Prediction of depressive symptoms onset and long-term trajectories in home-based older adults using machine learning techniques.

OBJECTIVES: Our aim was to explore the possibility of using machine learning (ML) in predicting the onset and trajectories of depressive symptom in home-based older adults over a 7-year period. METHODS: Depressive symptom data (collected in the year 2011, 2013, 2015 and 2018) of home-based older Chinese (n = 2650) recruited in the China Health and Retirement Longitudinal Study (CHARLS) were included in the current analysis. The latent class growth modeling (LCGM) and growth mixture modeling (GMM) were used to classify different trajectory classes. Based on the identified trajectory patterns, three ML classification algorithms (i.e. gradient boosting decision tree, support vector machine and random forest) were evaluated with a 10-fold cross-validation procedure and a metric of the area under the receiver operating characteristic curve (AUC). RESULTS: Four trajectories were identified for the depressive symptoms: no symptoms (63.9%), depressive symptoms onset {incident increasing symptoms [new-onset increasing (16.8%)], chronic symptoms [slowly decreasing (12.5%), persistent high (6.8%)]}. Among the analyzed baseline variables, the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10) score, cognition, sleep time, self-reported memory were the top five important predictors across all trajectories. The mean AUCs of the three predictive models had a range from 0.661 to 0.892. CONCLUSIONS: ML techniques can be robust in predicting depressive symptom onset and trajectories over a 7-year period with easily accessible sociodemographic and health information.Supplemental data for this article is available online at http://dx.doi.org/10.1080/13607863.2022.2031868.

A hybrid machine learning model of depression estimation in home-based older adults: a 7-year follow-up study.

BACKGROUND: Our aim was to explore whether a two-step hybrid machine learning model has the potential to discover the onset of depression in home-based older adults. METHODS: Depression data (collected in the year 2011, 2013, 2015 and 2018) of home-based older Chinese (n = 2,548) recruited in the China Health and Retirement Longitudinal Study were included in the current analysis. The long short-term memory network (LSTM) was applied to identify the risk factors of participants in 2015 utilizing the first 2 waves of data. Based on the identified predictors, three ML classification algorithms (i.e., gradient boosting decision tree, support vector machine and random forest) were evaluated with a 10-fold cross-validation procedure and a metric of the area under the receiver operating characteristic curve (AUROC) to estimate the depressive outcome. RESULTS: Time-varying predictors of the depression were successfully identified by LSTM (mean squared error =0.8). The mean AUCs of the three predictive models had a range from 0.703 to 0.749. Among the prediction variables, self-reported health status, cognition, sleep time, self-reported memory and ADL (activities of daily living) disorder were the top five important variables. CONCLUSIONS: A two-step hybrid model based on "LSTM+ML" framework can be robust in predicting depression over a 5-year period with easily accessible sociodemographic and health information.

EPIDEMIOLOGIC CHARACTERISTICS AND RISK FACTORS FOR CONGENITAL HYPOTHYROIDISM FROM 2009 TO 2018 IN XIAMEN, CHINA.

Objective: Early diagnosis and treatment of children with congenital hypothyroidism (CH) through newborn screening can effectively prevent delayed development. This study was designed to investigate the pathogenesis and factors that influence CH in urban areas of China between 2009 and 2018. Methods: A retrospective analysis of newborn screening data and diagnosis and treatment information for CH diagnosed in the information database of the neonatal disease screening center in one of China's five special economic zones from 2009 to 2018. Results: Of the 947,258 newborns screened between 2009 and 2018, 829 (406 girls) were diagnosed with CH at birth (1 diagnosis/1,136 births). Among the 608 cases of CH diagnosed at birth and re-evaluated at the age of 3 years, 487 were permanent congenital hypothyroidism (PCH, 1/1,429), and 121 were transient congenital hypothyroidism (TCH, 1/5,882). A total of 83.2% of infants with PCH (405/487) underwent thyroid imaging in the neonatal period, of which thyroid dysgenesis accounted for 28.64% (116/405) and functional defects accounted for 71.36% (289/405). The incidence of CH changed significantly in infants with initial serum thyroid-stimulating hormone concentrations of 41 to 100 mIU/L and ≥100 mIU/L, whereas the incidence of mild CH showed a slight increase. The incidence of CH was significantly higher in postterm infants (1/63) and low-birth-weight infants (1/370). Conclusion: In the past decade, the incidence of CH has increased, mainly due to the increase in the incidence of PCH and TCH. The incidence of mild CH has increased slightly. Postterm birth and low birth weight are important factors affecting the incidence of CH. Abbreviations: CH = congenital hypothyroidism; FT4 = free thyroxine; L-T4 = levothyroxine sodium; PCH = permanent congenital hypothyroidism; TCH = transient congenital hypothyroidism; TSH = thyroid-stimulating hormone; TT4 = total thyroxine.

Identifying and predicting physical limitation and cognitive decline trajectory group of older adults in China: A data-driven machine learning analysis.

OBJECTIVE: This study aimed to utilize data-driven machine learning methods to identify and predict potential physical and cognitive function trajectory groups of older adults and determine their crucial factors for promoting active ageing in China. METHODS: Longitudinal data on 3026 older adults from the Chinese Longitudinal Healthy Longevity and Happy Family Survey was used to identify potential physical and cognitive function trajectory groups using a group-based multi-trajectory model (GBMTM). Predictors were selected from sociodemographic characteristics, lifestyle factors, and physical and mental conditions. The trajectory groups were predicted using data-driven machine learning models and dynamic nomogram. Model performance was evaluated by area under the receiver operating characteristics curve (AUROC), area under the precision-recall curve (PRAUC), and confusion matrix. RESULTS: Two physical and cognitive function trajectory groups were determined, including a trajectory group with physical limitation and cognitive decline (14.18 %) and a normal trajectory group (85.82 %). Logistic regression performed well in predicting trajectory groups (AUROC = 0.881, PRAUC = 0.649). Older adults with lower baseline score of activities of daily living, older age, less frequent housework, and fewer actual teeth were more likely to experience physical limitation and cognitive decline trajectory group. LIMITATION: This study didn't carry out external validation. CONCLUSIONS: This study shows that GBMTM and machine learning models effectively identify and predict physical limitation and cognitive decline trajectory group. The identified predictors might be essential for developing targeted interventions to promote healthy ageing.

Long-term trajectories of depressive symptoms and machine learning techniques for fall prediction in older adults: Evidence from the China Health and Retirement Longitudinal Study (CHARLS).

BACKGROUND: Falls are the most common adverse outcome of depression in older adults, yet a accurate risk prediction model for falls stratified by distinct long-term trajectories of depressive symptoms is still lacking. METHODS: We collected the data of 1617 participants from the China Health and Retirement Longitudinal Study register, spanning between 2011 and 2018. The 36 input variables included in the baseline survey were regarded as candidate features. The trajectories of depressive symptoms were classified by the latent class growth model and growth mixture model. Three data balancing technologies and four machine learning algorithms were utilized to develop predictive models for fall classification of depressive prognosis. RESULTS: Depressive symptom trajectories were divided into four categories, i.e., non-symptoms, new-onset increasing symptoms, slowly decreasing symptoms, and persistent high symptoms. The random forest-TomekLinks model achieved the best performance among the case and incident models with an AUC-ROC of 0.844 and 0.731, respectively. In the chronic model, the gradient boosting decision tree-synthetic minority oversampling technique obtained an AUC-ROC of 0.783. In the three models, the depressive symptom score was the most crucial component. The lung function was a common and significant feature in both the case and the chronic models. CONCLUSIONS: This study suggests that the ideal model has a good chance of identifying older persons with a high risk of falling stratified by long-term trajectories of depressive symptoms. Baseline depressive symptom score, lung function, income, and injury experience are influential factors associated with falls of depression evolution.

Interpretable Machine Learning for Fall Prediction Among Older Adults in China.

INTRODUCTION: Falls in older adults are potentially devastating, whereas an accurate fall risk prediction model for community-dwelling older Chinese is still lacking. The objective of this study was to build prediction models for falls and fall-related injuries among community-dwelling older adults in China. METHODS: This study used data (Waves 2015 and 2018) from 5,818 participants from the China Health and Retirement Longitudinal Study. A total of 107 input variables at the baseline level were regarded as candidate features. Five machine learning algorithms were used to build the 3-year fall and fall-related injury risk prediction models. SHapley Additive exPlanations was used for the prediction model explanation. Analyses were conducted in 2022. RESULTS: The logistic regression model achieved the best performance among fall and fall-related injury prediction models with an area under the receiver operating characteristic curve of 0.739 and 0.757, respectively. Experience of falling was the most important feature in both models. Other important features included basic activity of daily living, instrumental activity of daily living, depressive symptoms, house tidiness, grip strength, and sleep duration. The important features unique to the fall model were house temperature, sex, and flush toilets, whereas lung function, smoking, and Internet access were exclusively related to the fall-related injury model. CONCLUSIONS: This study suggests that the optimal models hold promise for screening out older adults at high risk for falls in facilitated targeted interventions. Fall prevention strategies should specifically focus on fall history, physical functions, psychological factors, and home environment.

Seasonal prediction of daily PM2.5 concentrations with interpretable machine learning: a case study of Beijing, China.

Machine learning (ML) has shown high predictive ability in environmental research. Accurate estimation of daily PM2.5 concentrations is a prerequisite to address environmental public health issues. However, studies on the interpretability of ML algorithms were limited. In this study, we aimed to estimate the daily concentrations of PM2.5 at a seasonal level, and to understand the potential mechanisms of ML algorithms' decisions with SHapley Additive exPlanations (SHAP). Daily ground PM2.5 concentrations and meteorological data were obtained from the Beijing Municipal Ecological and Environmental Monitoring Center, and China Meteorological Data Service Centre between December 2013 and 2019 November. We calculated correlation coefficient and variance inflation factor (VIF) to eliminate the variables with collinearity, and recursive feature elimination (RFE) was further used to selected more important predictors. A series of ML algorithms, including linear regression, the variants of linear regression (Ridge, Lasso, Elasticnet), decision tree (DT), k-nearest neighbor (KNN), support vector regression (SVR), ensemble methods (random forest: RF, eXtreme Gradient Boosting: XGBoost), and deep learning (long short-term memory network: LSTM), were developed to estimate seasonal-level daily PM2.5 concentrations. A 10-fold cross validation was used to tune hyperparameters, and root mean square error (RMSE), mean absolute error (MAE), ratio of performance to deviation (RPD), and Lin's concordance correlation coefficient (LCCC) were used to evaluate models' performance. SHAP was performed for local and global interpretability analysis. The results showed that the distribution of PM2.5 concentrations in Beijing showed obvious seasonal patterns. A total of five variables (Precipitation, Mean wind speed, Sunshine duration, Mean surface temperature, Mean relative humidity) were selected for final prediction. LSTM showed much higher accuracy than other traditional ML models, achieved the smallest RMSE of 19.58 µg/m3 and MAE of 15.11 µg/m3. In terms of selected data set, there was acceptable (LCCC = 0.41 ~ 0.52) agreement and accuracy (RPD = 0.97 ~ 1.92) for LSTM. The SHAP analyses revealed that the meteorological factors had different influences in specific predictions, and the complex interactions were also illustrated. These results enhance our understanding of meteorological factors-PM2.5 relationships and explain the mechanisms of ML algorithms' decisions.

Predicting the long-term cognitive trajectories using machine learning approaches: A Chinese nationwide longitudinal database.

OBJECTIVES: This study aimed to explore the long-term cognitive trajectories and its' determinants, and construct prediction models for identifying high-risk populations with unfavorable cognitive trajectories. METHODS: This study included 3502 older adults aged 65-105 years at their first observations in a 16-year longitudinal cohort study. Cognitive function was measured by the Chinese version Mini Mental State Examination. The heterogeneity of cognitive function was identified through mixed growth model. Machine learning algorithms, namely regularized logistic regression (r-LR), support vector machine (SVM), random forest (RF), and super learner (SL) were used to predict cognitive trajectories. Discrimination and calibration metrics were used for performance evaluation. RESULTS: Two distinct trajectories were identified according to the changes of MMSE scores: intact cognitive functioning (93.6%), and dementia (6.4%). Older age, female gender, Han ethnicity, having no schooling, rural residents, low-frequency leisure activities, and low baseline BADL score were associated with a rapid decline in cognitive function. r-LR, SVM, and SL performed well in predicting cognitive trajectories (Sensitivity: 0.73, G-mean: 0.65). Age and psychological well-being were key predictors. CONCLUSION: Two cognitive trajectories were identified among older Chinese, and the identified key factors could be targeted for constructing early risk prediction models.

Clinical evaluation of Shufeng Jiedu Capsules combined with umifenovir (Arbidol) in the treatment of common-type COVID-19: a retrospective study.

OBJECTIVE: To understand the clinical effectiveness and safety of Shufeng Jiedu Capsules combined with umifenovir (Arbidol) in the treatment of common-type COVID-19. METHODS: A retrospective cohort study was used to analyze the case data of 200 inpatients diagnosed with common-type COVID-19 at Wuhan Hospital. Participants were divided into a control group and an experimental group. The control group was treated with Arbidol hydrochloride capsules while the experimental group was treated with combination Arbidol hydrochloride capsules and Shufeng Jiedu Capsules (SFJDC) for 14 days. RESULTS: Defervescence was achieved more rapidly in the experimental group (P < 0.05). The white blood cell count and the lymphocyte percentage in the experimental group were higher than that of the control group (P < 0.05). CRP and IL-6 levels in the experimental group were significantly lower than those in the control group (P < 0.05). The proportion of chest CT studies showing resolution of pneumonia in the experimental group was significantly higher than that in the control group (P < 0.05). CONCLUSIONS: A treatment regimen of Shufeng Jiedu Capsules combined with Arbidol to treat common-type COVID-19, combining traditional Chinese and western allopathic medicine, improves time to recovery, has better clinical effectiveness, and is safe.

Comparison of Regression and Machine Learning Methods in Depression Forecasting Among Home-Based Elderly Chinese: A Community Based Study.

BACKGROUND: Depression is highly prevalent and considered as the most common psychiatric disorder in home-based elderly, while study on forecasting depression risk in the elderly is still limited. In an endeavor to improve accuracy of depression forecasting, machine learning (ML) approaches have been recommended, in addition to the application of more traditional regression approaches. METHODS: A prospective study was employed in home-based elderly Chinese, using baseline (2011) and follow-up (2013) data of the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative cohort study. We compared four algorithms, including the regression-based models (logistic regression, lasso, ridge) and ML method (random forest). Model performance was assessed using repeated nested 10-fold cross-validation. As the main measure of predictive performance, we used the area under the receiver operating characteristic curve (AUC). RESULTS: The mean AUCs of the four predictive models, logistic regression, lasso, ridge, and random forest, were 0.795, 0.794, 0.794, and 0.769, respectively. The main determinants were life satisfaction, self-reported memory, cognitive ability, ADL (activities of daily living) impairment, CESD-10 score. Life satisfaction increased the odds ratio of a future depression by 128.6% (logistic), 13.8% (lasso), and 13.2% (ridge), and cognitive ability was the most important predictor in random forest. CONCLUSIONS: The three regression-based models and one ML algorithm performed equally well in differentiating between a future depression case and a non-depression case in home-based elderly. When choosing a model, different considerations, however, such as easy operating, might in some instances lead to one model being prioritized over another.