TY - JOUR
T1 - Disease severity classification using passively collected smartphone-based keystroke dynamics within multiple sclerosis
AU - Hoeijmakers, Aleide
AU - Licitra, Giovanni
AU - Meijer, Kim
AU - Lam, Ka-Hoo
AU - Molenaar, Pam
AU - Strijbis, Eva
AU - Killestein, Joep
N1 - Funding Information: The authors would like to thank all the patients for their participation in this study. The authors also disclose receipt of the following financial support for the research, authorship, and publication of this article: funding from the Public Private Partnership Allowance, made available by Health-Holland, Top Sector Life Sciences and Health (grant number LSHM16060-SGF), and Stichting Multiple Sclerosis Research (grant number 16-946 MS) to stimulate public-private partnerships; unrestricted fundingwas also received from Biogen. Publisher Copyright: © 2023, The Author(s).
PY - 2023/12/1
Y1 - 2023/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85147235369&partnerID=8YFLogxK
U2 - https://doi.org/10.1038/s41598-023-28990-6
DO - https://doi.org/10.1038/s41598-023-28990-6
M3 - Article
C2 - 36725975
SN - 2045-2322
VL - 13
JO - Scientific reports
JF - Scientific reports
IS - 1
M1 - 1871
ER -