BACKGROUND: Preoperative evaluation is important, and this study explored the application of machine learning methods for anesthetic risk classification and the evaluation of the contributions of various factors. To minimize the effects of confounding variables during model training, we used a homogenous group with similar physiological states and ages undergoing similar pelvic organ-related procedures not involving malignancies.
OBJECTIVE: Data on women of reproductive age (age 20-50 years) who underwent gestational or gynecological surgery between January 1, 2017, and December 31, 2021, were obtained from the National Taiwan University Hospital Integrated Medical Database.
METHODS: We first performed an exploratory analysis and selected key features. We then performed data preprocessing to acquire relevant features related to preoperative examination. To further enhance predictive performance, we used the log-likelihood ratio algorithm to generate comorbidity patterns. Finally, we input the processed features into the light gradient boosting machine (LightGBM) model for training and subsequent prediction.
RESULTS: A total of 10,892 patients were included. Within this data set, 9893 patients were classified as having low anesthetic risk (American Society of Anesthesiologists physical status score of 1-2), and 999 patients were classified as having high anesthetic risk (American Society of Anesthesiologists physical status score of >2). The area under the receiver operating characteristic curve of the proposed model was 0.6831.
CONCLUSIONS: By combining comorbidity information and clinical laboratory data, our methodology based on the LightGBM model provides more accurate predictions for anesthetic risk classification.
BACKGROUND: Research Ethics Committee of the National Taiwan University Hospital 202204010RINB; https://www.ntuh.gov.tw/RECO/Index.action.

OBJECTIVE: Postoperative delirium is a neuropsychological syndrome that typically occurs in surgical patients. Its onset can lead to prolonged hospitalization as well as increased morbidity and mortality. Therefore, it is important to promptly identify its signs. This study aimed to develop and validate a machine learning predictive model for postoperative delirium using extensive population data.
METHODS: Retrospective observational study.
METHODS: Japanese Diagnosis Procedure Combination inpatient data. Data were used for internal (2016.4-2018.12) and temporal validation (2019.01-2019.10).
METHODS: Patients aged ≥65 years who underwent general anesthesia for surgical procedure.
METHODS: The primary outcome was postoperative delirium, which was defined as a condition requiring newly prescribed antipsychotic drugs or assignment of the corresponding insurance claim code after the date of surgery. We trained and tuned the optimal machine-learning model through 10-fold cross-validation using the selected optimal area under the receiver operating characteristic curve (AUC) value. In the temporal validation, we measured the performance of our model.
RESULTS: The analysis included 557,990 patients. The light-gradient boosting machine models showed a higher AUC value (0.826 [95% confidence interval (CI): 0.822-0.829]) than the other models. Regarding performance, the model had a recall value of 0.124 (95% CI: 0.119-0.129) and precision value of 0.659 (95% CI: 0.641-0.677]). This performance was sustained in the temporal validation (AUC, 0.815 [95% CI: 0.811-0.818]). At a sensitivity of 0.80, the model achieved a specificity of 0.672 (95% CI: 0.670-0.674]), a negative predictive value of 0.975 (95% CI: 0.974-0.975), and a positive predictive value of 0.176 (95% CI: 0.176-0.179).
CONCLUSIONS: Using extensive Diagnostic Procedure Combination data, we successfully created and validated a machine learning model for predicting postoperative delirium. This model may facilitate prediction of postoperative delirium.

BACKGROUND: Respiratory syncytial virus (RSV) affects children, causing serious infections, particularly in high-risk groups. Given the seasonality of RSV and the importance of rapid isolation of infected individuals, there is an urgent need for more efficient diagnostic methods to expedite this process.
OBJECTIVE: This study aimed to investigate the performance of a machine learning model that leverages the temporal diversity of symptom onset for detecting RSV infections and elucidate its discriminatory ability.
METHODS: The study was conducted in pediatric and emergency outpatient settings in Japan. We developed a detection model that remotely confirms RSV infection based on patient-reported symptom information obtained using a structured electronic template incorporating the differential points of skilled pediatricians. An extreme gradient boosting-based machine learning model was developed using the data of 4174 patients aged ≤24 months who underwent RSV rapid antigen testing. These patients visited either the pediatric or emergency department of Yokohama City Municipal Hospital between January 1, 2009, and December 31, 2015. The primary outcome was the diagnostic accuracy of the machine learning model for RSV infection, as determined by rapid antigen testing, measured using the area under the receiver operating characteristic curve. The clinical efficacy was evaluated by calculating the discriminative performance based on the number of days elapsed since the onset of the first symptom and exclusion rates based on thresholds of reasonable sensitivity and specificity.
RESULTS: Our model demonstrated an area under the receiver operating characteristic curve of 0.811 (95% CI 0.784-0.833) with good calibration and 0.746 (95% CI 0.694-0.794) for patients within 3 days of onset. It accurately captured the temporal evolution of symptoms; based on adjusted thresholds equivalent to those of a rapid antigen test, our model predicted that 6.9% (95% CI 5.4%-8.5%) of patients in the entire cohort would be positive and 68.7% (95% CI 65.4%-71.9%) would be negative. Our model could eliminate the need for additional testing in approximately three-quarters of all patients.
CONCLUSIONS: Our model may facilitate the immediate detection of RSV infection in outpatient settings and, potentially, in home environments. This approach could streamline the diagnostic process, reduce discomfort caused by invasive tests in children, and allow rapid implementation of appropriate treatments and isolation at home. The findings underscore the potential of machine learning in augmenting clinical decision-making in the early detection of RSV infection.

BACKGROUND: Care for patients with heart failure (HF) causes a substantial load on health care systems where a prominent challenge is the elevated rate of readmissions within 30 days following initial discharge. Clinical professionals face high levels of uncertainty and subjectivity in the decision-making process on the optimal timing of discharge. Unwanted hospital stays generate costs and cause stress to patients and potentially have an impact on care outcomes. Recent studies have aimed to mitigate the uncertainty by developing and testing risk assessment tools and predictive models to identify patients at risk of readmission, often using novel methods such as machine learning (ML).
OBJECTIVE: This study aims to investigate how a developed clinical decision support (CDS) tool alters the decision-making processes of health care professionals in the specific context of discharging patients with HF, and if so, in which ways. Additionally, the aim is to capture the experiences of health care practitioners as they engage with the system\'s outputs to analyze usability aspects and obtain insights related to future implementation.
METHODS: A quasi-experimental design with randomized crossover assessment will be conducted with health care professionals on HF patients\' scenarios in a region located in the South of Sweden. In total, 12 physicians and nurses will be randomized into control and test groups. The groups shall be provided with 20 scenarios of purposefully sampled patients. The clinicians will be asked to take decisions on the next action regarding a patient. The test group will be provided with the 10 scenarios containing patient data from electronic health records and an outcome from an ML-based CDS model on the risk level for readmission of the same patients. The control group will have 10 other scenarios without the CDS model output and containing only the patients\' data from electronic medical records. The groups will switch roles for the next 10 scenarios. This study will collect data through interviews and observations. The key outcome measures are decision consistency, decision quality, work efficiency, perceived benefits of using the CDS model, reliability, validity, and confidence in the CDS model outcome, integrability in the routine workflow, ease of use, and intention to use. This study will be carried out in collaboration with Cambio Healthcare Systems.
RESULTS: The project is part of the Center for Applied Intelligent Systems Research Health research profile, funded by the Knowledge Foundation (2021-2028). Ethical approval for this study was granted by the Swedish ethical review authority (2022-07287-02). The recruitment process of the clinicians and the patient scenario selection will start in September 2023 and last till March 2024.
CONCLUSIONS: This study protocol will contribute to the development of future formative evaluation studies to test ML models with clinical professionals.
UNASSIGNED: PRR1-10.2196/52744.

The escalating levels of carbon dioxide (CO2) emissions represent the primary driver of global warming, and addressing them is of paramount importance. Timely and accurate prediction, as well as effective control of CO2 emissions, are pivotal for guiding mitigation measures. This paper aims to select the best prediction model for near-real-time daily CO2 emissions in China. The prediction models are based on univariate daily time-series data spanning January 1st, 2020, to September 30st, 2022. Six models are proposed, including three statistical models: grey prediction (GM(1,1)), autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average with exogenous factors (SARIMAX), and three machine learning models: artificial neural network (ANN), random forest (RF), and long short-term memory (LSTM). The performance of these six models is evaluated using five criteria: mean squared error (MSE), root-mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2). Our findings reveal that the three machine learning models consistently outperform the three statistical models across all five criteria. Among them, the LSTM model demonstrates exceptional performance for daily CO2 emission prediction, boasting an impressively low MSE value of 3.5179e-04, an RMSE value of 0.0187, an MAE value of 0.0140, an MAPE value of 14.8291%, and a high R2 value of 0.9844. This underscores the robustness of the LSTM model in capturing and predicting complex emission patterns, positioning it as the most suitable option for near-real-time daily CO2 emission prediction based on the provided daily time series data. Moreover, our study\'s results provide valuable insights into emissions forecasting, enabling data-driven decision-making for policymakers and stakeholders. The accurate and timely predictions offered by the LSTM model can aid in the formulation of effective strategies to mitigate carbon emissions, contributing to a more sustainable future. Furthermore, the findings of this study can enhance our understanding of the dynamics of CO2 emissions, leading to more informed environmental policies and actions aimed at reducing carbon emissions.

UNASSIGNED: Colorectal cancer (CRC) is a heterogeneous group of malignancies distinguished by distinct clinical features. The association of these features with venous thromboembolism (VTE) is yet to be clarified. Machine learning (ML) models are well suited to improve VTE prediction in CRC due to their ability to receive the characteristics of a large number of features and understand the dataset to obtain implicit correlations.
UNASSIGNED: Data were extracted from 4,914 patients with colorectal cancer between August 2019 and August 2022, and 1,191 patients who underwent surgery on the primary tumor site with curative intent were included. The variables analyzed included patient-level factors, cancer-level factors, and laboratory test results. Model training was conducted on 30% of the dataset using a ten-fold cross-validation method and model validation was performed using the total dataset. The primary outcome was VTE occurrence in postoperative 30 days. Six ML algorithms, including logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), weighted support vector machine (SVM), a multilayer perception (MLP) network, and a long short-term memory (LSTM) network, were applied for model fitting. The model evaluation was based on six indicators, including receiver operating characteristic curve-area under the curve (ROC-AUC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV), and Brier score. Two previous VTE models (Caprini and Khorana) were used as the benchmarks.
UNASSIGNED: The incidence of postoperative VTE was 10.8%. The top ten significant predictors included lymph node metastasis, C-reactive protein, tumor grade, anemia, primary tumor location, sex, age, D-dimer level, thrombin time, and tumor stage. In our results, the XGBoost model showed the best performance, with a ROC-AUC of 0.990, a SEN of 96.9%, a SPE of 96.1% in training dataset and a ROC-AUC of 0.908, a SEN of 77.5%, a SPE of 93.7% in validation dataset. All ML models outperformed the previously developed models (Caprini and Khorana).
UNASSIGNED: This study developed postoperative VTE predictive models using six ML algorithms. The XGBoost VTE model might supply a complementary tool for clinical VTE prophylaxis decision-making and the proposed risk factors could shed some light on VTE risk stratification in CRC patients.

A landslide is one of the most destructive natural disasters in the world. The accurate modeling and prediction of landslide hazards have been used as some of the vital tools for landslide disaster prevention and control. The purpose of this study was to explore the application of coupling models in landslide susceptibility evaluation. This paper used Weixin County as the research object. First, according to the landslide catalog database constructed, there were 345 landslides in the study area. Twelve environmental factors were selected, including terrain (elevation, slope, slope direction, plane curvature, and profile curvature), geological structure (stratigraphic lithology and distance from fault zone), meteorological hydrology (average annual rainfall and distance to rivers), and land cover (NDVI, land use, and distance to roads). Then, a single model (logistic regression, support vector machine, and random forest) and a coupled model (IV-LR, IV-SVM, IV-RF, FR-LR, FR-SVM, and FR-RF) based on information volume and frequency ratio were constructed, and the accuracy and reliability of the models were compared and analyzed. Finally, the influence of environmental factors on landslide susceptibility under the optimal model was discussed. The results showed that the prediction accuracy of the nine models ranged from 75.2% (LR model) to 94.9% (FR-RF model), and the coupling accuracy was generally higher than that of the single model. Therefore, the coupling model could improve the prediction accuracy of the model to a certain extent. The FR-RF coupling model had the highest accuracy. Under the optimal model FR-RF, distance from the road, NDVI, and land use were the three most important environmental factors, ac-counting for 20.15%, 13.37%, and 9.69%, respectively. Therefore, it was necessary for Weixin County to strengthen the monitoring of mountains near roads and areas with sparse vegetation to prevent landslides caused by human activities and rainfall.

Mangrove is an important carbon sink, as it can achieve climate regulation by sequestering carbon dioxide in the atmosphere. However, 50 % of mangrove species are threatened with extinction in China, and the carbon stocks in vegetation has also dropped by 53.1 %. Here, we couple remote sensing data with Random Forests, Support Vector Machines, and XGBoost to analyse mangroves in mainland China from 1986 to 2019. We find that aquaculture has crucial impacts on mangroves and prediction error. Future predictions indicate that the changes of mangroves in different cities range from -5.09E+06 m2 to 2.30E+06 m2, and soil carbon(C) stocks is \"-1.90E+05 Mg ~ 8.57E+04 Mg\". To protect mangroves, exploring the balance between aquaculture and mangroves and paying attention to the sustainable transformation of aquaculture are urgently required. In this way, mangroves can fully play the role of carbon sequestration and contribute to China\'s dual carbon goals.

Chordoma and chondrosarcoma share common radiographic characteristics yet are distinct clinically. A radiomic machine learning model differentiating these tumors preoperatively would help plan surgery. MR images were acquired from 57 consecutive patients with chordoma (N = 32) or chondrosarcoma (N = 25) treated at the University of Tokyo Hospital between September 2012 and February 2020. Preoperative T1-weighted images with gadolinium enhancement (GdT1) and T2-weighted images were analyzed. Datasets from the first 47 cases were used for model creation, and those from the subsequent 10 cases were used for validation. Feature extraction was performed semi-automatically, and 2438 features were obtained per image sequence. Machine learning models with logistic regression and a support vector machine were created. The model with the highest accuracy incorporated seven features extracted from GdT1 in the logistic regression. The average area under the curve was 0.93 ± 0.06, and accuracy was 0.90 (9/10) in the validation dataset. The same validation dataset was assessed by 20 board-certified neurosurgeons. Diagnostic accuracy ranged from 0.50 to 0.80 (median 0.60, 95% confidence interval 0.60 ± 0.06%), which was inferior to that of the machine learning model (p = 0.03), although there are some limitations, such as the risk of overfitting and the lack of an extramural cohort for truly independent final validation. In summary, we created a novel MRI-based machine learning model to differentiate skull base chordoma and chondrosarcoma from multiparametric signatures.

Stratification of the risk of lymph node metastasis (LNM) in patients with non-curative resection after endoscopic resection (ER) for early gastric cancer (EGC) is crucial in determining additional treatment strategies and preventing unnecessary surgery. Hence, we developed a machine learning (ML) model and validated its performance for the stratification of LNM risk in patients with EGC. We enrolled patients who underwent primary surgery or additional surgery after ER for EGC between May 2005 and March 2021. Additionally, patients who underwent ER alone for EGC between May 2005 and March 2016 and were followed up for at least 5 years were included. The ML model was built based on a development set (70%) using logistic regression, random forest (RF), and support vector machine (SVM) analyses and assessed in a validation set (30%). In the validation set, LNM was found in 337 of 4428 patients (7.6%). Among the total patients, the area under the receiver operating characteristic (AUROC) for predicting LNM risk was 0.86 in the logistic regression, 0.85 in RF, and 0.86 in SVM analyses; in patients with initial ER, AUROC for predicting LNM risk was 0.90 in the logistic regression, 0.88 in RF, and 0.89 in SVM analyses. The ML model could stratify the LNM risk into very low (<1%), low (<3%), intermediate (<7%), and high (≥7%) risk categories, which was comparable with actual LNM rates. We demonstrate that the ML model can be used to identify LNM risk. However, this tool requires further validation in EGC patients with non-curative resection after ER for actual application.