Observatorio de I+D+i UPM

Memorias de investigación
Artículos en revistas:
Unveiling relevant non-motor Parkinson's disease severity symptoms using a machine learning approach
Año:2013
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
OBJECTIVE: Is it possible to predict the severity staging of a Parkinson's disease (PD) patient using scores of non-motor symptoms? This is the kickoff question for a machine learning approach to classify two widely known PD severity indexes using individual tests from a broad set of non-motor PD clinical scales only. METHODS: The Hoehn & Yahr index and clinical impression of severity index are global measures of PD severity. They constitute the labels to be assigned in two supervised classification problems using only non-motor symptom tests as predictor variables. Such predictors come from a wide range of PD symptoms, such as cognitive impairment, psychiatric complications, autonomic dysfunction or sleep disturbance. The classification was coupled with a feature subset selection task using an advanced evolutionary algorithm, namely an estimation of distribution algorithm. RESULTS: Results show how five different classification paradigms using a wrapper feature selection scheme are capable of predicting each of the class variables with estimated accuracy in the range of 72-92%. In addition, classification into the main three severity categories (mild, moderate and severe) was split into dichotomic problems where binary classifiers perform better and select different subsets of non-motor symptoms. The number of jointly selected symptoms throughout the whole process was low, suggesting a link between the selected non-motor symptoms and the general severity of the disease. CONCLUSION: Quantitative results are discussed from a medical point of view, reflecting a clear translation to the clinical manifestations of PD. Moreover, results include a brief panel of non-motor symptoms that could help clinical practitioners to identify patients who are at different stages of the disease from a limited set of symptoms, such as hallucinations, fainting, inability to control body sphincters or believing in unlikely facts.
Internacional
Si
JCR del ISI
Si
Título de la revista
Artificial Intelligence in Medicine
ISSN
0933-3657
Factor de impacto JCR
1,355
Información de impacto
Volumen
58
DOI
Número de revista
3
Desde la página
195
Hasta la página
202
Mes
SIN MES
Ranking
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: R. Armañanzas
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
  • Autor: K.R. Chaudhuri
  • Autor: P. Martínez-Martín
  • Autor: P. Larrañaga
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Departamento: Inteligencia Artificial
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