective management and treatment. This study leverages machine learning (ML) to enhance the understanding of disease progression and uncover gender-based differences in MS by analyzing clinical data integrated with patient-reported outcomes (PROMs). We conducted a prospective cohort study involving 250 MS patients at a secondary care hospital in Spain over an 18-month period. Using REDCap for data management, we collected comprehensive demographic, clinical, and PROMs data. Our analysis utilized Decision Trees, Random Forest, and Support Vector Machine algorithms to classify patients based on disease evolution and infer Expanded Disability Status Scale (EDSS) levels. Additionally, we employed propensity score matching to analyze gender differences, focusing on clinical outcomes and quality of life measures. The results could indicate that integrating diverse data sets through ML would significantly improve the diagnostic accuracy and serve as a support for clinician's decision making. Our models achieved high accuracy in classifying MS types and predicting disability levels, demonstrating the potential of ML in personalized treatment planning. Furthermore, our findings suggest notable gender differences in disease progression and response to treatment. These insights advocate for a genderspecific approach in MS management and highlight the importance of personalized medicine. This study underscores the transformative potential of ML in enhancing the understanding and management of MS through integrated data analysis. pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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