A data mining methodology for predicting early stage Parkinson's disease using non-invasive, high-dimensional gait sensor data

Automated Stage Discrimination of Parkinson’s Disease

Abstract Background: Treatment plans for Parkinson’s disease (PD) are based on a disease stage scale, which is generally determined using a manual, observational procedure. Automated, sensor-based discrimination saves labor and costs in clinical settings and may offer augmented stage determination accuracy. Previous automated devices were either cumbersome or costly and were not suitable for individuals who cannot walk without support.Methods: Since 2017, a device has been available that successfully detects PD and operates for people who cannot walk without support. In the present study, the suitability of this device for automated discrimination of PD stages was tested. The device consists of a walking frame fitted with sensors to simultaneously support walking and monitor patient gait. Sixty-five PD patients in Hoehn and Yahr (HY) stages 1 to 4 and 24 healthy controls were subjected to supported Timed Up and Go (TUG) tests, while using the walking frame. The walking trajectory, velocity, acceleration and force were recorded by the device throughout the tests. These physical parameters were converted into symptomatic spatiotemporal quantities that are conventionally used in PD gait assessment.Results: An analysis of variance (ANOVA) test extended by a confidence interval (CI) analysis indicated statistically significant separability between HY stages for the following spatiotemporal quantities: TUG time (p < 0.001), straight line walking time (p < 0.001), turning time (p < 0.001), and step count (p < 0.001). A negative correlation was obtained for mean step velocity (p < 0.001) and mean step length (p < 0.001). Moreover, correlations were established between these, as well as additional spatiotemporal quantities, and disease duration, L-dihydroxyphenylalanine-(L-DOPA) dose, motor fluctuation, dyskinesia and the mobile part of the Unified Parkinson Disease Rating Scale (UPDRS).Conclusions: We have proven that stage discrimination of PD can be automated, even to patients who cannot support themselves. A similar method might be successfully applied to other gait disorders.

A boosting inspired personalized threshold method for sepsis screening

Sepsis is one of the biggest risks to patient safety, with a natural mortality rate between 25% and 50%. It is difficult to diagnose, and no validated standard for diagnosis currently exists. A commonly used scoring criteria is the quick sequential organ failure assessment (qSOFA). It demonstrates very low specificity in ICU populations, however. We develop a method to personalize thresholds in qSOFA that incorporates easily to measure patient baseline characteristics. We compare the personalized threshold method to qSOFA, five previously published methods that obtain an optimal constant threshold for a single biomarker, and to the machine learning algorithms based on logistic regression and AdaBoosting using patient data in the MIMIC-III database. The personalized threshold method achieves higher accuracy than qSOFA and the five published methods and has comparable performance to machine learning methods. Personalized thresholds, however, are much easier to adopt in real-life monitoring than machine learning methods as they are computed once for a patient and used in the same way as qSOFA, whereas the machine learning methods are hard to implement and interpret.

Artificial intelligence for assisting diagnostics and assessment of Parkinson's disease-A review

Artificial intelligence, specifically machine learning, has found numerous applications in computer-aided diagnostics, monitoring and management of neurodegenerative movement disorders of parkinsonian type. These tasks are not trivial due to high inter-subject variability and similarity of clinical presentations of different neurodegenerative disorders in the early stages. This paper aims to give a comprehensive, high-level overview of applications of artificial intelligence through machine learning algorithms in kinematic analysis of movement disorders, specifically Parkinson's disease (PD). We surveyed papers published between January 2007 and January 2019, within online databases, including PubMed and Science Direct, with a focus on the most recently published studies. The search encompassed papers dealing with the implementation of machine learning algorithms for diagnosis and assessment of PD using data describing motion of upper and lower extremities. This systematic review presents an overview of 48 relevant studies published in the abovementioned period, which investigate the use of artificial intelligence for diagnostics, therapy assessment and progress prediction in PD based on body kinematics. Different machine learning algorithms showed promising results, particularly for early PD diagnostics. The investigated publications demonstrated the potentials of collecting data from affordable and globally available devices. However, to fully exploit artificial intelligence technologies in the future, more widespread collaboration is advised among medical institutions, clinicians and researchers, to facilitate aligning of data collection protocols, sharing and merging of data sets.

Parkinson's Disease Classification of mPower Walking Activity Participants

Motion sensor data collected using Sage Bionetwork's mPower application on the Apple iPhone to record participant activities is analyzed to classify samples as positive or negative for Parkinson's Diagnosis. Pre-processing of the data showed differences in the time and frequency dimensions for features derived from Apple Core motion data. Several classic machine learning classification algorithms were trained on seventy-seven derived data points for best precision, recall, and F-1 score. Accuracy as high as ninety-two percent were achieved, with the best results attained from decision tree and multi-layered artificial neural network algorithms. This research shows that motion data produced on the Apple iPhone using the mPower application shows promise as an accessible platform to classify participants for presence of Parkinson's Disease signs.

Evaluating the Cost-Effectiveness of an Early Detection of Parkinson's Disease through Innovative Technology

Early detection of Parkinson's Disease (PD) is critically important as it can increase patient quality of life and save treatment cost. An innovative approach for early detection of PD is to use non-wearable sensors that are capable of capturing skeletal joint data. This paper evaluates the cost-effectiveness of this sensor-based intervention considering the quality-adjusted life years (QALYs) and the associated costs. The results indicate that the intervention would be cost-effective if devices were deployed for community health screening in public places such as health fairs and pharmacies.