Predicting early stage drug induced parkinsonism using unsupervised and supervised machine learning

Parvathy Nair; Roth Trisno; Maryam Shojaei Baghini; Gita Pendharkar; Hoam Chung

doi:10.1109/EMBC44109.2020.9175343

Predicting early stage drug induced parkinsonism using unsupervised and supervised machine learning

Parvathy Nair, Roth Trisno, Maryam Shojaei Baghini, Gita Pendharkar, Hoam Chung

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

3 Citations (Scopus)

Abstract

Drug Induced Parkinsonism (DIP) is the most common, debilitating movement disorder induced by antipsychotics. There is no tool available in clinical practice to effectively diagnose the symptoms at the onset of the disease. In this study, the variations in gait accelerometer data due to the intermittency of tremor at the initial stages is examined. These variations are used to train a logistic regression model to predict subjects with early-stage DIP. The logistic classifier predicts if a subject is a DIP or control with approximately 89% sensitivity and 96% specificity. This paper discusses the algorithm used to extract the features in gait data for training the classifier to predict DIP at the earliest.Clinical Relevance - Diagnosing the disease and the causative drug is vital as the physical health of a patient who is mentally unstable can deteriorate with prolonged usage of the drug. The proposed model helps clinicians to diagnose the disease at the onset of tremors with an accuracy of 93.58%.

Original language	English
Title of host publication	2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	776-779
Number of pages	4
ISBN (Electronic)	9781728119908
DOIs	https://doi.org/10.1109/EMBC44109.2020.9175343
Publication status	Published - 2020
Event	International Conference of the IEEE Engineering in Medicine and Biology Society 2020 - Montreal, Canada Duration: 20 Jul 2020 → 24 Jul 2020 Conference number: 42nd https://embc.embs.org/2020/ https://ieeexplore.ieee.org/xpl/conhome/9167168/proceeding (Proceedings)

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume	2020-July
ISSN (Print)	1557-170X

Conference

Conference	International Conference of the IEEE Engineering in Medicine and Biology Society 2020
Abbreviated title	EMBC 2020
Country/Territory	Canada
City	Montreal
Period	20/07/20 → 24/07/20
Internet address	https://embc.embs.org/2020/ https://ieeexplore.ieee.org/xpl/conhome/9167168/proceeding (Proceedings)

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10.1109/EMBC44109.2020.9175343

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Nair, P., Trisno, R., Baghini, M. S., Pendharkar, G., & Chung, H. (2020). Predicting early stage drug induced parkinsonism using unsupervised and supervised machine learning. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 776-779). (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2020-July). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/EMBC44109.2020.9175343

Nair, Parvathy ; Trisno, Roth ; Baghini, Maryam Shojaei et al. / Predicting early stage drug induced parkinsonism using unsupervised and supervised machine learning. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2020. pp. 776-779 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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title = "Predicting early stage drug induced parkinsonism using unsupervised and supervised machine learning",

abstract = "Drug Induced Parkinsonism (DIP) is the most common, debilitating movement disorder induced by antipsychotics. There is no tool available in clinical practice to effectively diagnose the symptoms at the onset of the disease. In this study, the variations in gait accelerometer data due to the intermittency of tremor at the initial stages is examined. These variations are used to train a logistic regression model to predict subjects with early-stage DIP. The logistic classifier predicts if a subject is a DIP or control with approximately 89% sensitivity and 96% specificity. This paper discusses the algorithm used to extract the features in gait data for training the classifier to predict DIP at the earliest.Clinical Relevance - Diagnosing the disease and the causative drug is vital as the physical health of a patient who is mentally unstable can deteriorate with prolonged usage of the drug. The proposed model helps clinicians to diagnose the disease at the onset of tremors with an accuracy of 93.58%.",

author = "Parvathy Nair and Roth Trisno and Baghini, {Maryam Shojaei} and Gita Pendharkar and Hoam Chung",

year = "2020",

doi = "10.1109/EMBC44109.2020.9175343",

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note = "International Conference of the IEEE Engineering in Medicine and Biology Society 2020, EMBC 2020 ; Conference date: 20-07-2020 Through 24-07-2020",

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Nair, P, Trisno, R, Baghini, MS, Pendharkar, G & Chung, H 2020, Predicting early stage drug induced parkinsonism using unsupervised and supervised machine learning. in 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2020-July, IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 776-779, International Conference of the IEEE Engineering in Medicine and Biology Society 2020, Montreal, Quebec, Canada, 20/07/20. https://doi.org/10.1109/EMBC44109.2020.9175343

Predicting early stage drug induced parkinsonism using unsupervised and supervised machine learning. / Nair, Parvathy; Trisno, Roth; Baghini, Maryam Shojaei et al.

2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2020. p. 776-779 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2020-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

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AU - Trisno, Roth

AU - Baghini, Maryam Shojaei

AU - Pendharkar, Gita

AU - Chung, Hoam

N1 - Conference code: 42nd

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N2 - Drug Induced Parkinsonism (DIP) is the most common, debilitating movement disorder induced by antipsychotics. There is no tool available in clinical practice to effectively diagnose the symptoms at the onset of the disease. In this study, the variations in gait accelerometer data due to the intermittency of tremor at the initial stages is examined. These variations are used to train a logistic regression model to predict subjects with early-stage DIP. The logistic classifier predicts if a subject is a DIP or control with approximately 89% sensitivity and 96% specificity. This paper discusses the algorithm used to extract the features in gait data for training the classifier to predict DIP at the earliest.Clinical Relevance - Diagnosing the disease and the causative drug is vital as the physical health of a patient who is mentally unstable can deteriorate with prolonged usage of the drug. The proposed model helps clinicians to diagnose the disease at the onset of tremors with an accuracy of 93.58%.

AB - Drug Induced Parkinsonism (DIP) is the most common, debilitating movement disorder induced by antipsychotics. There is no tool available in clinical practice to effectively diagnose the symptoms at the onset of the disease. In this study, the variations in gait accelerometer data due to the intermittency of tremor at the initial stages is examined. These variations are used to train a logistic regression model to predict subjects with early-stage DIP. The logistic classifier predicts if a subject is a DIP or control with approximately 89% sensitivity and 96% specificity. This paper discusses the algorithm used to extract the features in gait data for training the classifier to predict DIP at the earliest.Clinical Relevance - Diagnosing the disease and the causative drug is vital as the physical health of a patient who is mentally unstable can deteriorate with prolonged usage of the drug. The proposed model helps clinicians to diagnose the disease at the onset of tremors with an accuracy of 93.58%.

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ER -

Nair P, Trisno R, Baghini MS, Pendharkar G, Chung H. Predicting early stage drug induced parkinsonism using unsupervised and supervised machine learning. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2020. p. 776-779. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). https://doi.org/10.1109/EMBC44109.2020.9175343

Predicting early stage drug induced parkinsonism using unsupervised and supervised machine learning

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