Document Type

Conference Proceeding

Abstract

kHealth-Asthma, a personalised digital healthcare framework is developed to address the above shortcomings by continuous monitoring of the child’s digital phenotype, indoor, and outdoor environmental data. The kHealth-Asthma study has recruited 140 children (ongoing) with an aim to complete recruitment of 150 children. The study period is either 1 month or 3 month depending on the choice of the study participant. kHealth-Asthma collects 29 multi-modal parameters leading to 1852 data points per patient per day (i.e. deployment: 1 month:1852*30=55,560 data points per patient and 3 month:1852*90=166,680 data points per patient). The digital phenotype collected using the kHealth-Asthma generates a Digital Phenotype Score (DPS) which is a cumulative measure of a child’s health and well-being [Jain et al.2015, Jaimini et al.2018]. The generated DPS is clinically equivalent to the Asthma Control Test Score calculated during the clinical visits. The DPS can generate personalized actionable insights into a child’s health condition which can be used by the clinician for possible future interventions.Furthermore, the data collected from the kHealth-Asthma is being used to a) Develop a Personalized Bayesian Prediction framework to predict the future occurrences of a child’s asthma symptom(s) with the onset of asthma trigger(s), b) Using a Personalized Causal Model understand the cause and effect relationship between a child’s asthma symptom(s) and multiple co-occurring asthma trigger(s), c) Generate a Child’s Health coefficient as a measure of an overall health condition of the child with the onset of asthma trigger(s).

APA Citation

Jaimini, U., Sheth, A., Thirunarayan, K., Kalra, M., & Valtorta, M. (2020). Is it safe for my child's asthma? 6th International Conference on Computational Social Science, IC2S2.

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