SHapley Additive exPlanations Research Articles

An interpretable model for landslide susceptibility assessment based on Optuna hyperparameter optimization and Random Forest

This study proposed an interpretable model that combines Random Forest (RF), Optuna hyperparameter optimization, and SHapley Additive exPlanations (SHAP) to achieve optimal landslide susceptibility evaluation and provide explanations in the northwest region of Yunnan Province in China. First, an inventory of 4447 landslides and 23 related factors was considered for the landslide susceptibility assessment. Subsequently, a hyperparameter-optimized RF model was developed using the Optuna framework and the training dataset to generate landslide susceptibility maps. The performance of the models were evaluated using accuracy (ACC), precision (PPV), recall (TPR), F1-score (F1), and the Area Under the Curve (AUC) based on the Receiver Operating Characteristic. Furthermore, the interpretability of the model was enhanced through the implementation of SHAP. The proposed model demonstrated outstanding performance on the test set, achieving an ACC of 0.7792, PPV of 0.7448, TPR of 0.8769, F1 of 0.8055, and an AUC of 0.8387. The interpretability analysis revealed that elevation, population density, distance from roads, and normalized difference vegetation index were the primary factors influencing landslide occurrences in the study area. This study provides a comprehensive framework for evaluating landslide susceptibility in specific regions and offers invaluable insights for the prevention and management of landslide disasters.

Interpretable multiphasic CT-based radiomic analysis for preoperatively differentiating benign and malignant solid renal tumors: a multicenter study.

To develop and compare machine learning models based on triphasic contrast-enhanced CT (CECT) for distinguishing between benign and malignant renal tumors. In total, 427 patients were enrolled from two medical centers: Center 1 (serving as the training set) and Center 2 (serving as the external validation set). First, 1781 radiomic features were individually extracted from corticomedullary phase (CP), nephrographic phase (NP), and excretory phase (EP) CECT images, after which 10 features were selected by the minimum redundancymaximum relevance method. Second, random forest (RF) models were constructed from single-phase features (CP, NP, and EP) as well as from the combination of features from all three phases (TP). Third, the RF models were assessed in the training and external validation sets. Finally, the internal prediction mechanisms of the models were explained by the SHapley Additive exPlanations (SHAP) approach. A total of 266 patients with renal tumors from Center 1 and 161 patients from Center 2 were included. In the training set, the AUCs of the RF models constructed from the CP, NP, EP, and TP features were 0.886, 0.912, 0.930, and 0.944, respectively. In the external validation set, the models achieved AUCs of 0.860, 0.821, 0.921, and 0.908, respectively. The "original_shape_Flatness" feature played the most important role in the prediction outcome for the RF model based on EP features according to the SHAP method. The four RF models efficiently differentiated benign from malignant solid renal tumors, with the EP feature-based RF model displaying the best performance.

Hybrid feature selection in a machine learning predictive model for perioperative myocardial injury in noncoronary cardiac surgery with cardiopulmonary bypass.

Perioperative myocardial injury (PMI) is associated with increased mobility and mortality after noncoronary cardiac surgery. However, limited studies have developed a predictive model for PMI. Therefore, we used hybrid feature selection (FS) methods to establish a predictive model for PMI in noncoronary cardiac surgery with cardiopulmonary bypass (CPB). This was a single-center retrospective study conducted at the Fuwai Hospital in China. Patients aged 18-70years who underwent elective noncoronary surgery with CPB at our institution from December 2018 to April 2021 were enrolled. The primary outcome was PMI, defined as the postoperative cardiac troponin I (cTnI) levels exceeding 220 times of upper reference limit (URL). Statistical analyses were conducted by Python (Python Software Foundation, version 3.9.7 and integrated development environment Jupyter Notebook 1.1.0) and SPSS software version 26.0 (IBM Corp., Armonk, New York, USA). A total of 1130 patients were eventually eligible for this study. The incidence of PMI was 20.3% (229/1130) in the overall patients, 20.6% (163/791) in the training dataset, and 19.5% (66/339) in the testing dataset. The logistic regression model performed the best AUC of 0.6893 (95 CI%: 0.6371-0.7382) by the traditional selection method, and the random forest model performed the best AUC of 0.6937 (95 CI%: 0.6416-0.7423) by the union of Wrapper and Embedded method, and the CatBoost model performed the best AUC of 0.6828 (95 CI%: 0.6304-0.7320) by the union of Embedded and forward logistic regression technique, and the Naïve Bayes model achieved the best AUC with 0.7254 (95 CI%: 0.6746-0.7723) by forwarding logistic regression method. Moreover, the decision tree, KNeighborsClassifier, and support vector machine models performed the worse AUC in all selection forms. Furthermore, the SHapley Additive exPlanations plot showed that prolonged CPB, aortic clamp time, and preoperative low platelets count were strongly related to the PMI risk. In total, four category feature selection methods were utilized, comprising five individual selection techniques and 15 combined methods. Notably, the combination of logistic regression and embedded methods demonstrated outstanding performance in predicting PMI risk. We also concluded that the machine learning model, including random forest, catboost, and Naive Bayes, were suitable candidates for establishing PMI predictive model. Nevertheless, additional investigation and validation are imperative for substantiating these finding.

Development and Validation of an Explainable Deep Learning Model to Predict In-Hospital Mortality for Patients With Acute Myocardial Infarction: Algorithm Development and Validation Study.

Acute myocardial infarction (AMI) is one of the most severe cardiovascular diseases and is associated with a high risk of in-hospital mortality. However, the current deep learning models for in-hospital mortality prediction lack interpretability. This study aims to establish an explainable deep learning model to provide individualized in-hospital mortality prediction and risk factor assessment for patients with AMI. In this retrospective multicenter study, we used data for consecutive patients hospitalized with AMI from the Chongqing University Central Hospital between July 2016 and December 2022 and the Electronic Intensive Care Unit Collaborative Research Database. These patients were randomly divided into training (7668/10,955, 70%) and internal test (3287/10,955, 30%) data sets. In addition, data of patients with AMI from the Medical Information Mart for Intensive Care database were used for external validation. Deep learning models were used to predict in-hospital mortality in patients with AMI, and they were compared with linear and tree-based models. The Shapley Additive Explanations method was used to explain the model with the highest area under the receiver operating characteristic curve in both the internal test and external validation data sets to quantify and visualize the features that drive predictions. A total of 10,955 patients with AMI who were admitted to Chongqing University Central Hospital or included in the Electronic Intensive Care Unit Collaborative Research Database were randomly divided into a training data set of 7668 (70%) patients and an internal test data set of 3287 (30%) patients. A total of 9355 patients from the Medical Information Mart for Intensive Care database were included for independent external validation. In-hospital mortality occurred in 8.74% (670/7668), 8.73% (287/3287), and 9.12% (853/9355) of the patients in the training, internal test, and external validation cohorts, respectively. The Self-Attention and Intersample Attention Transformer model performed best in both the internal test data set and the external validation data set among the 9 prediction models, with the highest area under the receiver operating characteristic curve of 0.86 (95% CI 0.84-0.88) and 0.85 (95% CI 0.84-0.87), respectively. Older age, high heart rate, and low body temperature were the 3 most important predictors of increased mortality, according to the explanations of the Self-Attention and Intersample Attention Transformer model. The explainable deep learning model that we developed could provide estimates of mortality and visual contribution of the features to the prediction for a patient with AMI. The explanations suggested that older age, unstable vital signs, and metabolic disorders may increase the risk of mortality in patients with AMI.

Towards XAI agnostic explainability to assess differential diagnosis for Meningitis diseases

Abstract Meningitis, characterized by meninges and cerebrospinal fluid (CSF) inflammation, poses diagnostic challenges due to diverse clinical manifestations. This work introduces an explainable AI automatic medical decision methodology that determines critical features and their relevant values for the differential diagnosis of various meningitis cases. We proceed with knowledge acquisition to define the rules for this research. Currently, we have established the etiological diagnosis of Meningococcaemia, Meningococcal Meningitis, Tuberculous Meningitis, Aseptic Meningitis, Haemophilus influenzae Meningitis, and Pneumococcal
Meningitis. The data preprocessing was conducted after collecting data from samples with meningitis diseases at Setif Hospital in Algeria. Tree-based ensemble methods were then applied to assess the model’s performance. Finally, we implement an XAI agnostic explainability approach based on the SHapley Additive exPlanations technique to attribute each feature’s contribution to the model’s output. Experiments were conducted on the collected dataset and the SINAN database, obtained from the Brazilian Government’s Health Information System on Notifiable Diseases, which comprises 6729 patients aged over 18 years. The Extreme Gradient Boosting model was chosen for its superior performance metrics (Accuracy: 0.90, AUROC: 0.94, and F1-score: 0.98). Setif’s hospital data revealed notable performance metrics (Accuracy: 0.7143, F1-Score: 0.7857). This study's findings showcase each feature's contribution to the model’s predictions and diagnosis. It also reveals critical biomarker ranges associated with distinct types of Meningitis. Significant diagnostic effect was found for Meningococcal Meningitis with elevated neutrophil levels (>40%) and balanced lymphocyte levels (40-60%). Tuberculous Meningitis demonstrated low neutrophil levels (<60%) and elevated lymphocyte levels (>60%). Haemophilus influenzae meningitis exhibited a predominance of neutrophils (>80%), while Aseptic meningitis showed lower neutrophil levels (<40%) and lymphocyte levels within the range of 50-60%. The majority of the AI automatic medical decision results are twinned with validation by our team of infectious disease experts, confirming the alignment of algorithmic diagnoses with clinical practices.

Exploration of a machine learning approach for diagnosing sarcopenia among Chinese community-dwelling older adults using sEMG-based data

BackgroundIn the practical application of sarcopenia screening, there is a need for faster, time-saving, and community-friendly detection methods. The primary purpose of this study was to perform sarcopenia screening in community-dwelling older adults and investigate whether surface electromyogram (sEMG) from hand grip could potentially be used to detect sarcopenia using machine learning (ML) methods with reasonable features extracted from sEMG signals. The secondary aim was to provide the interpretability of the obtained ML models using a novel feature importance estimation method.MethodsA total of 158 community-dwelling older residents (≥ 60 years old) were recruited. After screening through the diagnostic criteria of the Asian Working Group for Sarcopenia in 2019 (AWGS 2019) and data quality check, participants were assigned to the healthy group (n = 45) and the sarcopenic group (n = 48). sEMG signals from six forearm muscles were recorded during the hand grip task at 20% maximal voluntary contraction (MVC) and 50% MVC. After filtering recorded signals, nine representative features were extracted, including six time-domain features plus three time-frequency domain features. Then, a voting classifier ensembled by a support vector machine (SVM), a random forest (RF), and a gradient boosting machine (GBM) was implemented to classify healthy versus sarcopenic participants. Finally, the SHapley Additive exPlanations (SHAP) method was utilized to investigate feature importance during classification.ResultsSeven out of the nine features exhibited statistically significant differences between healthy and sarcopenic participants in both 20% and 50% MVC tests. Using these features, the voting classifier achieved 80% sensitivity and 73% accuracy through a five-fold cross-validation. Such performance was better than each of the SVM, RF, and GBM models alone. Lastly, SHAP results revealed that the wavelength (WL) and the kurtosis of continuous wavelet transform coefficients (CWT_kurtosis) had the highest feature impact scores.ConclusionThis study proposed a method for community-based sarcopenia screening using sEMG signals of forearm muscles. Using a voting classifier with nine representative features, the accuracy exceeds 70% and the sensitivity exceeds 75%, indicating moderate classification performance. Interpretable results obtained from the SHAP model suggest that motor unit (MU) activation mode may be a key factor affecting sarcopenia.

Physical frailty identification using machine learning to explore the 5-item FRAIL scale, Cardiovascular Health Study index, and Study of Osteoporotic Fractures index

BackgroundPhysical frailty is an important issue in aging societies. Three models of physical frailty assessment, the 5-Item fatigue, resistance, ambulation, illness and loss of weight (FRAIL); Cardiovascular Health Study (CHS); and Study of Osteoporotic Fractures (SOF) indices, have been regularly used in clinical and research studies. However, no previous studies have investigated the predictive ability of machine learning (ML) for physical frailty assessment. The aim was to use two ML algorithms, random forest (RF) and extreme gradient boosting (XGBoost), to predict these three physical frailty assessment models.Materials and methodsQuestionnaires regarding demographic characteristics, lifestyle habits, living environment, and physical frailty assessment were answered by 445 participants aged 60 years and above. The RF and XGBoost algorithms were used to assess their scores for the three physical frailty indices. Furthermore, feature importance and Shapley additive explanations (SHAP) were used to determine the important physical frailty factors.ResultsThe XGBoost algorithm obtained higher accuracy for predicting the three physical frailty indices; the areas under the curve obtained by the XGBoost algorithm for the 5-Item FRAIL, CHS, and SOF indices were 0.84. 0.79, and 0.69, respectively. The feature importance and SHAP of the XGBoost algorithm revealed that systolic blood pressure, diastolic blood pressure, age, and body mass index play important roles in all three physical frailty models.ConclusionThe XGBoost algorithm has a more accurate predictive rate than RF across all three physical frailty assessments. Thus, ML can be a useful tool for the early detection of physical frailty.

Predicting osteoporotic fractures post-vertebroplasty: a machine learning approach with a web-based calculator

PurposeThe aim of this study was to develop and validate a machine learning (ML) model for predicting the risk of new osteoporotic vertebral compression fracture (OVCF) in patients who underwent percutaneous vertebroplasty (PVP) and to create a user-friendly web-based calculator for clinical use.MethodsA retrospective analysis of patients undergoing percutaneous vertebroplasty: A retrospective analysis of patients treated with PVP between June 2016 and June 2018 at Liuzhou People's Hospital was performed. The independent variables of the model were screened using Boruta and modelled using 9 algorithms. Model performance was assessed using the area under the receiver operating characteristic curve (ROC_AUC), and clinical utility was assessed by clinical decision curve analysis (DCA). The best models were analysed for interpretability using SHapley Additive exPlanations (SHAP) and the models were deployed visually using a web calculator.ResultsTraining and test groups were split using time. The SVM model performed best in both the training group tenfold cross-validation (CV) and validation group AUC, with an AUC of 0.77. DCA showed that the model was beneficial to patients in both the training and test sets. A network calculator developed based on the SHAP-based SVM model can be used for clinical risk assessment (https://nicolazhang.shinyapps.io/refracture_shap/).ConclusionsThe SVM-based ML model was effective in predicting the risk of new-onset OVCF after PVP, and the network calculator provides a practical tool for clinical decision-making. This study contributes to personalised care in spinal surgery.

Precise prediction of CO2 separation performance of metal–organic framework mixed matrix membranes based on feature selection and machine learning

Nowadays, the price of fossil fuels keeps setting new records, escalating continuing concerns about global warming from CO2 production from fuel combustion. As a promising membrane separation technique dealing with carbon capture, metal–organic framework (MOF) mixed matrix membranes (MMMs) have been extensively studied. Herein, a genetic algorithm (GA) optimized artificial neural network (ANN) was developed to form prediction model of MOF MMMs performances towards CO2/N2 separation. The MOF properties, polymer properties, and the operating conditions were used as the characteristic variables. To overcome the limitation, molecular descriptors were incorporated to reflect the physicochemical properties of polymers and target encoding was applied to digitalize the MOF and polymer types. In addition, recursive feature elimination algorithm was used to filter the optimal feature subset and Shapley additive explanations was utilized to analyze the feature importance. The results demonstrated that the model has a dramatically improved prediction performance than other machine learning methods.

Global de-trending significantly improves the accuracy of XGBoost-based county-level maize and soybean yield prediction in the Midwestern United States

ABSTRACT The application of machine learning in crop yield prediction has gained considerable traction, yet uncertainties persist regarding the impact of the yield trends on these predictions and the differences between the detrending methods. In our study, we utilized extreme gradient boosting (XGBoost) to scrutinize the effects of no trend processing (NTP), input year as a feature (IYF), input average yield as a feature (IAYF), input linear yield as a feature (ILYF), and the global detrending method (GDT) on the yield prediction of maize and soybean in the Midwestern United States. Based on our findings, compared with that of NTP, the incorporation of the yield trend as a predictor in XGBoost significantly improved the accuracy and reduced the uncertainty of the yield prediction. Notably, GDT emerged as a standout performer, significantly reducing the average yield prediction error by 0.091 t/ha for soybean and 0.158 t/ha for maize with respect to NTP, and concurrently improving the determination coefficient (R2) by 20.6% and 19.6% for soybean and maize, respectively. Compared with IYF, IAYF, and ILYF, GDT showed substantial improvements ranging from 3.8% to 12.7% in R2 for soybean and 3.6% to 12.7% for maize. The SHapley Additive ExPlanations (SHAP) framework showed that the enhanced vegetation index (EVI), particularly during the soybean podding and maize dough formation stages, played a crucial role in understanding the variations in interannual yield variability. These findings confirmed the importance of GDT in crop yield prediction via machine learning and could be used to facilitate future advancements in machine learning applications for yield forecasting.

Analysis of Meteorological Drivers of Taihu Lake Algal Blooms over the Past Two Decades and Development of a VOCs Emission Inventory for Algal Bloom

Cyanobacterial blooms represent a common environmental issue in aquatic systems, and these blooms bring forth numerous hazards, with the generation of volatile organic compounds (VOCs) being one of them. Global climate change has led to alterations in various climatic factors affecting algal growth, indirectly impacting the quantity of VOCs released by algae. With advancements in remote sensing technology, exploration of the spatiotemporal distributions of algae in large water bodies has become feasible. This study focuses on Taihu Lake, characterized by frequent occurrences of cyanobacterial blooms. Utilizing MODIS satellite imagery from 2001 to 2020, we analyzed the spatiotemporal characteristics of cyanobacterial blooms in Taihu Lake and its subregions. Employing the LightGBM machine learning model and the (SHapley Additive exPlanations) SHAP values, we quantitatively analyzed the major meteorological drivers influencing cyanobacterial blooms in each region. VOC-related source spectra and emission intensities from cyanobacteria in Taihu Lake are collected based on the literature review and are used to compile the first inventory of VOC emissions from blue-green algae blooms in Taihu Lake. The results indicate that since the 21st century, the situation of cyanobacterial blooms in Taihu Lake has continued to deteriorate with increasing variability. The relative impact of meteorological factors varies across different regions, but temperature consistently shows the highest sensitivity in all areas. The VOCs released from the algal blooms increase with the proliferation of the blooms, posing a continuous threat to the atmospheric environment of the surrounding cities. This study aims to provide a scientific basis for further improvement of air quality in urban areas adjacent to large lakes.

Multi-step ahead dissolved oxygen concentration prediction based on knowledge guided ensemble learning and explainable artificial intelligence

Accurate water quality prediction is crucial for effective environmental management and decision-making. However, previous studies have solely relied on historical data to simulate water quality, overlooking the potential discrepancies between predicted values and actual observations. Additionally, the opacity of machine learning models has posed challenges to the credibility of their predictions. Hence, considering the excellent nonlinear fitting ability of ensemble tree models, especially the Categorical Boosting (Catboost) model, this study proposes a knowledge-guided Catboost (KGCatboost) model for predicting the dissolved oxygen concentration, one of the vital water quality indicators, in 15 river sections of the Yangtze River Basin in Yunnan Province, China. Furthermore, to enhance the model’s interpretability, we employ the SHapley Additive exPlanations (SHAP) method to analyze the contributions of each input variable within the water body. The results demonstrate that on the test set of each dataset, the mean Nash-Sutcliffe Efficiency (NSE) value of KGCatboost is 0.874, which has improved by 0.34% and 3.07% compared to Catboost and eXtreme Gradient Boosting (Xgboost). In addition, this study reveals that pH has the most significant impact on DO concentrations. Specifically, as the pH increased, the DO concentration increased significantly. A regulatory mechanism has also been developed to alleviate the hazards caused by low DO concentrations. The KGCatboost model can provide valuable guidance for water resource management processes.

Interpretable Landslide Susceptibility Evaluation Based on Model Optimization

Machine learning (ML) is increasingly utilized in Landslide Susceptibility Mapping (LSM), though challenges remain in interpreting the predictions of ML models. To reveal the response relationship between landslide susceptibility and evaluation factors, an interpretability model was constructed to analyze how the results of the ML model are realized. This study focuses on Zhenba County in Shaanxi Province, China, employing both Random Forest (RF) and Support Vector Machine (SVM) to develop LSM models optimized through Random Search (RS). To enhance interpretability, the study incorporates techniques such as Partial Dependence Plot (PDP), Local Interpretable Model-Agnostic Explanations (LIMEs), and Shapley Additive Explanations (SHAP). The RS-optimized RF model demonstrated superior performance, achieving an Area Under the Curve (AUC) of 0.965. The interpretability model identified the NDVI and distance from road as important factors influencing landslides occurrence. NDVI plays a positive role in the occurrence of landslides in this region, and the landslide-prone areas are within 500 m from the road. These analyses indicate the importance of improved hyperparameter selection in enhancing model accuracy and performance. The interpretability model provides valuable insights into LSM, facilitating a deeper understanding of landslide formation mechanisms and guiding the formulation of effective prevention and control strategies.

Investigating the Effects of the COVID-19 Pandemic on Stock Volatility in Sub-Saharan Africa: Analysis Using Explainable Artificial Intelligence

This study examines the impact of the COVID-19 pandemic on sector volatility in sub-Saharan Africa by drawing evidence from two large and two small stock exchanges in the region. The analysis included stock-specific data, COVID-19 metrics, and macroeconomic indicators from January 2019 to July 2022. This study employs generalized autoregressive conditional heteroskedasticity (GARCH) models to estimate volatility and Explainable Artificial Intelligence (XAI) in the form of SHapley Additive exPlanations (SHAP) to identify significant factors driving stock volatility during the pandemic. The findings reveal significant volatility increases at the onset of the pandemic, with government stringency measures leading to increased volatility in larger exchanges, while the introduction of vaccination programs helped to reduce volatility. Weaker macroeconomic fundamentals impact volatility in smaller exchanges. The healthcare sector has emerged as the most resilient, while non-essential sectors, such as consumer discretionary, materials, and real estate, face greater vulnerability, especially in smaller exchanges. The research findings reveal that the heightened stock market volatility observed was mainly a result of the government’s actions to combat the spread of the pandemic, rather than its outbreak. We recommend that governments introduce sound policies to balance public health measures and economic stability, and that investors diversify their investments to reduce the impact of pandemics.

Exploring the Limits of the Generalized CHARMM and AMBER Force Fields through Predictions of Hydration Free Energy of Small Molecules.

Accurate force field parameters, potential energy functions, and receptor-ligand models are essential for modeling the solvation and binding of drug-like molecules to a receptor. A large and ever-growing chemical space of medicinally relevant scaffolds has also required these factors, especially force field parameters, to be highly transferable. Generalized force fields such as the CHARMM General Force Field (CGenFF) and the generalized AMBER force field (GAFF) have accomplished this feat along with other contemporaneous ones like OPLS. Here, we analyze the limits in the parametrization of drug-like small molecules by CGenFF and GAFF in terms of the various functional groups represented within them. Specifically, we link the presence of specific functional groups to the error in the absolute hydration free energy of over 600 small molecules, predicted by alchemical free energy methods implemented in the CHARMM program. Our investigation reveals that molecules with (i) a nitro group in CGenFF and GAFF are, respectively, over- or undersolubilized in aqueous medium, (ii) amine groups are undersolubilized more so in CGenFF than in GAFF, and (iii) carboxyl groups are more oversolubilized in GAFF than in CGenFF. We present our analyses of the potential factors underlying these trends. We also showcase the use of a machine-learning-based approach combined with the SHapley Additive exPlanations framework to attribute these trends to specific functional groups, which can be easily adopted to explore the limits of other general force fields.

Machine learning-based design of target property-oriented fuels using explainable artificial intelligence

This study presents a machine learning (ML) and explainable artificial intelligence (XAI) integrated framework to guide the experimental investigation of engine combustion toward the most promising fuel compositions. Specifically, the experimental study evaluated nine diesel fuels with differing levels of paraffin, cycloparaffins, and aromatics in a heavy-duty engine, providing key data for subsequent model development. Building on this data, we introduce a multidimensional neural network methodology for feature optimization and performance assessment, incorporating 31 distinct features including engine variables, fuel properties, and components. The methodology innovatively integrates tree-based models with shapley additive explanations (SHAP) for detailed feature importance ranking. Features are sequentially added to subsets based on their importance for multilayer perceptron (MLP) model training, allowing for precise regression performance metrics for each subset. The comprehensive assessment of these best subsets revealed robust regression capabilities, with coefficient of determination (R2) values ranging from 0.9918 to 0.9999, and both root mean square error (RMSE) and mean absolute percentage error (MAPE) maintained below 0.4974 and 0.1388, respectively. Through SHAP and partial dependence plots (PDP), it was demonstrated that optimizing diesel fuel compositions by increasing paraffin levels, reducing aromatics, and moderately increasing cycloparaffins can significantly enhance combustion efficiency and reduce emissions.

Surrogate-model-based optimization of a nonideal polymerization reactor for producing tailored molecular weight distribution

The optimization of polymerization reactors with nonideal mixing has been greatly hindered by a tremendous computation burden. In this study, a global optimization framework to integrate full MWD calculations and detailed reactor dynamics with nonideal mixing behaviors was developed by reducing the considerable computation time required for implementing full MWD. A combination of surrogate-model-based optimization and a computational fluid dynamics (CFD)-compartment reactor model was applied to produce the desired MWD in a nonideal reactor. A neural network (NN) surrogate model was trained using 3,000 MWD data produced from the mechanistic reactor model. The results showed that the model successfully described asymmetric MWD with bimodality resulting from the nonideal mixing behaviors in the reactor. The surrogate model was used to construct operation maps for the asymmetry and bimodality factors, with SHapley Additive exPlanations (SHAP) values showing that the flow rate of the chain transfer agent had the largest impact on the asymmetry factor, whereas temperature significantly influenced the bimodality factor. Finally, the optimization strategy based on the asymmetric and bimodality factors successfully determined operating conditions to produce four tailored MWDs, reported in actual polymerization reactors with R scores of 0.988–0.997.

Utilizing machine learning to facilitate the early diagnosis of posterior circulation stroke

BackgroundPosterior Circulation Syndrome (PCS) presents a diagnostic challenge characterized by its variable and nonspecific symptoms. Timely and accurate diagnosis is crucial for improving patient outcomes. This study aims to enhance the early diagnosis of PCS by employing clinical and demographic data and machine learning. This approach targets a significant research gap in the field of stroke diagnosis and management.MethodsWe collected and analyzed data from a large national Stroke Registry spanning from January 2014 to July 2022. The dataset included 15,859 adult patients admitted with a primary diagnosis of stroke. Five machine learning models were trained: XGBoost, Random Forest, Support Vector Machine, Classification and Regression Trees, and Logistic Regression. Multiple performance metrics, such as accuracy, precision, recall, F1-score, AUC, Matthew’s correlation coefficient, log loss, and Brier score, were utilized to evaluate model performance.ResultsThe XGBoost model emerged as the top performer with an AUC of 0.81, accuracy of 0.79, precision of 0.5, recall of 0.62, and F1-score of 0.55. SHAP (SHapley Additive exPlanations) analysis identified key variables associated with PCS, including Body Mass Index, Random Blood Sugar, ataxia, dysarthria, and diastolic blood pressure and body temperature. These variables played a significant role in facilitating the early diagnosis of PCS, emphasizing their diagnostic value.ConclusionThis study pioneers the use of clinical data and machine learning models to facilitate the early diagnosis of PCS, filling a crucial gap in stroke research. Using simple clinical metrics such as BMI, RBS, ataxia, dysarthria, DBP, and body temperature will help clinicians diagnose PCS early. Despite limitations, such as data biases and regional specificity, our research contributes to advancing PCS understanding, potentially enhancing clinical decision-making and patient outcomes early in the patient’s clinical journey. Further investigations are warranted to elucidate the underlying physiological mechanisms and validate these findings in broader populations and healthcare settings.

ACM BCB 2023: Predicting Postoperative Pain and Opioid Use with Machine Learning Applied to Longitudinal Electronic Health Record and Wearable Data.

Managing acute postoperative pain and minimizing chronic opioid use is crucial for patient recovery and long-term well-being. This study explored using preoperative electronic health records (EHR) and wearable device data for machine-learning models that predict postoperative acute pain and chronic opioid use. The study cohort consisted of ~347 All of Us Research Program participants who underwent one of eight surgical procedures and shared EHR and wearable device data. We developed four machine learning models and used the Shapley additive explanations (SHAP) technique to identify the most relevant predictors of acute pain and chronic opioid use. The stacking ensemble model achieved the highest accuracy in predicting acute pain (0.68) and chronic opioid use (0.89). The area under the curve (AUC) score for severe pain vs. other pain was highest (0.88) when predicting acute post-operative pain. Values of logistic regression, random forest, extreme gradient boosting, and stacking ensemble ranged from 0.74 to 0.90 when predicting postoperative chronic opioid use. Variables from wearable devices played a prominent role in predicting both outcomes. SHAP detection of individual risk factors for severe pain can help healthcare providers tailor pain management plans. Accurate prediction of postoperative chronic opioid use before surgery can help mitigate the risk for the outcomes we studied. Prediction can also reduce the chances of opioid overuse and dependence. Such mitigation can promote safer and more effective pain control for patients during their recovery.

Predicting compressive strength of fiber-reinforced coral aggregate concrete: Interpretable optimized XGBoost model and experimental validation

The addition of macro and micro fibers can enhance the compressive strength of fiber-reinforced coral aggregate concrete (FRCAC-CS). Traditional explicit models for FRCAC-CS suffer from shortcomings related to accuracy and generalization capabilities. To address this issue, a comprehensive database for FRCAC-CS was established by collecting 834 sets of experimental data from 15 literature sources. The hunger games search algorithm (HGS), sooty tern optimization algorithm, and whales optimization algorithm were used to optimize the hyperparameters of the XGBoost algorithm, resulting in optimized XGBoost models for FRCAC-CS. The performance of the prediction models was assessed through comparisons between predicted and actual values, model residual distribution, and performance evaluation metrics. Sensitivity analysis was conducted using the Shapley additive explanations method. Subsequently, 9 different mix proportions were formulated to validate the performance of the optimal model. The results suggest that the HGS–XGBoost model yields predictions that are closer to the actual values, exhibiting smaller mean and standard deviation in model residuals. The five major performance evaluation metrics, including RMSE, MSE, MAPE, MAE, and R2, for the HGS–XGBoost model are 0.91, 0.83, 2.35, 0.77, and 0.99, respectively, surpassing those of other models. Sensitivity analysis highlights the water–to–binder ratio and total aggregate content as the two most crucial factors among the mix components. Decreasing the water–to–binder ratio and the total aggregate content improves the FRCAC-CS. The experimental results from the 9 mix proportions demonstrate that the error between predicted and experimental values is below 7%, affirming the HGS–XGBoost model’s adequate accuracy and generalization capabilities for new datasets. This study introduces a novel AI-based prediction method for FRCAC-CS, establishing the groundwork for its potential application in reef construction.