ArticleNeurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology2020
The hidden information in patient-reported outcomes and clinician-assessed outcomes: multiple sclerosis as a proof of concept of a machine learning approach.
Article in Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review.BMC medical informatics and decision making · 2022Pooled it
- Patient-reported outcomes as predictors of disability evolution in Multiple Sclerosis: An interpretable machine learning approach.Multiple sclerosis (Houndmills, Basingstoke, England) · 2026Article
- Integrating big data and artificial intelligence to predict progression in multiple sclerosis: challenges and the path forward.Journal of neuroengineering and rehabilitation · 2025Review
- A taxonomy of cognitive phenotypes in Multiple Sclerosis: a 1-year longitudinal study.Scientific reports · 2024Article
- Mobile Health App (DIGICOG-MS) for Self-Assessment of Cognitive Impairment in People With Multiple Sclerosis: Instrument Validation and Usability Study.JMIR formative research · 2024Article
- The administration of the paper and electronic versions of the Manual Ability Measure-36 (MAM-36) and Fatigue Severity Scale (FSS) is equivalent in people with multiple sclerosis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024Article
- The global patient-reported outcomes for multiple sclerosis initiative: bridging the gap between clinical research and care - updates at the 2023 plenary event.Frontiers in neurology · 2024Review
- Monitoring and Predicting Health Status in Neurological Patients: The ALAMEDA Data Collection Protocol.Healthcare (Basel, Switzerland) · 2023Article
- Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis.The Cochrane database of systematic reviews · 2023Review
- Post-total joint arthroplasty opioid prescribing practices vary widely and are not associated with opioid refill: an observational cohort study.Archives of orthopaedic and trauma surgery · 2023Observational
- Classification of multiple sclerosis clinical profiles using machine learning and grey matter connectome.Frontiers in robotics and AI · 2022Article
- Personalized prediction of rehabilitation outcomes in multiple sclerosis: a proof-of-concept using clinical data, digital health metrics, and machine learning.Medical & biological engineering & computing · 2022Article
- Towards Multimodal Machine Learning Prediction of Individual Cognitive Evolution in Multiple Sclerosis.Journal of personalized medicine · 2021Review
- Machine Learning Use for Prognostic Purposes in Multiple Sclerosis.Life (Basel, Switzerland) · 2021Review
- Patient Reported Outcomes in Chronic Inflammatory Diseases: Current State, Limitations and Perspectives.Frontiers in immunology · 2021Review
- Italian validation of the Arm Function in Multiple Sclerosis Questionnaire (AMSQ).Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2020Article
- Measuring outcomes that matter most to people with multiple sclerosis: the role of patient-reported outcomes.Current opinion in neurology · 2020Review
- Italian translation and psychometric validation of the Manual Ability Measure-36 (MAM-36) and its correlation with an objective measure of upper limb function in patients with multiple sclerosis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2020Article
- Innovations in research and clinical care using patient-generated health data.CA: a cancer journal for clinicians · 2020Review
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.