Disease severity classification using passively collected smartphone-based keystroke dynamics within multiple sclerosis

Abstract Multiple Sclerosis (MS) is a progressive demyelinating disease of the central nervous system characterised by a wide range of motor and non-motor symptoms. The level of disability of people with MS (pwMS) is based on a wide range of clinical measures, though their frequency of evaluation an...

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Main Authors: Aleide Hoeijmakers, Giovanni Licitra, Kim Meijer, Ka-Hoo Lam, Pam Molenaar, Eva Strijbis, Joep Killestein
Format: Article
Language:English
Published: Nature Portfolio 2023-02-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-023-28990-6
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author Aleide Hoeijmakers
Giovanni Licitra
Kim Meijer
Ka-Hoo Lam
Pam Molenaar
Eva Strijbis
Joep Killestein
author_facet Aleide Hoeijmakers
Giovanni Licitra
Kim Meijer
Ka-Hoo Lam
Pam Molenaar
Eva Strijbis
Joep Killestein
author_sort Aleide Hoeijmakers
collection DOAJ
description Abstract Multiple Sclerosis (MS) is a progressive demyelinating disease of the central nervous system characterised by a wide range of motor and non-motor symptoms. The level of disability of people with MS (pwMS) is based on a wide range of clinical measures, though their frequency of evaluation and inaccuracies coming from objective and self-reported evaluations limits these assessments. Alternatively, remote health monitoring through devices can offer a cost-efficient solution to gather more reliable, objective measures continuously. Measuring smartphone keyboard interactions is a promising tool since typing and, thus, keystroke dynamics are likely influenced by symptoms that pwMS can experience. Therefore, this paper aims to investigate whether keyboard interactions gathered on a person’s smartphone can provide insight into the clinical status of pwMS leveraging machine learning techniques. In total, 24 Healthy Controls (HC) and 102 pwMS were followed for one year. Next to continuous data generated via smartphone interactions, clinical outcome measures were collected and used as targets to train four independent multivariate binary classification pipelines in discerning pwMS versus HC and estimating the level of disease severity, manual dexterity and cognitive capabilities. The final models yielded an AUC-ROC in the hold-out set above 0.7, with the highest performance obtained in estimating the level of fine motor skills (AUC-ROC=0.753). These findings show that keyboard interactions combined with machine learning techniques can be used as an unobtrusive monitoring tool to estimate various levels of clinical disability in pwMS from daily activities and with a high frequency of sampling without increasing patient burden.
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spelling doaj.art-22b37ce7d0714a81a2439b2313ef7ae22023-02-05T12:12:53ZengNature PortfolioScientific Reports2045-23222023-02-0113111210.1038/s41598-023-28990-6Disease severity classification using passively collected smartphone-based keystroke dynamics within multiple sclerosisAleide Hoeijmakers0Giovanni Licitra1Kim Meijer2Ka-Hoo Lam3Pam Molenaar4Eva Strijbis5Joep Killestein6Neurocast B.V.Neurocast B.V.Neurocast B.V.Department of Neurology, Amsterdam University Medical CentersDepartment of Neurology, Amsterdam University Medical CentersDepartment of Neurology, Amsterdam University Medical CentersDepartment of Neurology, Amsterdam University Medical CentersAbstract Multiple Sclerosis (MS) is a progressive demyelinating disease of the central nervous system characterised by a wide range of motor and non-motor symptoms. The level of disability of people with MS (pwMS) is based on a wide range of clinical measures, though their frequency of evaluation and inaccuracies coming from objective and self-reported evaluations limits these assessments. Alternatively, remote health monitoring through devices can offer a cost-efficient solution to gather more reliable, objective measures continuously. Measuring smartphone keyboard interactions is a promising tool since typing and, thus, keystroke dynamics are likely influenced by symptoms that pwMS can experience. Therefore, this paper aims to investigate whether keyboard interactions gathered on a person’s smartphone can provide insight into the clinical status of pwMS leveraging machine learning techniques. In total, 24 Healthy Controls (HC) and 102 pwMS were followed for one year. Next to continuous data generated via smartphone interactions, clinical outcome measures were collected and used as targets to train four independent multivariate binary classification pipelines in discerning pwMS versus HC and estimating the level of disease severity, manual dexterity and cognitive capabilities. The final models yielded an AUC-ROC in the hold-out set above 0.7, with the highest performance obtained in estimating the level of fine motor skills (AUC-ROC=0.753). These findings show that keyboard interactions combined with machine learning techniques can be used as an unobtrusive monitoring tool to estimate various levels of clinical disability in pwMS from daily activities and with a high frequency of sampling without increasing patient burden.https://doi.org/10.1038/s41598-023-28990-6
spellingShingle Aleide Hoeijmakers
Giovanni Licitra
Kim Meijer
Ka-Hoo Lam
Pam Molenaar
Eva Strijbis
Joep Killestein
Disease severity classification using passively collected smartphone-based keystroke dynamics within multiple sclerosis
Scientific Reports
title Disease severity classification using passively collected smartphone-based keystroke dynamics within multiple sclerosis
title_full Disease severity classification using passively collected smartphone-based keystroke dynamics within multiple sclerosis
title_fullStr Disease severity classification using passively collected smartphone-based keystroke dynamics within multiple sclerosis
title_full_unstemmed Disease severity classification using passively collected smartphone-based keystroke dynamics within multiple sclerosis
title_short Disease severity classification using passively collected smartphone-based keystroke dynamics within multiple sclerosis
title_sort disease severity classification using passively collected smartphone based keystroke dynamics within multiple sclerosis
url https://doi.org/10.1038/s41598-023-28990-6
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