Risk prediction model for postoperative cognitive dysfunction after total knee replacement based on Bayesian network algorithm

Objective To establish a prediction model of risk for postoperative cognitive dysfunction (POCD) after total knee replacement (TKR) by Bayesian network (BN) algorithm and investigate its predictive performance. Methods A case-control trial was conducted on 1 260 inpatients who underwent TKR from January 2017 to December 2021 in the Department of Joint Surgery of our hospital. Their main diagnosis of inclusion was severe osteoarthritis of left/right knee joint. They were 240 cases of male (19.0%) and 1 020 cases of female (81.0%), at an average age of 66.73±8.46 (23~79) years and a mean body mass index (BMI) of 25.08±5.08 kg/m2. The POCD patients (n=71) after surgery (from the end of surgery to discharge) were randomly divided into A1 group and B1 group at a ratio of 7∶3, and those without POCD (1 189 cases) were also randomly divided into A2 group and B2 group at a same ratio. The patients from A1 and A2 groups were combined together as training set, and those out of B1 and B2 groups as test set. Thirty-six indexes related to perioperative anesthesia decision, disease outcome and length of stay in TKR were selected as nodes, and the probability distribution model diagram of each node was established by using BN algorithm to predict the probability of risk for POCD, so as to minimize the length of stay and promote the maximum recovery of patients. Results The prediction model of risk for POCD after TKR was established based on BN algorithm. The area value under receiver operating characteristic curve (ROC-AUC) of the training set was 0.966 1 (95% CI: 0.954 1~0.978 4), and the ROC-AUC value of the test set was 0.897 4 (95% CI: 0.867 2~0.928 5), with an accuracy of 96.43% (95%CI: 0.951 1~0.976 4) and 93.44% (95% CI: 0.909 2~0.959 6), respectively. Conclusion Our risk prediction model for POCD after TKR based on BN algorithm has good performance and high accurac

Establishment of prediction model for risk of postoperative cognitive dysfunction after non-cardiac surgery based on different machine learning algorithms

Objective To establish a risk model for predicting postoperative cognitive dysfunction (POCD) after non-cardiac surgery using preoperative indicators based on machine learning algorithm. Methods A case-control study was designed, and conducted on the POCD patients after non-cardiac surgery in the medical big data platform of our hospital from January 2014 to January 2019. Finally, 92 patients were included in the POCD group. According to surgical type and age matched of the POCD group, another 276 patients who did not develop POCD after surgery and discharged from the hospital during the same time period from the same big data platform were assigned into the non-POCD group at a ratio of 1∶3. At the same time, these 368 patients were randomly divided into modeling group (n=259) and validation group (n=109) at a ratio of 7∶3. After data preprocessing and feature selection of preoperative clinical indicators (general data, relevant scoring scales, surgical-related data and results of preoperative laboratory tests), the risk prediction model of POCD for non-cardiac surgery was established based on 3 machine learning algorithms, that is, logistic regression (LR), support vector machine (SVM) and Decision Tree. The model efficacy was evaluated by sensitivity, specificity, F1 score, and the area under the receiver operating characteristic curve (AUC). Results The SVM algorithm was the best model among the 3 machine learning algorithms to predict the risk of POCD after non-cardiac surgery. The AUC value of the model in the validation group was 0.957 (95%CI: 0.905~1.000), with a sensitivity and specificity of 92.6% and 98.8%, respectively. Conclusion A prediction model of POCD after non-cardiac surgery is successfully established based on machine learning algorithm. This model shows good predictive performance for POCD. [Key words] machine learning , prediction model , postoperative cognitive dysfunction

P_VggNet: A convolutional neural network (CNN) with pixel-based attention map.

Attention maps have been fused in the VggNet structure (EAC-Net) [1] and have shown significant improvement compared to that of the VggNet structure. However, in [1], E-Net was designed based on the facial action unit (AU) center and for facial AU detection only. Thus, for the use of attention maps in every image type, this paper proposed a new convolutional neural network (CNN) structure, P_VggNet, comprising the following parts: P_Net and VggNet with 16 layers (VggNet-16). The generation approach of P_Net was designed, and the P_VggNet structure was proposed. To prove the efficiency of P_VggNet, we designed two experiments, which indicated that P_VggNet could more efficiently extract image features than VggNet-16

An Improved Arithmetic Optimization Algorithm and Its Application to Determine the Parameters of Support Vector Machine

The arithmetic optimization algorithm (AOA) is a new metaheuristic algorithm inspired by arithmetic operators (addition, subtraction, multiplication, and division) to solve arithmetic problems. The algorithm is characterized by simple principles, fewer parameter settings, and easy implementation, and has been widely used in many fields. However, similar to other meta-heuristic algorithms, AOA suffers from shortcomings, such as slow convergence speed and an easy ability to fall into local optimum. To address the shortcomings of AOA, an improved arithmetic optimization algorithm (IAOA) is proposed. First, dynamic inertia weights are used to improve the algorithm’s exploration and exploitation ability and speed up the algorithm’s convergence speed; second, dynamic mutation probability coefficients and the triangular mutation strategy are introduced to improve the algorithm’s ability to avoid local optimum. In order to verify the effectiveness and practicality of the algorithm in this paper, six benchmark test functions are selected for the optimization search test verification to verify the optimization search ability of IAOA; then, IAOA is used for the parameter optimization of support vector machines to verify the practical ability of IAOA. The experimental results show that IAOA has a strong global search capability, and the optimization-seeking capability is significantly improved, and it shows excellent performance in support vector machine parameter optimization

A self‐supervised causal feature reinforcement learning method for non‐invasive hemoglobin prediction

Abstract Anemia (hemoglobin (Hb) < 12.0 g/dL) is significantly correlated with many diseases. An invasive technique is the peripheral blood Hb detection method, which is used to examine red and white blood cells and platelets in clinical laboratory settings. However, non‐invasive methods for measuring Hb mainly include low‐precision prediction based on eye images and complex operation prediction based on fundus images. Moreover, these types of anemia testing techniques are time‐consuming, tedious, or prone to errors. Thus, developing a convenient and high‐precision method is vital for predicting Hb concentration. This study proposes self‐supervised causal features using actor‐critical reinforcement learning to improve the model prediction performance. Two networks are proposed: Actor Predictor and Hemoglobin Predictor to predict Hb concentration. Moreover, the model performance is evaluated using different techniques, namely, Mean Absolute Error (MAE) and Mean Square Error (MSE), via real eye image data and a smartphone. This model achieved 1.19(1.01,1.38) on the MAE and 2.25(1.59,2.90) on the MSE, which outperformed previous eye images' Hb prediction methods and was nearly similar to the fundus images' Hb prediction methods. The inference time was less than 0.05 s, making it efficient and accurate for predicting Hb. This model can be used for mobile deployment and health self‐screening

Table_1_Two-stage hemoglobin prediction based on prior causality.docx

IntroductionPerioperative hemoglobin (Hb) levels can influence tissue metabolism. For clinical physicians, precise Hb concentration greatly contributes to intraoperative blood transfusion. The reduction in Hb during an operation weakens blood's oxygen-carrying capacity and poses threats to multiple systems and organs of the whole body. Patients can die from perioperative anemia. Thus, a timely and accurate non-invasive prediction for patients' Hb content is of enormous significance.MethodIn this study, targeted toward the palpebral conjunctiva images in perioperative patients, a non-invasive model for predicting Hb levels is constructed by means of deep neural semantic segmentation and a convolutional network based on a priori causal knowledge, then an automatic framework was proposed to predict the precise concentration value of Hb. Specifically, according to a priori causal knowledge, the palpebral region was positioned first, and patients' Hb concentration was subjected to regression prediction using a neural network. The model proposed in this study was experimented on using actual medical datasets.ResultsThe R2 of the model proposed can reach 0.512, the explained variance score can reach 0.535, and the mean absolute error is 1.521.DiscussionIn this study, we proposed to predict the accurate hemoglobin concentration and finally constructed a model using the deep learning method to predict eyelid Hb of perioperative patients based on the a priori casual knowledge.</p