Public health and safety professionals, including firefighters, police officers, and emergency medical personnel, serve as critical pillars of public safety and societal well-being. These professions require navigating environments characterized by ‘brittleness’, ‘anxiety’, ‘nonlinearity’, and ‘incomprehensibility’—conditions that lead to chronic physical and psychological stress. This stress significantly elevates the risk of mental health disorders such as post-traumatic stress disorder, anxiety disorders, mood disorders, and sleep disturbances, while also increasing the likelihood of human errors driven by cognitive lapses. Such challenges extend beyond individual health, undermining organizational efficiency and ultimately jeopardizing public safety and societal welfare. Existing mental health management systems predominantly depend on reactive interventions, which are insufficient to meet the dynamic and unpredictable demands of high-risk occupational environments. As a proactive alternative, this paper introduces the concept of ‘Mind Vital Signs,’ an innovative framework that expands the traditional concept of vital signs into the mental health domain. Mind Vital Signs integrates physiological indicators—including heart rate variability, physical activity, respiratory rates, and sleep patterns—with psychological and behavioral data such as ecological momentary assessments and life logs. By employing real-time monitoring and advanced analytics, this multidimensional system facilitates early detection of mental health risks and supports targeted and timely preventive interventions. The implementation of Mind Vital Signs not only bolsters individual resilience and organizational stability but also enhances operational efficiency and strengthens public safety and societal well-being. Future research should prioritize clinical validation and policy development to ensure the effective integration and scalability of Mind Vital Signs in high-risk occupational settings.

Public health and safety professionals, including firefighters, police officers, and emergency medical personnel, serve as critical pillars of public safety and societal well-being. These professions require navigating environments characterized by ‘brittleness’, ‘anxiety’, ‘nonlinearity’, and ‘incomprehensibility’—conditions that lead to chronic physical and psychological stress. This stress significantly elevates the risk of mental health disorders such as post-traumatic stress disorder, anxiety disorders, mood disorders, and sleep disturbances, while also increasing the likelihood of human errors driven by cognitive lapses. Such challenges extend beyond individual health, undermining organizational efficiency and ultimately jeopardizing public safety and societal welfare. Existing mental health management systems predominantly depend on reactive interventions, which are insufficient to meet the dynamic and unpredictable demands of high-risk occupational environments. As a proactive alternative, this paper introduces the concept of ‘Mind Vital Signs,’ an innovative framework that expands the traditional concept of vital signs into the mental health domain. Mind Vital Signs integrates physiological indicators—including heart rate variability, physical activity, respiratory rates, and sleep patterns—with psychological and behavioral data such as ecological momentary assessments and life logs. By employing real-time monitoring and advanced analytics, this multidimensional system facilitates early detection of mental health risks and supports targeted and timely preventive interventions. The implementation of Mind Vital Signs not only bolsters individual resilience and organizational stability but also enhances operational efficiency and strengthens public safety and societal well-being. Future research should prioritize clinical validation and policy development to ensure the effective integration and scalability of Mind Vital Signs in high-risk occupational settings.

Public health and safety professionals, including firefighters, police officers, and emergency medical personnel, serve as critical pillars of public safety and societal well-being. These professions require navigating environments characterized by ‘brittleness’, ‘anxiety’, ‘nonlinearity’, and ‘incomprehensibility’—conditions that lead to chronic physical and psychological stress. This stress significantly elevates the risk of mental health disorders such as post-traumatic stress disorder, anxiety disorders, mood disorders, and sleep disturbances, while also increasing the likelihood of human errors driven by cognitive lapses. Such challenges extend beyond individual health, undermining organizational efficiency and ultimately jeopardizing public safety and societal welfare. Existing mental health management systems predominantly depend on reactive interventions, which are insufficient to meet the dynamic and unpredictable demands of high-risk occupational environments. As a proactive alternative, this paper introduces the concept of ‘Mind Vital Signs,’ an innovative framework that expands the traditional concept of vital signs into the mental health domain. Mind Vital Signs integrates physiological indicators—including heart rate variability, physical activity, respiratory rates, and sleep patterns—with psychological and behavioral data such as ecological momentary assessments and life logs. By employing real-time monitoring and advanced analytics, this multidimensional system facilitates early detection of mental health risks and supports targeted and timely preventive interventions. The implementation of Mind Vital Signs not only bolsters individual resilience and organizational stability but also enhances operational efficiency and strengthens public safety and societal well-being. Future research should prioritize clinical validation and policy development to ensure the effective integration and scalability of Mind Vital Signs in high-risk occupational settings.

Public health and safety professionals, including firefighters, police officers, and emergency medical personnel, serve as critical pillars of public safety and societal well-being. These professions require navigating environments characterized by ‘brittleness’, ‘anxiety’, ‘nonlinearity’, and ‘incomprehensibility’—conditions that lead to chronic physical and psychological stress. This stress significantly elevates the risk of mental health disorders such as post-traumatic stress disorder, anxiety disorders, mood disorders, and sleep disturbances, while also increasing the likelihood of human errors driven by cognitive lapses. Such challenges extend beyond individual health, undermining organizational efficiency and ultimately jeopardizing public safety and societal welfare. Existing mental health management systems predominantly depend on reactive interventions, which are insufficient to meet the dynamic and unpredictable demands of high-risk occupational environments. As a proactive alternative, this paper introduces the concept of ‘Mind Vital Signs,’ an innovative framework that expands the traditional concept of vital signs into the mental health domain. Mind Vital Signs integrates physiological indicators—including heart rate variability, physical activity, respiratory rates, and sleep patterns—with psychological and behavioral data such as ecological momentary assessments and life logs. By employing real-time monitoring and advanced analytics, this multidimensional system facilitates early detection of mental health risks and supports targeted and timely preventive interventions. The implementation of Mind Vital Signs not only bolsters individual resilience and organizational stability but also enhances operational efficiency and strengthens public safety and societal well-being. Future research should prioritize clinical validation and policy development to ensure the effective integration and scalability of Mind Vital Signs in high-risk occupational settings.

Public health and safety professionals, including firefighters, police officers, and emergency medical personnel, serve as critical pillars of public safety and societal well-being. These professions require navigating environments characterized by ‘brittleness’, ‘anxiety’, ‘nonlinearity’, and ‘incomprehensibility’—conditions that lead to chronic physical and psychological stress. This stress significantly elevates the risk of mental health disorders such as post-traumatic stress disorder, anxiety disorders, mood disorders, and sleep disturbances, while also increasing the likelihood of human errors driven by cognitive lapses. Such challenges extend beyond individual health, undermining organizational efficiency and ultimately jeopardizing public safety and societal welfare. Existing mental health management systems predominantly depend on reactive interventions, which are insufficient to meet the dynamic and unpredictable demands of high-risk occupational environments. As a proactive alternative, this paper introduces the concept of ‘Mind Vital Signs,’ an innovative framework that expands the traditional concept of vital signs into the mental health domain. Mind Vital Signs integrates physiological indicators—including heart rate variability, physical activity, respiratory rates, and sleep patterns—with psychological and behavioral data such as ecological momentary assessments and life logs. By employing real-time monitoring and advanced analytics, this multidimensional system facilitates early detection of mental health risks and supports targeted and timely preventive interventions. The implementation of Mind Vital Signs not only bolsters individual resilience and organizational stability but also enhances operational efficiency and strengthens public safety and societal well-being. Future research should prioritize clinical validation and policy development to ensure the effective integration and scalability of Mind Vital Signs in high-risk occupational settings.

Background: Surgical resection is the standard treatment for early-stage lung cancer. Since postoperative lung function is related to mortality, predicted postoperative lung function is used to determine the treatment modality. The aim of this study was to evaluate the predictive performance of linear regression and machine learning models. Methods: We extracted data from the Clinical Data Warehouse and developed three sets: set I, the linear regression model; set II, machine learning models omitting the missing data: and set III, machine learning models imputing the missing data. Six machine learning models, the least absolute shrinkage and selection operator (LASSO), Ridge regression, ElasticNet, Random Forest, eXtreme gradient boosting (XGBoost), and the light gradient boosting machine (LightGBM) were implemented. The forced expiratory volume in 1 second measured 6 months after surgery was defined as the outcome. Five-fold cross-validation was performed for hyperparameter tuning of the machine learning models. The dataset was split into training and test datasets at a 70:30 ratio. Implementation was done after dataset splitting in set III. Predictive performance was evaluated by R2 and mean squared error (MSE) in the three sets. Results: A total of 1,487 patients were included in sets I and III and 896 patients were included in set II. In set I, the R2 value was 0.27 and in set II, LightGBM was the best model with the highest R2 value of 0.5 and the lowest MSE of 154.95. In set III, LightGBM was the best model with the highest R2 value of 0.56 and the lowest MSE of 174.07. Conclusion The LightGBM model showed the best performance in predicting postoperative lung function.