Toward <scp>e‐Scales</scp>: Digital Administration of the International Parkinson and Movement Disorder Society Rating Scales

Monje, Mariana H.G.; Fuller, Rebecca; Cubo, Esther; Mestre, Tiago; Tan, Ai Huey; Stout, Julie C.; Ali, Shazia; Chahine, Lana M.; Dujardin, Kathy; Fitzer‐Attas, Cheryl; Youn, Jinyoung; Bloem, Bastiaan R.; Horak, Fay B.; Merola, Aristide; Reilmann, Ralf; Paul, Serene S.; Dorsey, E. Ray; Maetzler, Walter; Espay, Alberto J.; Martinez‐Martín, Pablo; Stebbins, Glenn T.; Sánchez‐Ferro, Álvaro

doi:10.1002/mdc3.13135

Cited by 6 publications

(2 citation statements)

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“…Telemonitoring is the remote gathering of information about a patient which is used to inform healthcare providers (in a clinical setting) or researchers (in the framework of a trial). A wide and expanding spectrum of tools can be used for telemonitoring, including body-worn sensors [ 27 , 28 ], home sensors [ 29 ], specific apps for the smartphone [ 30 , 31 ], digital diaries [ 32 ], or analysis of common appliances such as computer keyboards [ 33 ] (only several selected high-quality references are given here). The promise of remote monitoring is to offer objective, continuous measures of relevant symptoms while patients are at home.…”

Section: Telemonitoringmentioning

confidence: 99%

The state of telemedicine for persons with Parkinson's disease

Bergh

Bloem

Meinders³

et al. 2021

Current Opinion in Neurology

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Purpose of review The COVID-pandemic has facilitated the implementation of telemedicine in both clinical practice and research. We highlight recent developments in three promising areas of telemedicine: teleconsultation, telemonitoring, and teletreatment. We illustrate this using Parkinson's disease as a model for other chronic neurological disorders. Recent findings Teleconsultations can reliably administer parts of the neurological examination remotely, but are typically not useful for establishing a reliable diagnosis. For follow-ups, teleconsultations can provide enhanced comfort and convenience to patients, and provide opportunities for blended and proactive care models. Barriers include technological challenges, limited clinician confidence, and a suboptimal clinician-patient relationship. Telemonitoring using wearable sensors and smartphone-based apps can support clinical decision-making, but we lack large-scale randomized controlled trials to prove effectiveness on clinical outcomes. Increasingly many trials are now incorporating telemonitoring as an exploratory outcome, but more work remains needed to demonstrate its clinical meaningfulness. Finding a balance between benefits and burdens for individual patients remains vital. Recent work emphasised the promise of various teletreatment solutions, such as remotely adjustable deep brain stimulation parameters, virtual reality enhanced exercise programs, and telephone-based cognitive behavioural therapy. Personal contact remains essential to ascertain adherence to teletreatment. Summary The availability of different telemedicine tools for remote consultation, monitoring, and treatment is increasing. Future research should establish whether telemedicine improves outcomes in routine clinical care, and further underpin its merits both as intervention and outcome in research settings.

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Section: Telemonitoringmentioning

confidence: 99%

The state of telemedicine for persons with Parkinson's disease

Bergh

Bloem

Meinders³

et al. 2021

Current Opinion in Neurology

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“…The most common disease or symptom resembling PD is Kampavata, and many of the other PD symptoms, mainly motor symptoms, through which PD is diagnosed, fall under the category of Vata dosha, so PD is called Vata dosha disorder in Ayurveda (9,10). If we want to consider all the different types of motor and non-motor symptoms that have emerged in PD over time, we can follow different standard scaling systems for assessing different motor and non-motor symptoms associated with PD (11,12).…”

Section: Introductionmentioning

confidence: 99%

Development of a machine learning-based Parkinson's disease prediction system through Ayurvedic dosha analysis

Majhi¹,

Choudhury²,

Saha³

et al. 2023

IJAM

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The development of a machine learning (ML) based prediction model for Parkinson's disease (PD) using Ayurvedic literature is very rare in practice. In modern era of digitization and artificial intelligence this lack leads us for developing a significant and useful predictive model for PD in light of Ayurveda. As in contemporary Ayurvedic literature, PD has not been fully classified according to all of its motor and non-motor symptoms, with the exception of Kampavata or tremor, one of the cardinal motor symptoms of PD, we have taken the help of MDS-UPDRS-II and MDS-NMSQ scaling system as an initial input for this purpose. Based on the available literature, we determined our tridosha score using these scales, which become our main inputs to the ML algorithm, along with other general health attributes such as age, sex, and BMI. We applied various ML algorithms and ranked our best ML model based on their performance. For training and testing purposes, we used the Fox Insight dataset with n = 80,916 records including PD and control. Finally, we found that Kernel-SVM, SVM, Logistics Regression (LR), and XGBoost are our four most accurate algorithms, with an accuracy more than 92.5% with no dimensionality reduction applied. Here we chose the LR model as our best ML model, depending on the lower false positive rate of 0.045 with an accuracy of 92.6%. The designed LR model is statistically significant with χ2(6) = 70703.137, p =0.000. The LR coefficient was also calculated for probability analysis and future implementation of digital Ayurveda-based PD prediction applications.

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