Personalized prediction of diabetic foot ulcer recurrence in elderly individuals using machine learning paradigms.

BACKGROUND: This study utilizes machine learning to analyze the recurrence risk of diabetic foot ulcers (DFUs) in elderly diabetic patients, aiming to enhance prevention and intervention efforts. OBJECTIVE: The goal is to construct accurate predictive models for assessing the recurrence risk of DFUs based on high-risk factors, such as age, blood sugar control, alcohol consumption, and smoking, in elderly diabetic patients. METHODS: Data from 138 elderly diabetic patients were collected, and after data cleaning, outlier screening, and feature integration, machine learning models were constructed. Support Vector Machine (SVM) was employed, achieving an accuracy rate of 93%. RESULTS: Experimental results demonstrate the effectiveness of SVM in predicting the recurrence risk of DFUs in elderly diabetic patients, providing clinicians with a more accurate tool for assessment. CONCLUSIONS: The study highlights the significance of machine learning in managing foot ulcers in elderly diabetic patients, particularly in predicting recurrence risk. This approach facilitates timely intervention, reducing the likelihood of patient recurrence, and introduces computer-assisted medical strategies in elderly diabetes management.

A deep learning approach for acute liver failure prediction with combined fully connected and convolutional neural networks.

BACKGROUND: Acute Liver Failure (ALF) is a critical medical condition with rapid development, often caused by viral infections, hepatotoxic drug abuse, or other severe liver diseases. Timely and accurate prediction of ALF occurrence is clinically crucial. However, predicting ALF poses challenges due to the diverse physiological differences among patients and the dynamic nature of the disease. OBJECTIVE: This study introduces a deep learning approach that combines fully connected and convolutional neural networks for effective ALF prediction. The goal is to overcome limitations of traditional machine learning methods and enhance predictive model performance and generalization. METHODS: The proposed model integrates a fully connected neural network for handling basic patient features and a convolutional neural network dedicated to capturing temporal patterns in patient data. The combination allows automatic learning of complex patterns and abstract features present in highly dynamic medical data associated with ALF. RESULTS: The model's effectiveness is demonstrated through comprehensive experiments and performance evaluations. It outperforms traditional machine learning methods, achieving 94.8% accuracy and superior generalization capabilities. CONCLUSIONS: The study highlights the potential of deep learning in ALF prediction, emphasizing the importance of considering individualized medical factors. Future research should focus on improving model robustness, addressing imbalanced data, and further exploring personalized features for enhanced predictive accuracy in real-world clinical scenarios.