Explainable deep learning predictions for illness risk of mental disorders in Nanjing, China

A novel higher performance nomogram based on explainable machine learning for predicting mortality risk in stroke patients within 30 days based on clinical features on the first day ICU admission

BACKGROUND

This study aimed to develop a higher performance nomogram based on explainable machine learning methods, and to predict the risk of death of stroke patients within 30 days based on clinical characteristics on the first day of intensive care units (ICU) admission.

METHODS

Data relating to stroke patients were extracted from the Medical Information Marketplace of the Intensive Care (MIMIC) IV and III database. The LightGBM machine learning approach together with Shapely additive explanations (termed as explain machine learning, EML) was used to select clinical features and define cut-off points for the selected features. These selected features and cut-off points were then evaluated using the Cox proportional hazards regression model and Kaplan-Meier survival curves. Finally, logistic regression-based nomograms for predicting 30-day mortality of stroke patients were constructed using original variables and variables dichotomized by cut-off points, respectively. The performance of two nomograms were evaluated in overall and individual dimension.

RESULTS

A total of 2982 stroke patients and 64 clinical features were included, and the 30-day mortality rate was 23.6% in the MIMIC-IV datasets. 10 variables ("sofa (sepsis-related organ failure assessment)", "minimum glucose", "maximum sodium", "age", "mean spo2 (blood oxygen saturation)", "maximum temperature", "maximum heart rate", "minimum bun (blood urea nitrogen)", "minimum wbc (white blood cells)" and "charlson comorbidity index") and respective cut-off points were defined from the EML. In the Cox proportional hazards regression model (Cox regression) and Kaplan-Meier survival curves, after grouping stroke patients according to the cut-off point of each variable, patients belonging to the high-risk subgroup were associated with higher 30-day mortality than those in the low-risk subgroup. The evaluation of nomograms found that the EML-based nomogram not only outperformed the conventional nomogram in NIR (net reclassification index), brier score and clinical net benefits in overall dimension, but also significant improved in individual dimension especially for low "maximum temperature" patients.

CONCLUSIONS

The 10 selected first-day ICU admission clinical features require greater attention for stroke patients. And the nomogram based on explainable machine learning will have greater clinical application.

Exploring the association between two groups of metals with potentially opposing renal effects and renal function in middle-aged and older adults: Evidence from an explainable machine learning method

BACKGROUND

Machine learning models have promising applications in capturing the complex relationship between mixtures of exposures and outcomes.

OBJECTIVE

Our study aimed at introducing an explainable machine learning (EML) model to assess the association between metal mixtures with potentially opposing renal effects and renal function in middle-aged and older adults.

METHODS

This study extracted data from two cycle years of the National Health and Nutrition Examination Survey (NHANES). Participants aged 45 years or older with complete data on six metals (lead, cadmium, manganese, mercury, and selenium) and related covariates were enrolled. The EML model was developed by the optimized machine learning model together with Shapley Additive exPlanations (SHAP) to assess the chronic kidney disease (CKD) risk with metal mixtures. The results from EML were further compared in detail with multiple logistic regression (MLR) and Bayesian kernel machine regression (BKMR).

RESULTS

After adjusting for included covariates, MLR pointed out the lead and arsenic were generally positively associated with CKD, but manganese had a negative association. In the BKMR analysis, each metal was found to have a non-linear association with the risk of CKD, and interactions can exist between metals, especially for arsenic and lead. The EML ranked the feature importance: lead, manganese, arsenic and selenium were close behind in importance after gender, age or BMI for participants with CKD. Strong interactions between mercury and lead, manganese and cadmium and arsenic and manganese were identified by partial dependence plot (PDP) of SHAP and bivariate exposure-response effect plots of BKMR. The EML model determined the "trigger point" at which the risk of CKD abruptly changed.

CONCLUSION

Co-exposure to metals with different nephrotoxicity could have different joint association with renal function, and EML can be a powerful method for studying complex exposure mixtures.

Towards Personalised Mood Prediction and Explanation for Depression from Biophysical Data

Digital health applications using Artificial Intelligence (AI) are a promising opportunity to address the widening gap between available resources and mental health needs globally. Increasingly, passively acquired data from wearables are augmented with carefully selected active data from depressed individuals to develop Machine Learning (ML) models of depression based on mood scores. However, most ML models are black box in nature, and hence the outputs are not explainable. Depression is also multimodal, and the reasons for depression may vary significantly between individuals. Explainable and personalised models will thus be beneficial to clinicians to determine the main features that lead to a decline in the mood state of a depressed individual, thus enabling suitable personalised therapy. This is currently lacking. Therefore, this study presents a methodology for developing personalised and accurate Deep Learning (DL)-based predictive mood models for depression, along with novel methods for identifying the key facets that lead to the exacerbation of depressive symptoms. We illustrate our approach by using an existing multimodal dataset containing longitudinal Ecological Momentary Assessments of depression, lifestyle data from wearables and neurocognitive assessments for 14 mild to moderately depressed participants over one month. We develop classification- and regression-based DL models to predict participants' current mood scores-a discrete score given to a participant based on the severity of their depressive symptoms. The models are trained inside eight different evolutionary-algorithm-based optimisation schemes that optimise the model parameters for a maximum predictive performance. A five-fold cross-validation scheme is used to verify the DL model's predictive performance against 10 classical ML-based models, with a model error as low as 6% for some participants. We use the best model from the optimisation process to extract indicators, using SHAP, ALE and Anchors from explainable AI literature to explain why certain predictions are made and how they affect mood. These feature insights can assist health professionals in incorporating personalised interventions into a depressed individual's treatment regimen.

A methodological study of exposome based on an open database: Association analysis between exposure to metal mixtures and hyperuricemia

BACKGROUND

Exposome recognizes that humans are constantly exposed to multiple environmental factors, and elucidating the health effects of complex exposure mixtures places greater demands on analytical methods.

OBJECTS

We aimed to explore the association between mixed exposure to metals and hyperuricemia (HUA), and highlight the potential of explainable machine learning (EML) and causal mediation analysis (CMA) for application in the analysis of exposome data.

METHODS

Pre-pandemic data from the National Health and Nutrition Examination Survey (NHANES) 2011-2020 and a total of 13780 individuals were included. We first used traditional statistical models (multiple logistic regression (MLR) and restricted cubic spline regression (RCS)) and EML to explore associations between mixed metals exposures and HUA, followed by the CMA using the 4-way decomposition method to analyze the interaction and mediation effects among BMI or estimated glomerular filtration rate (eGFR), metals and HUA.

RESULTS

The prevalence of HUA was 18.91% (2606/13780). The MLR showed that mercury (Q4 vs Q1: OR = 1.08, 95% CI:1.02-1.14) and lead (Q4 vs Q1: OR = 1.23, 95% CI:1.13-1.34) were generally positively associated with HUA. Higher concentrations of lead, mercury, selenium and manganese were associated with the increased odds of HUA, and BMI and eGFR were the top two variables attributable to the risk of developing HUA in the EML. Subgroup analyses from the MLR and EML consistently demonstrated the positive relationship between exposure to lead, mercury and selenium in participants with BMI <25 kg/m2 and BMI ≥30 kg/m2. BMI mediated 32.12% of the association between lead exposure and HUA, and the interaction between BMI and lead accounted for 3.88% of the association in the CMA.

CONCLUSIONS

Heavy metals can increase the HUA risk and BMI or eGFR can mediate and interact with metals to cause HUA. Future studies based on exposome can attempt to utilize the EML and CMA.

Residential greenness and cardiac conduction abnormalities: epidemiological evidence and an explainable machine learning modeling study

BACKGROUND

Previous studies indicated the beneficial influence of residential greenness on cardiovascular disease (CVD), however, the association of residential greenness with cardiac conduction performance remains unclear. This study aims to examine the epidemiological associations between residential greenness and cardiac conduction abnormalities in rural residents, simultaneously exploring the role of residential greenness for cardiac health in an explainable machine learning modeling study.

METHODS

A total of 27,294 participants were derived from the Henan Rural Cohort. Two satellite-based indices, the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI), were used to estimate residential greenness. Independent and combined associations of residential greenness indices and physical activities with electrocardiogram (ECG) parameter abnormalities were evaluated using the logistic regression model and generalized linear model. The Gradient Boosting Machine (GBM) and the SHapely Additive exPlanations (SHAP) were employed in the modeling study.

RESULTS

The odds ratios (OR) and 95% confidence interval (CI) for QRS interval, heart rate (HR), QTc interval, and PR interval abnormalities with per interquartile range in NDVI were 0.896 (0.873-0.920), 0.955 (0.926-0.986), 1.015 (0.984-1.047), and 0.986 (0.929-1.045), respectively. Furthermore, the participants with higher physical activities plus residential greenness (assessed by EVI) were related to a 1.049-fold (1.017-1.081) and 1.298-fold (1.245-1.354) decreased risk for abnormal QRS interval and HR. Similar results were also observed in the sensitivity analysis. The NDVI ranked fifth (SHAP mean value 0.116) in the analysis for QRS interval abnormality risk in the modeling study.

CONCLUSION

A higher level of residential greenness was significantly associated with cardiac conduction abnormalities. This effect might be strengthened in residents with more physical activities. This study indicated the cruciality of environmental greenness to cardiac functions and also contributed to refining preventive medicine and greenness design strategies.

Deposition-mediated phytoremediation of nitrogen oxide emissions

The growing global population and use of natural resources lead to significant air pollution. Nitrogen oxide emissions is a potential killer threatening human health requiring focus and remediation using vegetation being efficient and cheap. Here we review the mechanisms of removing nitrogen oxides by dry deposition of plants, discussing the principle of leaf absorption of pollutants and factors affecting the removal of nitrogen oxides providing a theoretical basis for the selection of urban greening vegetation.

Construction and Interpretation of Prediction Model of Teicoplanin Trough Concentration via Machine Learning

Objective

To establish an optimal model to predict the teicoplanin trough concentrations by machine learning, and explain the feature importance in the prediction model using the SHapley Additive exPlanation (SHAP) method.

Methods

A retrospective study was performed on 279 therapeutic drug monitoring (TDM) measurements obtained from 192 patients who were treated with teicoplanin intravenously at the First Affiliated Hospital of Army Medical University from November 2017 to July 2021. This study included 27 variables, and the teicoplanin trough concentrations were considered as the target variable. The whole dataset was divided into a training group and testing group at the ratio of 8:2, and predictive performance was compared among six different algorithms. Algorithms with higher model performance (top 3) were selected to establish the ensemble prediction model and SHAP was employed to interpret the model.

Results

Three algorithms (SVR, GBRT, and RF) with high R² scores (0.676, 0.670, and 0.656, respectively) were selected to construct the ensemble model at the ratio of 6:3:1. The model with R² = 0.720, MAE = 3.628, MSE = 22.571, absolute accuracy of 83.93%, and relative accuracy of 60.71% was obtained, which performed better in model fitting and had better prediction accuracy than any single algorithm. The feature importance and direction of each variable were visually demonstrated by SHAP values, in which teicoplanin administration and renal function were the most important factors.

Conclusion

We firstly adopted a machine learning approach to predict the teicoplanin trough concentration, and interpreted the prediction model by the SHAP method, which is of great significance and value for the clinical medication guidance.

Time series analysis of short-term effects of particulate matter pollution on the circulatory system disease mortality risk in Lishui District, China

Epidemiological evidence has shown a significant association between short-term exposure to air pollution and mortality risk for circulatory system diseases (CSD). However, informative insights on the significance and magnitude of its relationship in the process of government interventions on abating air pollution are still lacking, particularly in a burgeoning Chinese city. In this study, we conducted a time series study in Lishui District, Nanjing, to examine the effect of ambient particulate matter (PM), e.g., PM_2.5 and PM₁₀, on daily death counts of CSD which included cardiovascular disease (CVD), cerebrovascular disease (CEVD), and arteriosclerotic heart disease (ASHD) mortality from January 1, 2015, to December 31, 2019. The results revealed that each 10 μg/m³ increase in PM_2.5 and PM₁₀ concentration at lag0 day was associated with an increase of 1.33% (95% confidence interval, 0.08%, 2.60%) and 1.12% (0.43%, 1.82%) in CSD mortality; 2.42% (0.44%, 4.43%) and 1.43% (0.32%, 2.55%) in CVD mortality; 1.20% (- 0.31%, 2.73%) and 1.21% (0.38%, 2.05%) in CEVD mortality; and 2.78% (0.00%, 5.62%) and 1.66% (0.14%, 3.21%) in ASHD mortality, respectively. For cumulative risk, the corresponding increase in daily mortality for the same change in PM_2.5 concentration at lag03 day was significantly associated with 1.94% (0.23%, 3.68%), 3.17% (0.58%, 5.84%), 2.38% (0.17%, 4.63%), and 4.92% (1.18%, 8.81%) for CSD, CVD, CEVD, and ASHD, respectively. The exposure-response curves were approximately nonlinear over the entire exposure range of the PM concentrations. We also analyzed the effect modifications by season (warm or cold), age group (0-64 years, 65-74 years, or ≥ 75 years), and sex (male or female). Although not statistically significant, stratified analysis showed greater vulnerability to PM exposure for cold season, population over 65 years of age, and female group.