| Application: | 42107, Using wearable and health data to model and classify individual health risk |
| Title: | Defying the Circadian Rhythm: Clustering Participant Telemetry in the UK Biobank Data |
| Size: | 759259 B |
| Archived: | 1 Sep 2021 |
| Stability: | Complete |
| Personal: | No individual-level data |
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Notes
The UK Biobank dataset follows over 500,000 volunteers and contains a diverse set of information related to societal outcomes. Among this vast collection, a large quantity of telemetry collected from wrist-worn accelerometers provides a snapshot of participant activity. Using this data, a population of shift workers, subjected to disrupted circadian rhythms, is analysed using a mixture model-based approach to yield protective effects from physical activity on survival outcomes. In this paper, we develop a scalable, standardized, and unique methodology that efficiently clusters a vast quantity of participant telemetry. By building upon the work of Doherty et al. (2017), we introduce a standardized, low-dimensional feature for clustering purposes. Participants are clustered using a matrix variate mixture model-based approach. Once clustered, survival analysis is performed to demonstrate distinct lifetime outcomes for individuals within each cluster. In summary, we process, cluster, and analyse a subset of UK Biobank participants to show the protective effects from physical activity on circadian disrupted individuals.Application 42107
Using wearable and health data to model and classify individual health risk
An increase in the amount of individual-level health data, including that from wearable devices provides the opportunity to classify risk and predict the occurrence of adverse health events, therefore allowing preventative interventions to be taken. This study will use health and accelerometer data to both classify risks and predict adverse health outcomes, through the use of advanced machine learning algorithms. These models have several applications across the private and public sector, with this study focusing primarily on individual understanding of risk , adverse-health event avoidance and hospital readmission prevention.
| Lead investigator: | Dr Mark Farrell |
| Lead institution: | Queen's University Belfast |
Enabling scientific discoveries that improve human health