Background Vital signs have been widely adopted in in-hospital cardiac arrest (IHCA) assessment, which plays an important role in inpatient deterioration detection. As the number of early warning systems and artificial intelligence applications increases, health care information exchange and interoperability are becoming more complex and difficult. Although Health Level 7 Fast Healthcare Interoperability Resources (FHIR) have already developed a vital signs profile, it is not sufficient to support IHCA applications or machine learning–based models. Objective In this paper, for IHCA instances with vital signs, we define a new implementation guide that includes data mapping, a system architecture, a workflow, and FHIR applications. Methods We interviewed 10 experts regarding health care system integration and defined an implementation guide. We then developed the FHIR Extract Transform Load to map data to FHIR resources. We also integrated an early warning system and machine learning pipeline. Results The study data set includes electronic health records of adult inpatients who visited the En-Chu-Kong hospital. Medical staff regularly measured these vital signs at least 2 to 3 times per day during the day, night, and early morning. We used pseudonymization to protect patient privacy. Then, we converted the vital signs to FHIR observations in the JSON format using the FHIR Extract Transform Load application. The measured vital signs include systolic blood pressure, diastolic blood pressure, heart rate, respiratory rate, and body temperature. According to clinical requirements, we also extracted the electronic health record information to the FHIR server. Finally, we integrated an early warning system and machine learning pipeline using the FHIR RESTful application programming interface. Conclusions We successfully demonstrated a process that standardizes health care information for inpatient deterioration detection using vital signs. Based on the FHIR definition, we also provided an implementation guide that includes data mapping, an integration process, and IHCA assessment using vital signs. We also proposed a clarifying system architecture and possible workflows. Based on FHIR, we integrated the 3 different systems in 1 dashboard system, which can effectively solve the complexity of the system in the medical staff workflow.

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.

BACKGROUND Vital signs have been widely adopted in in-hospital cardiac arrest (IHCA) assessment, which plays an important role in inpatient deterioration detection. As the number of early warning systems and artificial intelligence applications increases, healthcare information exchange and interoperability are becoming more complex and difficult. Although Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) has already developed a vital signs profile, it is not sufficient to support IHCA applications or machine learning-based models. OBJECTIVE For IHCA instances with vital signs, we define a new implementation guide that includes data mapping, a system architecture, a workflow, and FHIR applications. METHODS We interviewed ten experts regarding healthcare system integration and defined an implementation guide. We then developed the FHIR Extract-Transform-Load (ETL) to map data to FHIR resources. We also integrated an early warning system and machine learning pipeline. RESULTS The study data set include electronic health records (EHRs) of adult inpatients who visited the En-Chu-Kong hospital. Medical staff regularly measured these vital signs at least two to three times per day during the day, night, and early morning. We used pseudonymization to protect patient privacy. Then, we converted the vital signs to FHIR observations in the JSON format using FHIR ETL. The measured vital signs include the systolic blood pressure, diastolic blood pressure, heart rate, respiratory rate, and body temperature. According to clinical requirements, we also extracted the EHR information to the FHIR server. Finally, we integrated an early warning system and machine learning pipeline using the FHIR RESTful API. CONCLUSIONS We successfully demonstrated a process that standardizes healthcare information for inpatient deterioration detection using vital signs. Based on the FHIR definition, we also provide an implementation guide that includes data mapping, an integration process, and IHCA using vital signs. We also propose a clarifying system architecture and possible workflows.

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).