To investigate the predict effect of machine learning on POI underwent laparoscopic colorectal cancer surgery, we conducted a retrospective observational study at Nanjing Medical University Affiliated Suzhou Hospital.
Participants
We performed a retrospective analysis of consecutive patients aged 18 years or older who underwent laparoscopic colorectal surgery for malignant lesions from April 2016 to January 2017. Exclusion criteria were patients who underwent surgery other than laparoscopic colorectal surgery, converted to open surgery, robot-assisted laparoscopic colorectal surgery, and parenteral nutrition surgery. POI was defined as flatulence and/or fecal pass delay or oral intake intolerance on the third day after surgery and confirmed with radiographs that small and/or large intestinal dilatation on abdominal X-ray films.
Anesthesia and operation management
The surgeries were performed by six different surgeons, each with more than 200 experiences in laparoscopic colorectal surgery. Laparoscopic surgery includes single incision and conventional laparoscopic colorectal surgery. Anesthesia techniques were similar in all cases. There was no thoracic epidural analgesia. Intravenous midazolam,sufentanil, propofol, and rocuronium were applied for induction of anesthesia, providing neuromuscular blockage for endotracheal intubation. Anesthesia was maintained with propofol, remifentanil, and sevoflurane. Opioids were routinely administered for postoperative pain 30 minutes prior to the end of surgery.
Variables collection
Data on patient demographics, social habits, comorbidities, intraoperative data (duration of surgery and anesthesia, type of surgery and anesthesia, quantity of intravenous infusion, estimated blood loss) and postoperative analgesia were collected (Maximum pain score [NRS] and cumulative dose of opioid used on the third day after surgery). All opioid administrations were converted to equivalent doses of intravenous morphine. The age-adjusted Charlson Comorbidity Index was used to assess comorbidity. The original Charlson Comorbidity Index was coined in 1987 and it was calculated by summing the weighted scores of 19 medical conditions. Since age was determined to be an important factor in overall survival, the patient's age subsequently acted as a correction variable in the final Charlson index score. It is reported that this modification of the Charlson Comorbidity Index called age adjustment has better predictive effect on hospital mortality and adverse events than other versions of the Charlson Comorbidity Index. Events such as postoperative wound dehiscence were also recorded.
Machine learning algorithm
Logistic regression is one of the most commonly used and most classical classification methods in machine learning. Although it is called a regression model, it deals with classification problems. This is mainly because its essence is a linear model plus a mapping function sigmoid, which maps the continuous results obtained by the linear model to discrete models.
Decision tree learning is a method of approaching the objective function of discrete value in which the learned function is represented as a decision tree. A decision tree classifies an instance by arranging instances from a root node to a leaf node. The leaf node is the class to which the instance belongs. Each node on the tree specifies a test for an attribute of the instance, and each subsequent branch of the node corresponds to a possible value for the attribute. The classification to an instance starts from the root of the tree, testing the properties of the node, and then move down the branches corresponding to the property values of the given instance. This process is then repeated on the subtree of the new root node.
Random forest, as the name implies, establishes a forest randomly. There are many decision trees in the forest, and there is no correlation between each decision tree in the random forest. After the forest is gotten, when a new input sample enters, each decision tree in the forest makes a separate judgment to classify the sample (for the classification algorithm), and the sample is predicted to be the classification that has been chosen for the most times.
Gbdt is an iterative decision tree algorithm consisting of multiple decision trees. The conclusions of all trees are added together to make the final answer.
Lightgbm (gbm) is another implementation method of Gbdt, which adopts two new strategies based on Gbdt. Gradient-based One-Side Sampling (GOSS) is that, although Gbdt has no data weight, each data instance has different gradients. According to the definition of computing information gain, the instance with larger gradient has greater influence on the information gain. Thus, samples with large gradients should be kept (pre-set thresholds or highest percentiles) and samples with small gradients should be randomly removed during downsampling whenever possible. Exclusive Feature Bundling (EFB) means that many features are almost mutually exclusive especially in sparse feature spaces, and we can bundle mutually exclusive features. Finally, we reduce the bundling problem to the graph coloring problem and obtain an approximate solution through the greedy algorithm.
Statistical analysis
Python programming language (Python Software Foundation, version 3.6) were used for our analysis. The following packages for machine learning were used: Scikit-learn (https://github.com/scikit-learn/scikit-learn) and lightgbm (https://github.com/Microsoft/LightGBM). The following machine learning techniques were used: Logistic Regression, Decision Tree, Gradient Boosting, and Lightgbm. 31 explanatory variables were applied for machine learning analysis. Our samples were randomly divided into training and test groups at a ratio of 8:2. Machine learning techniques were evaluated and the prediction accuracy was compared with the following methods, we calculated the area under the receiver operator characteristic curves (AUC) and compared the F1-Measure, accuracy, recall rate, and MSE. The missed values of the variables were estimated with multiple imputations. Values were normalized and scaled to 0-1.
F1-Measure evaluation indicator is often used in information retrieval and natural language processing. It is a comprehensive evaluation index based on Precision and Recall (F1 = 2rp / ( r+p )), which is defined as follows: Where r is recall and p is precision. Accuracy rate indicates the proportion of the correctly classified samples in the total samples. Precision rate indicates the probability of the correctly predicted positive samples among all the samples predicted as positive samples. Recall rate indicates the probability of correctly predicted positive samples among all the original positive samples. The full name of the ROC is the " receiver operating characteristic curve ". AUC is defined as the area under the ROC curve.