A Precision Health Service for Chronic Diseases: Development and Cohort Study Using Wearable Device, Machine Learning, and Deep Learning

Abstract

This paper presents an integrated and scalable precision health service for health promotion and chronic disease prevention. Continuous real-time monitoring of lifestyle and environmental factors is implemented by integrating wearable devices, open environmental data, indoor air quality sensing devices, a location-based smartphone app, and an AI-assisted telecare platform. The AI-assisted telecare platform provided comprehensive insight into patients' clinical, lifestyle, and environmental data, and generated reliable predictions of future acute exacerbation events. All data from 1,667 patients were collected prospectively during a 24-month follow-up period, resulting in the detection of 386 abnormal episodes. Machine learning algorithms and deep learning algorithms were used to train modular chronic disease models. The modular chronic disease prediction models that have passed external validation include obesity, panic disorder, and chronic obstructive pulmonary disease, with an average accuracy of 88.46%, a sensitivity of 75.6%, a specificity of 93.0%, and an F1 score of 79.8%. Compared with previous studies, we establish an effective way to collect lifestyle, life trajectory, and symptom records, as well as environmental factors, and improve the performance of the prediction model by adding objective comprehensive data and feature selection. Our results also demonstrate that lifestyle and environmental factors are highly correlated with patient health and have the potential to predict future abnormal events better than using only questionnaire data. Furthermore, we have constructed a cost-effective model that needs only a few features to support the prediction task, which is helpful for deploying real-world modular prediction models.

Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review

“…Wrapper-based feature selection can be completed by ranking the features in terms of relative importance using a ML model (such as decision trees or random forests) [88,101,113]. We identified a handful of feature ranking methods that include two stepwise regression techniques: Forward Selection and Backwards Elimination [29,36,52,[114][115][116], as well as Recursive Feature Selection (RFE) [30,117]. Forward selection starts the modelling process with zero features and adds a new feature to the model incrementally, each time testing for statistical significance.…”

“…When similar hyperparameter tuning processes can be used for different ML algorithms for different datasets, researchers can then identify the optimal ML model. Among the selected studies, 25 discussed which hyperparameters were considered for their models [23,24,34,43,44,46,53,69,73,86,87,94,95,[107][108][109][110]114,138,158,159,[181][182][183][184], of which one stated they used the default hyperparameters of the models [69]. Only nine studies discussed how they selected or optimized their hyperparameters.…”

Enhanced Detection of Chronic Heart Disease: A Novel Approach With Extreme Gradient Boosting-Logistic Regression Algorithm

“…They offer accurate, measurable precision medicine to improve health and prevent chronic disease. Similarly, smartphone app integration enables real-time monitoring of lifestyle and environmental parameters [8]. Therefore, the OCI-DBN method may compare the trial findings with other cuttingedge methods to enhance the system's performance [9].…”

Application of wearable devices for monitoring cardiometabolic dysfunction under the exposome paradigm

“…Several studies have suggested that wearable devices can provide more reliable, detailed data on human activities and exposure characteristics, 4,5 providing an effective way to assess health risks and facilitate further interventions 6,7 . For example, wearable devices can monitor health indicators such as weight, fat, blood sugar, and blood pressure to detect the risks of metabolic diseases early and help patients effectively control their condition.…”

“…Several studies have suggested that wearable devices can provide more reliable, detailed data on human activities and exposure characteristics, 4 , 5 providing an effective way to assess health risks and facilitate further interventions. 6 , 7 For example, wearable devices can monitor health indicators such as weight, fat, blood sugar, and blood pressure to detect the risks of metabolic diseases early and help patients effectively control their condition. Moreover, these devices can deliver real‐time condition data of patient changes and identify potential risks in a timely manner to provide clinical doctors with customized treatment plans to effectively improve their patients’ health.…”