The hidden information in patient-reported outcomes and clinician-assessed outcomes: multiple sclerosis as a proof of concept of a machine learning approach.
Neurol Sci
; 41(2): 459-462, 2020 Feb.
Article
em En
| MEDLINE
| ID: mdl-31659583
ABSTRACT
Machine learning (ML) applied to patient-reported (PROs) and clinical-assessed outcomes (CAOs) could favour a more predictive and personalized medicine. Our aim was to confirm the important role of applying ML to PROs and CAOs of people with relapsing-remitting (RR) and secondary progressive (SP) form of multiple sclerosis (MS), to promptly identifying information useful to predict disease progression. For our analysis, a dataset of 3398 evaluations from 810 persons with MS (PwMS) was adopted. Three steps were provided course classification; extraction of the most relevant predictors at the next time point; prediction if the patient will experience the transition from RR to SP at the next time point. The Current Course Assignment (CCA) step correctly assigned the current MS course with an accuracy of about 86.0%. The MS course at the next time point can be predicted using the predictors selected in CCA. PROs/CAOs Evolution Prediction (PEP) followed by Future Course Assignment (FCA) was able to foresee the course at the next time point with an accuracy of 82.6%. Our results suggest that PROs and CAOs could help the clinician decision-making in their practice.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Índice de Gravidade de Doença
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Avaliação de Resultados em Cuidados de Saúde
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Progressão da Doença
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Aprendizado de Máquina
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Esclerose Múltipla
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Adult
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article