Cheng-Mao Zhou’s research while affiliated with First Affiliated Hospital of China Medical University and other places

Objective The objective of this study was to develop and validate a clinically applicable nomogram for predicting the risk of delirium following hepatectomy. Methods We applied the LASSO regression model to identify the independent risk factors associated with POD. Subsequently, we utilized R software to develop and validate a nomogram model capable of accurately predicting the incidence of POD. Results The final variables selected by the LASSO method were: Ramelteon, Age, Sex, Alcohol, Viral status, Cardiovascular disease, ASA class, Total bilirubin, Prothrombin time, Laparoscopic approach, and Blood transfusion. The performance of the nomogram was measured using ROC curve analysis, with an AUC of 0.854 (95% CI: 0.794–0.914) for the model. At the optimal cutoff value, the model demonstrated a sensitivity of 91.9% and a specificity of 68.8%. Model validation was performed using internal bootstrap validation to further verify the regression analysis. The ROC curve was generated by repeating the bootstrapping process 500 times, resulting in an AUC of 0.848 (95% CI: 0.786–0.904) for the model. The DCA curve representing the net benefit demonstrated the strong clinical validity of the model in predicting postoperative delirium. Conclusion Our results demonstrated that LASSO-based regression effectively constructed a nomogram model for predicting post-hepatectomy delirium.

Objective The primary objective of this study was to assess the potential of artificial intelligence techniques, in conjunction with transthoracic echocardiography (TEE) examinations, to forecast postoperative mortality outcomes in patients undergoing moderate-to-high-risk noncardiac surgeries. Methods This is a second retrospective analysis using the BioStudies public database. This dataset includes data from two medical centers. We partitioned the dataset utilizing a 7:3 ratio. This model seamlessly integrated diverse algorithms, encompassing both machine learning and deep learning methodologies such as logistic regression, gradient boosting decision tree, XGBoost, LightGBM, CatBoost, linear support vector classification, multilayer perceptron classifier, Gaussian Naive Bayes, Adaboost, recurrent neural network, convolutional neural network, Bayesian neural network, and probabilistic Bayesian neural network. To thoroughly evaluate the model's performance, we employed multiple metrics, including the receiver operating characteristic curve, accuracy, precision, F1 score, recall, calibration curve, and clinical decision curve. Results The present study included a total of 1453 patients. The Gbdt algorithm ranks the variable importance, and the top five important results are creatinine (Cr), creatinine exceeding twice the upper limit (Cr > 2), creatinine clearance, left ventricular end-diastolic internal diameter, and hemoglobin. Among these algorithms, only Gbdt algorithm yielded satisfactory results both in the training and test groups. In the training group, Gbdt had an area under the curve (AUC) value of 0.904, accuracy of 0.984, and precision of 1; In the testing group, Gbdt had an AUC value of 0.835, accuracy of 0.984, and precision of 0.5. However, the Gbdt algorithm demonstrated suboptimal performance in terms of recall rate and F1 score. Finally, we successfully developed an online intelligent prediction webpage that utilizes the Gbdt algorithm and TEE. Conclusions Gbdt represents an optimal approach for predicting postoperative mortality among patients undergoing non-cardiac surgery with moderate-to-high risk.

We constructed an early prediction model for postoperative pulmonary complications after thoracoscopic surgery using machine learning and deep learning algorithms. The artificial intelligence prediction models were built in Python, primarily using artificial intelligencealgorithms including both machine learning and deep learning algorithms. Correlation analysis showed that postoperative pulmonary complications were positively correlated with age and surgery duration, and negatively correlated with serum albumin. Using the light gradient boosting machine(LGBM) algorithm, weighted feature engineering revealed that single lung ventilation duration, history of smoking, surgery duration, ASA score, and blood glucose were the main factors associated with postoperative pulmonary complications. Results of artificial intelligence algorithms for predicting pulmonary complications after thoracoscopy in the test group: In terms of accuracy, the two best algorithms were Logistic Regression (0.831) and light gradient boosting machine(0.827); in terms of precision, the two best algorithms were Gradient Boosting (0.75) and light gradient boosting machine (0.742); in terms of recall, the three best algorithms were gaussian naive bayes (0.581), Logistic Regression (0.532), and pruning Bayesian neural network (0.516); in terms of F1 score, the two best algorithms were LogisticRegression (0.589) and pruning Bayesian neural network (0.566); and in terms of Area Under Curve(AUC), the two best algorithms were light gradient boosting machine(0.873) and pruning Bayesian neural network (0.869). The results of this study suggest that pruning Bayesian neural network (PBNN) can be used to assess the possibility of pulmonary complications after thoracoscopy, and to identify high-risk groups prior to surgery.

Objective By constructing a predictive model using machine learning and deep learning technologies, we aim to understand the risk factors for postoperative intestinal obstruction in laparoscopic colorectal cancer patients, and establish an effective artificial intelligence-based predictive model to guide individualized prevention and treatment, thus improving patient outcomes. Methods We constructed a model of the artificial intelligence algorithm in Python. Subjects were randomly assigned to either a training set for variable identification and model construction, or a test set for testing model performance, at a ratio of 7:3. The model was trained with ten algorithms. We used the AUC values of the ROC curves, as well as accuracy, precision, recall rate and F1 scores. Results The results of feature engineering composited with the GBDT algorithm showed that opioid use, anesthesia duration, and body weight were the top three factors in the development of POI. We used ten machine learning and deep learning algorithms to validate the model, and the results were as follows: the three algorithms with best accuracy were XGB (0.807), Decision Tree (0.807) and Neural DecisionTree (0.807); the two algorithms with best precision were XGB (0.500) and Decision Tree (0.500); the two algorithms with best recall rate were adab (0.243) and Decision Tree (0.135); the two algorithms with highest F1 score were adab (0.290) and Decision Tree (0.213); and the three algorithms with best AUC were Gradient Boosting (0.678), XGB (0.638) and LinearSVC (0.633). Conclusion This study shows that XGB and Decision Tree are the two best algorithms for predicting the risk of developing ileus after laparoscopic colon cancer surgery. It provides new insight and approaches to the field of postoperative intestinal obstruction in colorectal cancer through the application of machine learning techniques, thereby improving our understanding of the disease and offering strong support for clinical decision-making.

Objective PONV reduces patient satisfaction and increases hospital costs as patients remain in the hospital for longer durations. In this study, we build a preliminary artificial intelligence algorithm model to predict early PONV in patients. Methods We use R for statistical analysis and Python for the machine learning prediction model. Results Average characteristic engineering results showed that haloperidol, sex, age, history of smoking, and history of PONV were the first 5 contributing factors in the occurrence of early PONV. Test group results for artificial intelligence prediction of early PONV: in terms of accuracy, the four best algorithms were CNNRNN (0.872), Decision Tree (0.868), SVC (0.866) and adab (0.865); in terms of precision, the three best algorithms were CNNRNN (1.000), adab (0.400) and adab (0.868); in terms of AUC, the top three algorithms were Logistic Regression (0.732), SVC (0.731) and adab (0.722). Finally, we built a website to predict early PONV online using the Streamlit app on the following website: (https://zhouchengmao-streamlit-app-lsvc-ad-st-app-lsvc-adab-ponv-m9ynsb.streamlit.app/). Conclusion Artificial intelligence algorithms can predict early PONV, whereas logistic regression, SVC and adab were the top three artificial intelligence algorithms in overall performance. Haloperidol, sex, age, smoking history, and PONV history were the first 5 contributing factors associated with early PONV.

Background: Postoperative delirium (POD) is a common surgical complication associated with increased morbidity and mortality in elderly. Although the underlying mechanisms remain elusive, perioperative risk factors were reported to be closely related to its development. This study was designed to investigate the association between the duration of intraoperative hypotension and POD incidence following thoracic and orthopedic surgery in elderly. Method: The perioperative data from 605 elderly undergoing thoracic and orthopedic surgery from January 2021 to July 2022 were analyzed. The primary exposure was a cumulative duration of mean arterial pressure (MAP) ≤ 65 mmHg. The primary end-point was the POD incidence assessed with confusion assessment method (CAM) or CAM-ICU for three days after surgery. Restricted cubic spline (RCS) was conducted to examine the continuous relationship between the duration of intraoperative hypotension and POD incidence adjusted with patients' demographics and surgery related factors. Then the duration of intraoperative hypotension was categorized into three groups: no hypotension, short (< 5 mins) or long duration (≥ 5 mins) of hypotension for further analysis. Result: The incidence of POD was 14.7% (89 cases out of 605) within three days after surgery. The duration of hypotension presented a non-linear and "inverted L-shaped" effect on POD development. Compared to no hypotension, long duration (adjusted OR 3.93; 95% CI: 2.07-7.45; P < 0.001) rather than short duration of MAP ≤65 mmHg (adjusted OR 1.18; 95% CI: 0.56-2.50; P = 0.671) was closely related to the POD incidence. Conclusion: Intraoperative hypotension (MAP ≤65 mmHg) for ≥5 mins was associated with an increased incidence of POD after thoracic and orthopedic surgery in elderly.

Objective: We tested the performance of general machine learning and joint machine learning algorithms in the classification of bone metastasis, in patients with lung adenocarcinoma. Methods: We used R version 3.5.3 for statistical analysis of the general information, and Python to construct machine learning models. Results: We first used the average classifiers of the 4 machine learning algorithms to rank the features and the results showed that race, sex, whether they had surgery and marriage were the first 4 factors affecting bone metastasis. Machine learning results in the training group: for area under the curve (AUC), except for RF and LR, the AUC values of all machine learning classifiers were greater than .8, but the joint algorithm did not improve the AUC for any single machine learning algorithm. Among the results related to accuracy and precision, the accuracy of other machine learning classifiers except the RF algorithm was higher than 70%, and only the precision of the LGBM algorithm was higher than 70%. Machine learning results in the test group: Similarly, for areas under the curve (AUC), except RF and LR, the AUC values for all machine learning classifiers were greater than .8, but the joint algorithm did not improve the AUC value for any single machine learning algorithm. For accuracy, except for the RF algorithm, the accuracy of other machine learning classifiers was higher than 70%. The highest precision for the LGBM algorithm was .675. Conclusion: The results of this concept verification study show that machine learning algorithm classifiers can distinguish the bone metastasis of patients with lung cancer. This will provide a new research idea for the future use of non-invasive technology to identify bone metastasis in lungcancer. However, more prospective multicenter cohort studies are needed.

Objective There is a strong association between gastric cancer and inflammatory factors. Many studies have shown that machine learning can predict cancer patients’ prognosis. However, there has been no study on predicting gastric cancer death based on machine learning using related inflammatory factor variables. Methods Six machine learning algorithms are applied to predict total gastric cancer death after surgery. Results The Gradient Boosting Machine (GBM) algorithm factors accounting for the prognosis weight outcome show that the three most important factors are neutrophil-lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR) and age. The total postoperative death model showed that among patients with gastric cancer from the predictive test group: The highest accuracy was LR (0.759), followed by the GBM algorithm (0.733). For the six algorithms, the AUC values, from high to low, were LR, GBM, GBDT, forest, Tr and Xgbc. Among the six algorithms, Logistic had the highest precision (precision = 0.736), followed by the GBM algorithm (precision = 0.660). Among the six algorithms, GBM had the highest recall rate (recall = 0.667). Conclusion Postoperative mortality from gastric cancer can be predicted based on machine learning.

Background In this paper, we examine whether machine learning and deep learning can be used to predict difficult airway intubation in patients undergoing thyroid surgery. Methods We used 10 machine learning and deep learning algorithms to establish a corresponding model through a training group, and then verify the results in a test group. We used R for the statistical analysis and constructed the machine learning prediction model in Python. Results The top 5 weighting factors for difficult airways identified by the average algorithm in machine learning were age, sex, weight, height, and BMI. In the training group, the AUC values and accuracy and the Gradient Boosting precision were 0.932, 0.929, and 100%, respectively. As for the modeled effects of predicting difficult airways in test groups, among the models constructed by the 10 algorithms, the three algorithms with the highest AUC values were Gradient Boosting, CNN, and LGBM, with values of 0.848, 0.836, and 0.812, respectively; In addition, among the algorithms, Gradient Boosting had the highest accuracy with a value of 0.913; Additionally, among the algorithms, the Gradient Boosting algorithm had the highest precision with a value of 100%. Conclusion According to our results, Gradient Boosting performed best overall, with an AUC >0.8, an accuracy >90%, and a precision of 100%. Besides, the top 5 weighting factors identified by the average algorithm in machine learning for difficult airways were age, sex, weight, height, and BMI.