Overview / Usage

Background: Intraoperative dexmedetomidine can be useful for its sedative and analgesic sparing effects, and for the prevention of emergence delirium. Conversely, it can cause hypotension and bradycardia. The aim of this study was to assess the safety and efficacy of dexmedetomidine in pediatric anesthesia. Methods: This is a retrospective cohort study in children who received intravenous dexmedetomidine (Dex group) or opioids (No-Dex group) during general anesthesia for tonsillectomy, between November 2014 and November 2016. From the medical records, data on the intraoperative phase (hemodynamic adverse events, respiratory failure at the emergence, awakening and extubation times, emergence delirium), recovery room (pain, hemodynamic parameters, and desaturation), and ward stay (pain, and nausea and vomiting) were investigated. Time of hospital discharge was calculated. Results: Three hundred twenty-eight (Dex group 183; No-Dex group 145) children ranging from 1.5 to 10 years were included. The percentage of intraoperative hypotension was significantly higher in the Dex group (p=0.01). The extubation times were significantly higher in No-Dex group (p=0.0001), although the awakening times were significantly longer with dexmedetomidine (p= 0.0001). Desaturation episodes were higher in the Dex group (p=0.0001). The incidence of emergence delirium was similar in the two groups, but of greater intensity in the No-Dex group. While in the immediate postoperative period there was no difference in pain, after 24 hours, the incidence of pain and vomiting was significantly higher (p=0.003; p=0.0001) in the No-Dex group. Conclusions: Although several outcome parameters showed important advantages of dexmedetomidine over opioid-based regimens in terms of safety and efficacy, issues such as the increased intraoperative hypotension, indicated that it is not possible to draw any definitive conclusions.

Methodology / Approach

Oncology-Project

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Exploratory Data Analysis (EDA):

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• Examine the distributions of the key variables like age, gender, ASA score, surgery duration, etc. using histograms, boxplots. Look for outliers.

• Compute basic summary statistics on the key variables.

• Visualize relationships between input variables and the target delirium using scatterplots, correlation matrices.

• Check for missing data and handle appropriately.

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Predictive Modeling:

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• Split data into train/validation/test sets.

• Try different ML algorithms like logistic regression, random forest, gradient boosting machines. Evaluate using AUC-ROC, precision, recall on validation set.

• Tune hyperparameters using grid search and cross-validation.

• Select best model based on validation performance. Evaluate on test set.

• Interpret important features using feature importance scores, partial dependence plots.

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Unsupervised Learning:

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• Try clustering algorithms like K-Means, hierarchical clustering.

• Optimize number of clusters using Silhouette score or elbow method.

• Analyze and describe the clusters based on input features.

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For implementation, I would likely use Python with libraries like Pandas, Matplotlib, Scikit-Learn, XGBoost.

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Supervised learning:

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The target variable is delirium (binary or multi-class based on PAED scale).

We can build a classification model to predict delirium from the input variables.

Evaluation metrics would be things like AUC-ROC, precision, recall.

Unsupervised learning:

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There is no explicit target variable.

We can use clustering algorithms to find groups in the data.

Evaluation metrics would be things like silhouette score to determine optimal number of clusters.

We can then analyze the clusters to understand what input variables characterize each cluster.

So in summary:

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Supervised approach would predict delirium from inputs.

Unsupervised approach would find clusters in the data and then analyze what defines those clusters.

​

Supervised learning:

​

The target variable is delirium (binary or multi-class based on PAED scale).

We can build a classification model to predict delirium from the input variables.

Evaluation metrics would be things like AUC-ROC, precision, recall.

Unsupervised learning:

​

There is no explicit target variable.

We can use clustering algorithms to find groups in the data.

Evaluation metrics would be things like silhouette score to determine optimal number of clusters.

We can then analyze the clusters to understand what input variables characterize each cluster.

So in summary:

​

Supervised approach would predict delirium from inputs.

Unsupervised approach would find clusters in the data and then analyze what defines those clusters.

Technologies Used

Python

Machine Learning

Scikit_learn

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Repository

https://github.com/JEEVITHA2512/Oncology-Project