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Analyzing Surgical Treatment of Intestinal Obstruction in Children with Artificial Intelligence
Wang-Ren Qiu1, Gang Chen1, Jin Wu2
1Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333046, China.
Insights
This study introduces a machine learning model using routine blood and urine tests to aid in diagnosing childhood intestinal obstruction. The model achieved 80.04% accuracy, assisting clinical decisions for pediatric ileus.
Area of Science:
- Pediatric Surgery
- Medical Informatics
- Machine Learning in Healthcare
Background:
- Intestinal obstruction is a critical surgical emergency in children, posing diagnostic challenges due to adult-suited methods' inapplicability.
- Machine learning shows promise in medical text mining for pediatric ileus diagnosis, an area less explored than medical imaging.
Purpose of the Study:
- To propose a two-layer machine learning model utilizing routine clinical text data for diagnosing childhood intestinal obstruction.
- To assist in clinical decision-making for pediatric ileus by analyzing physiological indexes.
Main Methods:
- A deep learning neural network was developed using 63 physiological indexes from 526 children with intestinal obstruction.
- The model employed a two-layer approach, with the first layer undergoing 5-fold cross-validation.
- Data included routine blood count and urine tests, categorized by surgical intervention and intestinal necrosis.
Main Results:
- The first layer of the model achieved a mean accuracy (Acc) of 80.04%, with sensitivity (Se) of 67.48%, specificity (Sp) of 87.46%, and MCC of 0.57.
- The second layer of the model demonstrated an accuracy of 70.4%.
Conclusions:
- The proposed machine learning algorithm effectively processes clinical text data for diagnosing childhood ileus.
- This approach offers valuable guidance for clinical decision-making in pediatric intestinal obstruction.
Abstract:
Intestinal obstruction is a common surgical emergency in children. However, it is challenging to seek appropriate treatment for childhood ileus since many diagnostic measures suitable for adults are not applicable to children. The rapid development of machine learning has spurred much interest in its application to medical imaging problems but little in medical text mining. In this paper, a two-layer model based on text data such as routine blood count and urine tests is proposed to provide guidance on the diagnosis and assist in clinical decision-making. The samples of this study were 526 children with intestinal obstruction. Firstly, the samples were divided into two groups according to whether they had intestinal obstruction surgery, and then, the surgery group was divided into two groups according to whether the intestinal tube was necrotic. Specifically, we combined 63 physiological indexes of each child with their corresponding label and fed them into a deep learning neural network which contains multiple fully connected layers. Subsequently, the corresponding value was obtained by activation function. The 5-fold cross-validation was performed in the first layer and demonstrated a mean accuracy (Acc) of 80.04%, and the corresponding sensitivity (Se), specificity (Sp), and MCC were 67.48%, 87.46%, and 0.57, respectively. Additionally, the second layer can also reach an accuracy of 70.4%. This study shows that the proposed algorithm has direct meaning to processing of clinical text data of childhood ileus.
