Nikos F. Fragopanagos

Bio: Nikos F. Fragopanagos is an academic researcher. The author has contributed to research in topics: Analytics. The author has an hindex of 2, co-authored 4 publications receiving 11 citations.

From Wellness to Medical Diagnostic Apps: The Parkinson's Disease Case.

Abstract: This paper presents the design and development of the CloudUPDRS app and supporting system developed as a Class I medical device to assess the severity of motor symptoms for Parkinson’s Disease. We report on lessons learnt towards meeting fidelity and regulatory requirements; effective procedures employed to structure user context and ensure data quality; a robust service provision architecture; a dependable analytics toolkit; and provisions to meet mobility and social needs of people with Parkinson’s.

Towards Longitudinal Data Analytics in Parkinson's Disease.

Abstract: The CloudUPDRS app has been developed as a Class I medical device to assess the severity of motor symptoms for Parkinson’s Disease using a fully automated data capture and signal analysis process based on the standard Unified Parkinson’s Disease Rating Scale. In this paper we report on the design and development of the signal processing and longitudinal data analytics microservices developed to carry out these assessments and to forecast the long-term development of the disease. We also report on early findings from the application of these techniques in the wild with a cohort of early adopters.

Towards Longitudinal Data Analytics in Parkinson’s Disease

Abstract: The CloudUPDRS app has been developed as a Class I medical device to assess the severity of motor symptoms for Parkinson’s Disease using a fully automated data capture and signal analysis process based on the standard Unified Parkinson’s Disease Rating Scale. In this paper we report on the design and development of the signal processing and longitudinal data analytics microservices developed to carry out these assessments and to forecast the long-term development of the disease. We also report on early findings from the application of these techniques in the wild with a cohort of early adopters.

From wellness to medical diagnostic apps: the Parkinson's Disease case

Abstract: This paper presents the design and development of the CloudUPDRS app and supporting system developed as a Class I medical device to assess the severity of motor symptoms for Parkinson’s Disease. We report on lessons learnt towards meeting fidelity and regulatory requirements; effective procedures employed to structure user context and ensure data quality; a robust service provision architecture; a dependable analytics toolkit; and provisions to meet mobility and social needs of people with Parkinson’s.

Deep learning Parkinson's from smartphone data

Abstract: The cloudUPDRS app is a Class I medical device, namely an active transient non-invasive instrument, certified by the Medicines and Healthcare products Regulatory Agency in the UK for the clinical assessment of the motor symptoms of Parkinson's Disease. The app follows closely the Unified Parkinson's Disease Rating Scale which is the most commonly used protocol in the clinical study of PD; can be used by patients and their carers at home or in the community; and, requires the user to perform a sequence of iterated movements which are recorded by the phone sensors. This paper discusses how the cloudUPDRS system addresses two key challenges towards meeting essential consistency and efficiency requirements, namely: (i) How to ensure high-quality data collection especially considering the unsupervised nature of the test, in particular, how to achieve firm user adherence to the prescribed movements; and (ii) How to reduce test duration from approximately 25 minutes typically required by an experienced patient, to below 4 minutes, a threshold identified as critical to obtain significant improvements in clinical compliance. To address the former, we combine a bespoke design of the user experience tailored so as to constrain context, with a deep learning approach used to identify failures to follow the movement protocol while at the same time limiting false positives to avoid unnecessary repetition. We address the latter by developing a machine learning approach to personalise assessments by selecting those elements of the UPDRS protocol that most closely match individual symptom profiles and thus offer the highest inferential power hence closely estimating the patent's overall UPRDS score.

Developing a tool for remote digital assessment of Parkinson's disease

Abstract: BACKGROUND: The natural fluctuation of motor symptoms of Parkinson's disease (PD) makes judgement of any change challenging and the use of clinical scales such as the International Parkinson and Movement Disorder Society (MDS)-UPDRS imperative. Recently developed commodity mobile communication devices, such as smartphones, could possibly be used to assess motor symptoms in PD patients in a convenient way with low cost. We provide the first report on the development and testing of stand-alone software for mobile devices that could be used to assess both tremor and bradykinesia of PD patients. METHODS: We assessed motor symptoms with a custom-made smartphone application in 14 patients and compared the results with their MDS-UPDRS scores. RESULTS: We found significant correlation between five subscores of MDS-UPDRS (rest tremor, postural tremor, pronation-supination, leg agility, and finger tapping) and eight parameters of the data collected with the smartphone. CONCLUSIONS: These results provide evidence as a proof of principle that smartphones could be a useful tool to objectively assess motor symptoms in PD in clinical and experimental settings.

PDkit: an open source data science toolkit for Parkinson's disease

Abstract: Parkinson's Disease (PD) is a long-term neurodegenerative disorder that affects over four million people worldwide. State-of-the-art mobile and wearable sensing technologies offer the prospect of enhanced clinical care pathways for PD patients through integration of automated symptom tracking within current healthcare infrastructures. Yet, even though sensor data collection can be performed efficiently today using these technologies, automated inference of high-level severity scores from such data is still limited by the lack of validated evidence, despite a plethora of published research. In this paper, we introduce PDkit, an open source toolkit for PD progression monitoring using multimodal sensor data obtained by smartphone apps or wearables. We discuss how PDkit implements an information processing pipeline incorporating distinct stages for data ingestion and quality assessment, feature and biomarker estimation, and clinical scoring using high-level clinical scales. Finally, we demonstrate how PDkit facilitates outcome reproducibility and algorithmic transparency in the CUSSP clinical trial, a pilot, dual-site, open label study.

Scaling-Out Longitudinal Clinical Analytics with Dataflow Processing

Abstract: There are two key ingredients in supporting high-frequency and continuous clinical assessment of patient populations at scale: first, the availability of validated metrics of disease progression which reliably capture the longitudinal variations of symptoms; and second, the ability to compute these metrics on the fly over multiple concurrent streams of sensor data captured at home or in the community. In this paper, we describe the design, development and validation of PDkit, a comprehensive data science toolkit for Parkinson's Disease, and explore the dataflow paradigm as a means to provide salable performance. Our aim is to contribute towards the development of robust clinical outcome measures for therapeutic trials and to support longitudinal investigations of disease mechanism through the analysis of data collected from wearables and smartphones. The PDkit is released as open source and offers a succinct interface for interactive collaborative data exploration. Moreover, it enables the composition of data processing pipelines for tremor, tapping, bradykinesia and gait tests with the view to support horizontal scalability over common Cloud infrastructures on production workloads. Specifically, we report on our early experiments executing PDkit pipelines using Apache Beam, a unified dataflow multi-runtime stream processing engine. Our long-term aim is to provide the PD research community with the tools needed to individually tailor treatment plans and to empower patients to become more involved in their own care.