The digital biomarker discovery pipeline: An open-source software platform for the development of digital biomarkers using mHealth and wearables data

Bent, Brinnae; Wang, Ke; Grzesiak, Emilia; Jiang, Chentian; Qi, Yuankai; Jiang, Yihang; Cho, Peter; Zingler, Kyle; Ogbeide, Felix Ikponmwosa; Zhao, Arthur; Runge, Ryan; Sim, Ida; Dunn, Jessilyn

doi:10.1017/cts.2020.511

Cited by 63 publications

(46 citation statements)

References 21 publications

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“…This structure will allow for infinite growth of technology in whatever direction is needed for a given species because digital phenotyping is a dynamic field of study [8]. With proper information protection, the acceptance of a shared data system could also improve efficiency, standardization, and welfare, much as it has for hospital data systems [12,53]. This shared information could help catalyze evaluations of geographical or global complications within a species, and it even opens up the opportunity for genetically diverse and data-backed breeding programs.…”

Section: New Phenotyping Platforms For Accurate Assessment Of Livestockmentioning

confidence: 99%

Digital Phenotyping in Livestock Farming

Neethirajan

Kemp

2021

Animals

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Currently, large volumes of data are being collected on farms using multimodal sensor technologies. These sensors measure the activity, housing conditions, feed intake, and health of farm animals. With traditional methods, the data from farm animals and their environment can be collected intermittently. However, with the advancement of wearable and non-invasive sensing tools, these measurements can be made in real-time for continuous quantitation relating to clinical biomarkers, resilience indicators, and behavioral predictors. The digital phenotyping of humans has drawn enormous attention recently due to its medical significance, but much research is still needed for the digital phenotyping of farm animals. Implications from human studies show great promise for the application of digital phenotyping technology in modern livestock farming, but these technologies must be directly applied to animals to understand their true capacities. Due to species-specific traits, certain technologies required to assess phenotypes need to be tailored efficiently and accurately. Such devices allow for the collection of information that can better inform farmers on aspects of animal welfare and production that need improvement. By explicitly addressing farm animals’ individual physiological and mental (affective states) needs, sensor-based digital phenotyping has the potential to serve as an effective intervention platform. Future research is warranted for the design and development of digital phenotyping technology platforms that create shared data standards, metrics, and repositories.

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Section: New Phenotyping Platforms For Accurate Assessment Of Livestockmentioning

confidence: 99%

Digital Phenotyping in Livestock Farming

Neethirajan

Kemp

2021

Animals

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“…Indeed, there is a growing interest in research to identify data-driven biomarkers [ 83 , 101 ]. More recently termed as digital biomarkers, these data-driven indices have unique advantages beyond traditional biomarkers, such as analysis at both the individual and population level, longitudinal and continuous measures, and passive monitoring [ 83 ].…”

Section: Digital Biomarkers Of Cognitive Fatigue Through Wearables and Machine Learningmentioning

confidence: 99%

“…More recently termed as digital biomarkers, these data-driven indices have unique advantages beyond traditional biomarkers, such as analysis at both the individual and population level, longitudinal and continuous measures, and passive monitoring [ 83 ]. More importantly, the emergence and increasing prevalence of wearables with the capability to measure physiological data allows for the further development of putative physiological-based digital biomarkers [ 101 ]. These wearables are capable of collecting physiological data, such as blood oxygen saturation, blood pressure, body temperature, electrodermal activity, and heart rate [ 102 ].…”

Section: Digital Biomarkers Of Cognitive Fatigue Through Wearables and Machine Learningmentioning

confidence: 99%

Biomarker-Informed Machine Learning Model of Cognitive Fatigue from a Heart Rate Response Perspective

Lee

Gan

Christopoulos

2021

Sensors

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Cognitive fatigue is a psychological state characterised by feelings of tiredness and impaired cognitive functioning arising from high cognitive demands. This paper examines the recent research progress on the assessment of cognitive fatigue and provides informed recommendations for future research. Traditionally, cognitive fatigue is introspectively assessed through self-report or objectively inferred from a decline in behavioural performance. However, more recently, researchers have attempted to explore the biological underpinnings of cognitive fatigue to understand and measure this phenomenon. In particular, there is evidence indicating that the imbalance between sympathetic and parasympathetic nervous activity appears to be a physiological correlate of cognitive fatigue. This imbalance has been indexed through various heart rate variability indices that have also been proposed as putative biomarkers of cognitive fatigue. Moreover, in contrast to traditional inferential methods, there is also a growing research interest in using data-driven approaches to assessing cognitive fatigue. The ubiquity of wearables with the capability to collect large amounts of physiological data appears to be a major facilitator in the growth of data-driven research in this area. Preliminary findings indicate that such large datasets can be used to accurately predict cognitive fatigue through various machine learning approaches. Overall, the potential of combining domain-specific knowledge gained from biomarker research with machine learning approaches should be further explored to build more robust predictive models of cognitive fatigue.

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“…Methods used in this study have been made publicly available in the Digital Biomarker Discovery Pipeline (DBDP) to promote reproducibility. 43 Methods for extracting glucose variability metrics from a CGM are available in both Python and R in the cgmquantify module of the DBDP. The feature engineering methods for wearable sensors can be found in the wearablevar module of the DBDP.…”

Section: Statistical Analysesmentioning

confidence: 99%

Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept

Bent

Cho

Wittmann

et al. 2021

BMJ Open Diab Res Care

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IntroductionDiabetes prevalence continues to grow and there remains a significant diagnostic gap in one-third of the US population that has pre-diabetes. Innovative, practical strategies to improve monitoring of glycemic health are desperately needed. In this proof-of-concept study, we explore the relationship between non-invasive wearables and glycemic metrics and demonstrate the feasibility of using non-invasive wearables to estimate glycemic metrics, including hemoglobin A1c (HbA1c) and glucose variability metrics.Research design and methodsWe recorded over 25 000 measurements from a continuous glucose monitor (CGM) with simultaneous wrist-worn wearable (skin temperature, electrodermal activity, heart rate, and accelerometry sensors) data over 8–10 days in 16 participants with normal glycemic state and pre-diabetes (HbA1c 5.2–6.4). We used data from the wearable to develop machine learning models to predict HbA1c recorded on day 0 and glucose variability calculated from the CGM. We tested the accuracy of the HbA1c model on a retrospective, external validation cohort of 10 additional participants and compared results against CGM-based HbA1c estimation models.ResultsA total of 250 days of data from 26 participants were collected. Out of the 27 models of glucose variability metrics that we developed using non-invasive wearables, 11 of the models achieved high accuracy (<10% mean average per cent error, MAPE). Our HbA1c estimation model using non-invasive wearables data achieved MAPE of 5.1% on an external validation cohort. The ranking of wearable sensor’s importance in estimating HbA1c was skin temperature (33%), electrodermal activity (28%), accelerometry (25%), and heart rate (14%).ConclusionsThis study demonstrates the feasibility of using non-invasive wearables to estimate glucose variability metrics and HbA1c for glycemic monitoring and investigates the relationship between non-invasive wearables and the glycemic metrics of glucose variability and HbA1c. The methods used in this study can be used to inform future studies confirming the results of this proof-of-concept study.

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The digital biomarker discovery pipeline: An open-source software platform for the development of digital biomarkers using mHealth and wearables data

Cited by 63 publications

References 21 publications

Digital Phenotyping in Livestock Farming

Digital Phenotyping in Livestock Farming

Biomarker-Informed Machine Learning Model of Cognitive Fatigue from a Heart Rate Response Perspective

Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept

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