Articles you may be interested inQuantum well electron scattering rates through longitudinal optic-phonon dynamical screened interaction: An analytic approach

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.

Discretization algorithms have been combined with discrete estimation of distribution algorithms (EDAs) to work on real-valued problems. Existing discretization algorithms, such as the fixed-height histogram (FHH) and the split-on-demand (SoD), utilize merely densities of selected chromosomes to build nextgeneration population, and therefore have limited exploration. This paper adds the concept of model building to FHH and SoD to solve these problems. The model utilizes a variety of information from selected chromosomes to improve the abilities of FHH and SoD to identify promising regions for future exploration. Specifically, a model of expected values of selected chromosomes is combined with FHH and SoD to form expected-value FHH and expected-value SoD. The expected-value-discretization algorithms outperform their original versions on an exploration test function as well as the 25 benchmark functions used in the SoD paper. This paper also introduces a model of differential-expected-value of selected chromosomes. The differential-expected-value FHH and differential-expected-value SoD outperform their expected-value versions when tested on the exploration test function and the 25 benchmark functions.

BACKGROUND The dramatic increase in adolescent obesity is a serious public health crisis in the world. The World Health Organization has projected that by 2030, adolescent obesity will reach 254 million children worldwide. Increasing evidences show that obesity in adolescence would increase the risk of type 2 diabetes and cardiovascular disease in adulthood. A prediction model for adolescent obesity could help clinicians and adolescents monitor, control, and identify risk factors before children become overweight, enabling more personalized healthy lifestyle improvement for adolescent obesity. OBJECTIVE This study aims to develop a risk prediction model for adolescents using lifestyle factors, living environment data, and health literacy for the prediction of becoming overweight and obese in the upcoming month, and to explore living environment and lifestyle factors that may predispose youth to overweight and obesity. METHODS This prospective study was conducted at National Taiwan University Hospital. Parents and eligible adolescents were enrolled in the study. Living environment and lifestyle factors were collected by a wearable device, a smartphone app, the open environmental data API, and a case management platform. Standardized questionnaires were designed to evaluate the health literacy value of adolescents. To analyze the large amounts of heterogeneous data, we implemented six machine learning models: Random Forest, Decision Tree, SVM, KNN, LDA, and AdaBoost, and used Shapely Additive exPlanations and feature selection process to find the most cost-effective feature set to account for the problem of incomplete data in the real world. RESULTS All data from 120 adolescents were collected prospectively during a mean 1-year follow-up. For the risk prediction, the proposed model produced the best performance an accuracy of 94.3%, precision of 99,9%, and F1 score of 78.8%. Overall, the accuracy of the test set was 81.6%-94.3% for six machine learning algorithms. After the process of feature selection, the combination of daily consumption in calories, health literacy value, average heart rate and minimum heart rate was identified as the most cost-effective feature set. The purposed model with only these four features could achieve an accuracy of 93.7%, sensitivity of 71%, precision of 88.8%, and F1 score of 78.6%. CONCLUSIONS In contrast with previous existing studies, the proposed model could yield reliable prediction of the risk of becoming overweight and obesity in adolescent by adding objective lifestyle and environmental data. Our results indicated that lower values for features such as health literacy, consumption in calories, average heart rate, and rapid eye movement time would increase the risk of becoming overweight and obese. This information would help adolescents understand exactly how to improve their lifestyle and health outcomes. Furthermore, we have constructed the most cost-effective model that only needs four features to complete the prediction task, which is very helpful for deploying the risk prediction model in real life. CLINICALTRIAL The study protocol was approved by the institutional review board of the National Taiwan University Hospital (201710066RINB).