Using Consumer-Wearable Activity Trackers for Risk Prediction of Life-Threatening Heart Arrhythmia in Patients with an Implantable Cardioverter-Defibrillator: An Exploratory Observational Study

Frodi, Diana My; Manea, Vlad; Diederichsen, Søren Zöga; Svendsen, Jesper Hastrup; Wac, Katarzyna; Andersen, Tariq Osman

doi:10.3390/jpm12060942

Cited by 2 publications

(1 citation statement)

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“…Such information is rich in detail, not just for the isolated values of each measured variable but for the link between all the measurements collected at one moment and their temporal dimension and dependency across the measurements. For example, by continuously monitoring the heart's rhythm for months, it is possible to detect cardiovascular issues early or forecast the occurrence of arrhythmia events [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Dejà vu: Recurrent Neural Networks for health wearables data forecast

Matias

Wac

2022

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)

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Wearable devices are a useful and widely used source of continuous and temporal dependant data. In contrast to the traditional clinical environment, these devices allow time series data collection in an individual's daily living environment. However, missing data can occur while using them. Many techniques have been applied to solve these data gaps; nonetheless, missing time series data poses extra challenges, such as maintaining the temporal dependency. In this article, we addressed the forecast of sleep trackers data (sleeping heart rate (HR) and time asleep) for 2 main reasons: (1) to design models capable of accurately forecasting missing data from those devices, and (2) to apply those models to empower sleep interventions that may increase its quality, by forecasting future sleep events. We collected wearables data over 290 days (per individual) from 12 participants using a smartwatch and made this dataset publicly available. We then explored several hyperparameters of 2 Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). We further elaborated and compared the performance of 3 approaches to training those RNNs. Although similar performance, slightly more accurate results were obtained after training a GRU network on an entire population's dataset, which was able to forecast the average, minimum, and maximum sleeping HR with a root-mean-squared error (RMSE) of 4.4 (± 1.4), 4.9 (± 2.6), and 12.1 (± 4.0) beats per minute, respectively. However, the total time asleep was impossible to forecast with low error.

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Section: Introductionmentioning

confidence: 99%

Dejà vu: Recurrent Neural Networks for health wearables data forecast

Matias

Wac

2022

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)

Self Cite

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Risk and Uncertainty Communication in Deployed AI-based Clinical Decision Support Systems: A scoping review

Gray,

Page,

Buchan

et al. 2024

Preprint

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Clinical decision support systems (CDSS) employing data-driven technology such as artificial intelligence, machine- and statistical-learning are increasingly deployed in healthcare settings. These systems often provide clinicians with diagnostic, prognostic, or risk scores modelled from curated patient-level data and frequently involve iterative and non-deterministic optimisation of flexible, parameterised models. All of these data and algorithms have uncertainties associated with them that should be taken into account when used to support clinical decisions at the patient level. This scoping review aims to describe the literature on how deployed data-driven CDSSs present information about uncertainty to their intended users. We describe common clinical applications of CDSSs, characterise the decisions that are being supported, and examine how the CDSS provides outputs to end users, including uncertainty at the individual patient level, as well as indirect measures such as CDSS performance against a reference standard. We conclude with a discussion and recommendations on how CDSS development can be improved.

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Using Consumer-Wearable Activity Trackers for Risk Prediction of Life-Threatening Heart Arrhythmia in Patients with an Implantable Cardioverter-Defibrillator: An Exploratory Observational Study

Cited by 2 publications

References 46 publications

Dejà vu: Recurrent Neural Networks for health wearables data forecast

Dejà vu: Recurrent Neural Networks for health wearables data forecast

Risk and Uncertainty Communication in Deployed AI-based Clinical Decision Support Systems: A scoping review

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