Background: Achieving an accurate clinical course description in Multiple Sclerosis (MS) is a very hard task even for clinical experts, but it is crucial for communication, prognosis, treatment decision-making, design and recruitment of clinical trials. In this context, meaningful data being “hidden” into Patient Centered Outcomes (PCOs), could provide, through Advanced Machine learning (ML) approaches, a new perspective in predicting MS disease course. Aims: This work aims at using PRO, CS and anthropometric measures to build a statistical model for the detection of MS courses by means of machine learning techniques. The analysis has been conducted on the dataset of the ongoing Italian MS Foundation (FISM) initiative “A New Functional Profile to Monitor the Progression Of Disability In Multiple Sclerosis - PROMOPRO-MS”. Methods: The dataset is composed of 778 patients with MS, that were enrolled in the study without any inclusion/exclusion criteria unless MS diagnosis. The variables identified in the study were based on functions sufficient to encompass the patient"s disability and to represent whole-person behaviours. The set of PCOs selected were related mainly to mobility, fatigue, cognitive performances, emotional status, bladder continence, quality of life. Both unsupervised and supervised machine learning methods were taken into account. The first goal was to assess whether the collected features could discriminate any of the different disease courses by using unsupervised learning techniques looking for a meaningful data structure, then to apply a supervised approach, inferred in the previous step, in order to learn a classifier based only on a subset of the available features. Results: The applied machine learning techniques showed that patients with MS (PwMS) diagnosed as relapsing-remitting (RR) could be isolated from other clinical courses (ALL). In particular, nine “top” questions were selected by the "Features Selection" supervised (FS) algorithm: three questions from Life Satisfaction Index, three items from Functional Independence Measure; two from Modified Fatigue Impact Scale and one from Hospital Anxiety and Depression Scale. Conclusions: To the very best of our knowledge this is the first study which predicted MS course taking only into account a small subset of anthropometric and questionnaires variables, which could be proposed as a novel questionnaire, tailored for RR detection.

Predicting multiple sclerosis disease course with patient centred outcomes (PCOs): a machine learning approach

BRICHETTO, GIAMPAOLO;FIORINI, SAMUELE;BARLA, ANNALISA;VERRI, ALESSANDRO;TACCHINO, ANDREA
2016

Abstract

Background: Achieving an accurate clinical course description in Multiple Sclerosis (MS) is a very hard task even for clinical experts, but it is crucial for communication, prognosis, treatment decision-making, design and recruitment of clinical trials. In this context, meaningful data being “hidden” into Patient Centered Outcomes (PCOs), could provide, through Advanced Machine learning (ML) approaches, a new perspective in predicting MS disease course. Aims: This work aims at using PRO, CS and anthropometric measures to build a statistical model for the detection of MS courses by means of machine learning techniques. The analysis has been conducted on the dataset of the ongoing Italian MS Foundation (FISM) initiative “A New Functional Profile to Monitor the Progression Of Disability In Multiple Sclerosis - PROMOPRO-MS”. Methods: The dataset is composed of 778 patients with MS, that were enrolled in the study without any inclusion/exclusion criteria unless MS diagnosis. The variables identified in the study were based on functions sufficient to encompass the patient"s disability and to represent whole-person behaviours. The set of PCOs selected were related mainly to mobility, fatigue, cognitive performances, emotional status, bladder continence, quality of life. Both unsupervised and supervised machine learning methods were taken into account. The first goal was to assess whether the collected features could discriminate any of the different disease courses by using unsupervised learning techniques looking for a meaningful data structure, then to apply a supervised approach, inferred in the previous step, in order to learn a classifier based only on a subset of the available features. Results: The applied machine learning techniques showed that patients with MS (PwMS) diagnosed as relapsing-remitting (RR) could be isolated from other clinical courses (ALL). In particular, nine “top” questions were selected by the "Features Selection" supervised (FS) algorithm: three questions from Life Satisfaction Index, three items from Functional Independence Measure; two from Modified Fatigue Impact Scale and one from Hospital Anxiety and Depression Scale. Conclusions: To the very best of our knowledge this is the first study which predicted MS course taking only into account a small subset of anthropometric and questionnaires variables, which could be proposed as a novel questionnaire, tailored for RR detection.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/861586
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? 0
social impact