Creating a delirium risk assessment model for geriatric patients with femoral neck fractures through artificial intelligence techniques: a theoretical exploration.

Background: Postoperative delirium (POD) is a common occurrence following orthopedic surgery, particularly in the older population. However, there is a relative scarcity of research on the use of intelligent algorithms to predict POD in older patients after orthopedic surgery. Therefore, the objective of this study was to evaluate the efficacy of ten distinct intelligent algorithms in predicting POD in older patients undergoing femoral neck fracture surgery.

Development of a LASSO machine learning algorithm-based model for postoperative delirium prediction in hepatectomy patients.

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.

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.

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.

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.

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.

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.

Predicting postoperative delirium after microvascular decompression surgery with machine learning.

Objective: The aim of this study was to predict early delirium after microvascular decompression using machine learning.

Design: Retrospective cohort study.

Setting: Second Hospital of Lanzhou University.

A comparative study of machine learning algorithms for predicting acute kidney injury after liver cancer resection.

Objective: Machine learning methods may have better or comparable predictive ability than traditional analysis. We explore machine learning methods to predict the likelihood of acute kidney injury after liver cancer resection.

Methods: This is a secondary analysis cohort study.

Machine Learning Algorithms for Predicting the Recurrence of Stage IV Colorectal Cancer After Tumor Resection.

The aim of this study is to explore the feasibility of using machine learning (ML) technology to predict postoperative recurrence risk among stage IV colorectal cancer patients. Four basic ML algorithms were used for prediction-logistic regression, decision tree, GradientBoosting and lightGBM. The research samples were randomly divided into a training group and a testing group at a ratio of 8:2.

Preoperative platelet-to-lymphocyte ratio is a valuable prognostic biomarker in patients with hepatocellular carcinoma undergoing curative liver resection.

The Platelet to lymphocyte ratio (PLR) has been reported to predict prognosis of patients with hepatocellular carcinoma (HCC). This study examined the prognostic potential of stratified PLR for HCC patients undergoing curative liver resection. Medical records were retrospectively analyzed for 778 HCC patients undergoing curative liver resection at the Affiliated Tumor Hospital of Guangxi Medical University and the First People's Hospital of Changde between April 2010 and October 2013.