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Article ; Online: A Multifaceted benchmarking of synthetic electronic health record generation models

Chao Yan / Yao Yan / Zhiyu Wan / Ziqi Zhang / Larsson Omberg / Justin Guinney / Sean D. Mooney / Bradley A. Malin

Nature Communications, Vol 13, Iss 1, Pp 1-

2022 Volume 18

Abstract: Synthetic health data have the potential to mitigate privacy concerns when sharing data to support biomedical research and the development of innovative healthcare applications. In this work, the authors introduce a use case oriented benchmarking ... ...

Abstract	Synthetic health data have the potential to mitigate privacy concerns when sharing data to support biomedical research and the development of innovative healthcare applications. In this work, the authors introduce a use case oriented benchmarking framework to evaluate data synthesis models through a set of utility and privacy metrics.
Keywords	Science ; Q
Language	English
Publishing date	2022-12-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A Smartphone Application as an Exploratory Endpoint in a Phase 3 Parkinson’s Disease Clinical Trial

Alex Page / Norman Yung / Peggy Auinger / Charles Venuto / Alistair Glidden / Eric Macklin / Larsson Omberg / Michael A. Schwarzschild / E. Ray Dorsey

Digital Biomarkers, Vol 6, Iss 1, Pp 1-

A Pilot Study

2022 Volume 8

Abstract: Background: Smartphones can generate objective measures of Parkinson’s disease (PD) and supplement traditional in-person rating scales. However, smartphone use in clinical trials has been limited. Objective: This study aimed to determine the feasibility ... ...

Abstract	Background: Smartphones can generate objective measures of Parkinson’s disease (PD) and supplement traditional in-person rating scales. However, smartphone use in clinical trials has been limited. Objective: This study aimed to determine the feasibility of introducing a smartphone research application into a PD clinical trial and to evaluate the resulting measures. Methods: A smartphone application was introduced part-way into a phase 3 randomized clinical trial of inosine. The application included finger tapping, gait, and cognition tests, and participants were asked to complete an assessment battery at home and in clinic alongside the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). Results: Of 236 eligible participants in the parent study, 88 (37%) consented to participate, and 59 (27 randomized to inosine and 32 to placebo) completed a baseline smartphone assessment. These 59 participants collectively completed 1,292 batteries of assessments. The proportion of participants who completed at least one smartphone assessment was 61% at 3, 54% at 6, and 35% at 12 months. Finger tapping speed correlated weakly with the part III motor portion (r = −0.16, left hand; r = −0.04, right hand) and total (r = −0.14) MDS-UPDRS. Gait speed correlated better with the same measures (r = −0.25, part III motor; r = −0.34, total). Over 6 months, finger tapping speed, gait speed, and memory scores did not differ between those randomized to active drug or placebo. Conclusions: Introducing a smartphone application midway into a phase 3 clinical trial was challenging. Measures of bradykinesia and gait speed correlated modestly with traditional outcomes and were consistent with the study’s overall findings, which found no benefit of the active drug.
Keywords	smartphone ; parkinson disease ; clinical trial ; inosine ; telemedicine ; gait ; movement ; cognition ; Biology (General) ; QH301-705.5
Subject code	610
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Karger Publishers
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Disentangling personalized treatment effects from “time-of-the-day” confounding in mobile health studies

Elias Chaibub Neto / Thanneer M. Perumal / Abhishek Pratap / Aryton Tediarjo / Brian M. Bot / Lara Mangravite / Larsson Omberg

PLoS ONE, Vol 17, Iss

2022 Volume 8

Abstract: Ideally, a patient’s response to medication can be monitored by measuring changes in performance of some activity. In observational studies, however, any detected association between treatment (“on-medication” vs “off-medication”) and the outcome ( ... ...

Abstract	Ideally, a patient’s response to medication can be monitored by measuring changes in performance of some activity. In observational studies, however, any detected association between treatment (“on-medication” vs “off-medication”) and the outcome (performance in the activity) might be due to confounders. In particular, causal inferences at the personalized level are especially vulnerable to confounding effects that arise in a cyclic fashion. For quick acting medications, effects can be confounded by circadian rhythms and daily routines. Using the time-of-the-day as a surrogate for these confounders and the performance measurements as captured on a smartphone, we propose a personalized statistical approach to disentangle putative treatment and “time-of-the-day” effects, that leverages conditional independence relations spanned by causal graphical models involving the treatment, time-of-the-day, and outcome variables. Our approach is based on conditional independence tests implemented via standard and temporal linear regression models. Using synthetic data, we investigate when and how residual autocorrelation can affect the standard tests, and how time series modeling (namely, ARIMA and robust regression via HAC covariance matrix estimators) can remedy these issues. In particular, our simulations illustrate that when patients perform their activities in a paired fashion, positive autocorrelation can lead to conservative results for the standard regression approach (i.e., lead to deflated true positive detection), whereas negative autocorrelation can lead to anticonservative behavior (i.e., lead to inflated false positive detection). The adoption of time series methods, on the other hand, leads to well controlled type I error rates. We illustrate the application of our methodology with data from a Parkinson’s disease mobile health study.
Keywords	Medicine ; R ; Science ; Q
Subject code	310
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Disentangling personalized treatment effects from "time-of-the-day" confounding in mobile health studies.

Elias Chaibub Neto / Thanneer M Perumal / Abhishek Pratap / Aryton Tediarjo / Brian M Bot / Lara Mangravite / Larsson Omberg

PLoS ONE, Vol 17, Iss 8, p e

2022 Volume 0271766

Abstract: Ideally, a patient's response to medication can be monitored by measuring changes in performance of some activity. In observational studies, however, any detected association between treatment ("on-medication" vs "off-medication") and the outcome ( ... ...

Abstract	Ideally, a patient's response to medication can be monitored by measuring changes in performance of some activity. In observational studies, however, any detected association between treatment ("on-medication" vs "off-medication") and the outcome (performance in the activity) might be due to confounders. In particular, causal inferences at the personalized level are especially vulnerable to confounding effects that arise in a cyclic fashion. For quick acting medications, effects can be confounded by circadian rhythms and daily routines. Using the time-of-the-day as a surrogate for these confounders and the performance measurements as captured on a smartphone, we propose a personalized statistical approach to disentangle putative treatment and "time-of-the-day" effects, that leverages conditional independence relations spanned by causal graphical models involving the treatment, time-of-the-day, and outcome variables. Our approach is based on conditional independence tests implemented via standard and temporal linear regression models. Using synthetic data, we investigate when and how residual autocorrelation can affect the standard tests, and how time series modeling (namely, ARIMA and robust regression via HAC covariance matrix estimators) can remedy these issues. In particular, our simulations illustrate that when patients perform their activities in a paired fashion, positive autocorrelation can lead to conservative results for the standard regression approach (i.e., lead to deflated true positive detection), whereas negative autocorrelation can lead to anticonservative behavior (i.e., lead to inflated false positive detection). The adoption of time series methods, on the other hand, leads to well controlled type I error rates. We illustrate the application of our methodology with data from a Parkinson's disease mobile health study.
Keywords	Medicine ; R ; Science ; Q
Subject code	310
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Indicators of retention in remote digital health studies

Abhishek Pratap / Elias Chaibub Neto / Phil Snyder / Carl Stepnowsky / Noémie Elhadad / Daniel Grant / Matthew H. Mohebbi / Sean Mooney / Christine Suver / John Wilbanks / Lara Mangravite / Patrick J. Heagerty / Pat Areán / Larsson Omberg

npj Digital Medicine, Vol 3, Iss 1, Pp 1-

a cross-study evaluation of 100,000 participants

2020 Volume 10

Abstract: Abstract Digital technologies such as smartphones are transforming the way scientists conduct biomedical research. Several remotely conducted studies have recruited thousands of participants over a span of a few months allowing researchers to collect ... ...

Abstract	Abstract Digital technologies such as smartphones are transforming the way scientists conduct biomedical research. Several remotely conducted studies have recruited thousands of participants over a span of a few months allowing researchers to collect real-world data at scale and at a fraction of the cost of traditional research. Unfortunately, remote studies have been hampered by substantial participant attrition, calling into question the representativeness of the collected data including generalizability of outcomes. We report the findings regarding recruitment and retention from eight remote digital health studies conducted between 2014–2019 that provided individual-level study-app usage data from more than 100,000 participants completing nearly 3.5 million remote health evaluations over cumulative participation of 850,000 days. Median participant retention across eight studies varied widely from 2–26 days (median across all studies = 5.5 days). Survival analysis revealed several factors significantly associated with increase in participant retention time, including (i) referral by a clinician to the study (increase of 40 days in median retention time); (ii) compensation for participation (increase of 22 days, 1 study); (iii) having the clinical condition of interest in the study (increase of 7 days compared with controls); and (iv) older age (increase of 4 days). Additionally, four distinct patterns of daily app usage behavior were identified by unsupervised clustering, which were also associated with participant demographics. Most studies were not able to recruit a sample that was representative of the race/ethnicity or geographical diversity of the US. Together these findings can help inform recruitment and retention strategies to enable equitable participation of populations in future digital health research.
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	150
Language	English
Publishing date	2020-02-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Developing better digital health measures of Parkinson's disease using free living data and a crowdsourced data analysis challenge.

Solveig K Sieberts / Henryk Borzymowski / Yuanfang Guan / Yidi Huang / Ayala Matzner / Alex Page / Izhar Bar-Gad / Brett Beaulieu-Jones / Yuval El-Hanani / Jann Goschenhofer / Monica Javidnia / Mark S Keller / Yan-Chak Li / Mohammed Saqib / Greta Smith / Ana Stanescu / Charles S Venuto / Robert Zielinski / BEAT-PD DREAM Challenge Consortium /

Arun Jayaraman / Luc J W Evers / Luca Foschini / Alex Mariakakis / Gaurav Pandey / Nicholas Shawen / Phil Synder / Larsson Omberg

PLOS Digital Health, Vol 2, Iss 3, p e

2023 Volume 0000208

Abstract: One of the promising opportunities of digital health is its potential to lead to more holistic understandings of diseases by interacting with the daily life of patients and through the collection of large amounts of real-world data. Validating and ... ...

Abstract	One of the promising opportunities of digital health is its potential to lead to more holistic understandings of diseases by interacting with the daily life of patients and through the collection of large amounts of real-world data. Validating and benchmarking indicators of disease severity in the home setting is difficult, however, given the large number of confounders present in the real world and the challenges in collecting ground truth data in the home. Here we leverage two datasets collected from patients with Parkinson's disease, which couples continuous wrist-worn accelerometer data with frequent symptom reports in the home setting, to develop digital biomarkers of symptom severity. Using these data, we performed a public benchmarking challenge in which participants were asked to build measures of severity across 3 symptoms (on/off medication, dyskinesia, and tremor). 42 teams participated and performance was improved over baseline models for each subchallenge. Additional ensemble modeling across submissions further improved performance, and the top models validated in a subset of patients whose symptoms were observed and rated by trained clinicians.
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	006
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Detecting the impact of subject characteristics on machine learning-based diagnostic applications

Elias Chaibub Neto / Abhishek Pratap / Thanneer M. Perumal / Meghasyam Tummalacherla / Phil Snyder / Brian M. Bot / Andrew D. Trister / Stephen H. Friend / Lara Mangravite / Larsson Omberg

npj Digital Medicine, Vol 2, Iss 1, Pp 1-

2019 Volume 6

Abstract: Abstract Collection of high-dimensional, longitudinal digital health data has the potential to support a wide-variety of research and clinical applications including diagnostics and longitudinal health tracking. Algorithms that process these data and ... ...

Abstract	Abstract Collection of high-dimensional, longitudinal digital health data has the potential to support a wide-variety of research and clinical applications including diagnostics and longitudinal health tracking. Algorithms that process these data and inform digital diagnostics are typically developed using training and test sets generated from multiple repeated measures collected across a set of individuals. However, the inclusion of repeated measurements is not always appropriately taken into account in the analytical evaluations of predictive performance. The assignment of repeated measurements from each individual to both the training and the test sets (“record-wise” data split) is a common practice and can lead to massive underestimation of the prediction error due to the presence of “identity confounding.” In essence, these models learn to identify subjects, in addition to diagnostic signal. Here, we present a method that can be used to effectively calculate the amount of identity confounding learned by classifiers developed using a record-wise data split. By applying this method to several real datasets, we demonstrate that identity confounding is a serious issue in digital health studies and that record-wise data splits for machine learning- based applications need to be avoided.
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	006
Language	English
Publishing date	2019-10-01T00:00:00Z
Publisher	Nature Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Accelerometer data collected with a minimum set of wearable sensors from subjects with Parkinson’s disease

Scientific Data, Vol 8, Iss 1, Pp 1-

2021 Volume 13

Abstract: Measurement(s) body movement coordination trait • Movement Disorder Society Unified Parkinson’s Disease Rating Scale Questionnaire • Medication • motor coordination/balance trait • sleep pattern • MDS-UPDRS Tasks and Simulated Activities of Daily Living ( ...

Abstract	Measurement(s) body movement coordination trait • Movement Disorder Society Unified Parkinson’s Disease Rating Scale Questionnaire • Medication • motor coordination/balance trait • sleep pattern • MDS-UPDRS Tasks and Simulated Activities of Daily Living (in-clinic) • Activity of Daily Living Technology Type(s) Accelerometer • body movement/behavior method • Clinical Observation • smartphone • Subject Diary Factor Type(s) age of patient • gender of patient • timing of medication intake Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13342055
Keywords	Science ; Q
Language	English
Publishing date	2021-02-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Limb and trunk accelerometer data collected with wearable sensors from subjects with Parkinson’s disease

Scientific Data, Vol 8, Iss 1, Pp 1-

2021 Volume 12

Abstract	Measurement(s) body movement coordination trait • Movement Disorder Society Unified Parkinson’s Disease Rating Scale Questionnaire • Medication • motor coordination/balance trait • sleep pattern • MDS-UPDRS Tasks and Simulated Activities of Daily Living (in-clinic) • Activity of Daily Living Technology Type(s) Accelerometer • body movement/behavior method • Clinical Observation • smartphone • Subject Diary Factor Type(s) age of patient • gender of patient • timing of medication intake Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13574279
Keywords	Science ; Q
Language	English
Publishing date	2021-02-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Crowdsourcing digital health measures to predict Parkinson’s disease severity

Solveig K. Sieberts / Jennifer Schaff / Marlena Duda / Bálint Ármin Pataki / Ming Sun / Phil Snyder / Jean-Francois Daneault / Federico Parisi / Gianluca Costante / Udi Rubin / Peter Banda / Yooree Chae / Elias Chaibub Neto / E. Ray Dorsey / Zafer Aydın / Aipeng Chen / Laura L. Elo / Carlos Espino / Enrico Glaab /

Ethan Goan / Fatemeh Noushin Golabchi / Yasin Görmez / Maria K. Jaakkola / Jitendra Jonnagaddala / Riku Klén / Dongmei Li / Christian McDaniel / Dimitri Perrin / Thanneer M. Perumal / Nastaran Mohammadian Rad / Erin Rainaldi / Stefano Sapienza / Patrick Schwab / Nikolai Shokhirev / Mikko S. Venäläinen / Gloria Vergara-Diaz / Yuqian Zhang / the Parkinson’s Disease Digital Biomarker Challenge Consortium / Yuanjia Wang / Yuanfang Guan / Daniela Brunner / Paolo Bonato / Lara M. Mangravite / Larsson Omberg

npj Digital Medicine, Vol 4, Iss 1, Pp 1-

the Parkinson’s Disease Digital Biomarker DREAM Challenge

2021 Volume 12

Abstract: Abstract Consumer wearables and sensors are a rich source of data about patients’ daily disease and symptom burden, particularly in the case of movement disorders like Parkinson’s disease (PD). However, interpreting these complex data into so-called ... ...

Abstract	Abstract Consumer wearables and sensors are a rich source of data about patients’ daily disease and symptom burden, particularly in the case of movement disorders like Parkinson’s disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95).
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	006
Language	English
Publishing date	2021-03-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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