A predictive model for post-thoracoscopic surgery pulmonary complications based on the PBNN algorithm.

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.

Artificial intelligence algorithms for predicting post-operative ileus after laparoscopic 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.

Predicting early postoperative PONV using multiple machine-learning- and deep-learning-algorithms.

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.

A long duration of intraoperative hypotension is associated with postoperative delirium occurrence following thoracic and orthopedic surgery in elderly.

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.

Differentiation of Bone Metastasis in Elderly Patients With Lung Adenocarcinoma Using Multiple Machine Learning Algorithms.

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.

Predicting postoperative gastric cancer prognosis based on inflammatory factors and machine learning technology.

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.

Predicting difficult airway intubation in thyroid surgery using multiple machine learning and deep learning algorithms.

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.

Non-linear relationship of gamma-glutamyl transpeptidase to lymphocyte count ratio with the recurrence of hepatocellular carcinoma with staging I-II: a retrospective cohort study.

Background: High recurrence rate was a major factor for the poor postoperative prognosis of hepatocellular carcinoma (HCC) patients. The present study was intended to evaluate the association of gamma-glutamyl transpeptidase to lymphocyte count ratio (GLR) and the recurrence of HCC with staging I-II in Chinese.

Methods: The retrospective cohort data was derived from the First Affiliated Hospital of Zhengzhou University from January 2014 to December 2018 on 496 patients who underwent radical resection of HCC with staging I-II.

Using Nomograms to Predict the PPCs of Patients With Diffuse Peritonitis Undergoing Emergency Gastrointestinal Surgery.

To develop and validate a nomogram model for predicting postoperative pulmonary complications (PPCs) in patients with diffuse peritonitis undergoing emergency gastrointestinal surgery. We used the least absolute shrinkage and selection operator (LASSO) regression model to analyze the independent risk factors for PPCs in patients with diffuse peritonitis who underwent emergency gastrointestinal surgery. Using R, we developed and validated a nomogram model for predicting PPCs in patients with diffuse peritonitis undergoing emergency gastrointestinal surgery.

Quadratus Lumborum Block Spares Postoperative Opioid Usage but Does Not appear to Prevent the Development of Chronic Pain After Gastrointestinal Surgery.

Background: Regional anesthesia has been used to reduce acute postsurgical pain and to  prevent chronic pain. The best technique, however, remains controversial.

Objectives: The aim of this study was to assess the short- and long-term postoperative analgesic efficacy of ultrasound-guided quadratus lumborum block (QLB) in open gastrointestinal surgery.

Developing Machine Learning Algorithms to Predict Pulmonary Complications After Emergency Gastrointestinal Surgery.

Investigate whether machine learning can predict pulmonary complications (PPCs) after emergency gastrointestinal surgery in patients with acute diffuse peritonitis. This is a secondary data analysis study. We use five machine learning algorithms (Logistic regression, DecisionTree, GradientBoosting, Xgbc, and gbm) to predict postoperative pulmonary complications.

Machine Learning Can Predict Total Death After Radiofrequency Ablation in Liver Cancer Patients.

Objective: Over 1 million new cases of hepatocellular carcinoma (HCC) are diagnosed worldwide every year. Its prognosis remains poor, and the 5-year survival rate in all disease stages is estimated to be between 10% and 20%. Radiofrequency ablation (RFA) has become an important local treatment for liver cancer, and machine learning (ML) can provide many shortcuts for liver cancer medical research.

Predicting lung adenocarcinoma prognosis with a novel risk scoring based on platelet-related gene expression.

Lung adenocarcinoma is the most common subtype of non-small cell lung cancer, and platelet receptor-related genes are related to its occurrence and progression. A new prognostic indicator based on platelet receptor-related genes was developed with multivariate COX analysis. Prognostic markers based on platelet-related risk score perform moderately in prognosis prediction.

Predictors of Acute Postsurgical Pain following Gastrointestinal Surgery: A Prospective Cohort Study.

Background: Several predictors have been shown to be independently associated with chronic postsurgical pain for gastrointestinal surgery, but few studies have investigated the factors associated with acute postsurgical pain (APSP). The aim of this study was to identify the predictors of APSP intensity and severity through investigating demographic, psychological, and clinical variables.

Methods: We performed a prospective cohort study of 282 patients undergoing gastrointestinal surgery to analyze the predictors of APSP.

Predicting postoperative liver cancer death outcomes with machine learning.

Objective: To investigate the effect of 5 machine learning algorithms in predicting total hepatocellular carcinoma (HCC) postoperative death outcomes.

Methods: This study was a secondary analysis. A prognosis model was established using machine learning with python.

A machine learning-based predictor for the identification of the recurrence of patients with gastric cancer after operation.

To explore the predictive performance of machine learning on the recurrence of patients with gastric cancer after the operation. The available data is divided into two parts. In particular, the first part is used as a training set (such as 80% of the original data), and the second part is used as a test set (the remaining 20% of the data).

Machine learning predicts lymph node metastasis of poorly differentiated-type intramucosal gastric cancer.

To construct a machine learning algorithm model of lymph node metastasis (LNM) in patients with poorly differentiated-type intramucosal gastric cancer. 1169 patients with postoperative gastric cancer were divided into a training group and a test group at a ratio of 7:3. The model for lymph node metastasis was established with python machine learning.

Identifying Common Genes Related to Platelet and Immunity for Lung Adenocarcinoma Prognosis Prediction.

Background: Although 1000s of immune-related and platelet receptor-related genes have been identified in lung adenocarcinoma, their role in prognosis prediction remains unclear.

Methods: We downloaded mRNA data from the Cancer Genome Atlas Dataset (TCGA), and GSE68465 or GSE14814 data sets from the Gene Expression Omnibus (GEO) database.

Results: The high-risk group's overall survival (OS) time was lower than that of the low-risk group's in TCGA ( = 1.

Predicting Peritoneal Metastasis of Gastric Cancer Patients Based on Machine Learning.

Objective: The aim is to explore the prediction effect of 5 machine learning algorithms on peritoneal metastasis of gastric cancer.

Methods: 1080 patients with postoperative gastric cancer were divided into a training group and test group according to the ratio of 7:3. The model of peritoneal metastasis was established by using 5 machine learning (gbm(Light Gradient Boosting Machine), GradientBoosting, forest, Logistic and DecisionTree).

Machine learning to predict the cancer-specific mortality of patients with primary non-metastatic invasive breast cancer.

Purpose: We used five machine-learning algorithms to predict cancer-specific mortality after surgical resection of primary non-metastatic invasive breast cancer.

Methods: This study was a secondary analysis of data for 1661 women with primary non-metastatic invasive breast cancer. The overall patient population was divided into a training group and a test group at a ratio of 8:2 and python was used for machine learning to establish the prognosis model.

A highly colorimetric photonic film composed of non-close-packed melanin-like colloidal arrays.

Hothesis: Because of their flexible structure and adjustable color, structural colors with non-close-packed colloidal crystal arrays (NCCAs) have broad applications. However, most of these structural colors are limited by an approximate refractive index or high background scattering, and they present an unsatisfactory color that seriously hinders their practical application. Preparation of particles with a high refractive index or adsorption coefficient may be an effective approach to construct highly colorimetric NCCA structural colors in a nonaqueous solvent.