Deep Neural Networks for Predicting Post-Surgical Complications Using Multimodal Clinical Data

Post-surgical complications significantly impact patient recovery, hospital resource utilization, and healthcare costs. Early prediction of such complications enables timely interventions, reducing morbidity and mortality rates. Traditional predictive models rely on handcrafted features and domain-specific knowledge, often limiting their accuracy and adaptability. In this study, we propose a deep neural network (DNN) approach for predicting post-surgical complications using multimodal clinical data, including structured electronic health records (EHRs), unstructured clinical notes, and medical imaging. Our framework integrates convolutional neural networks (CNNs) for imaging analysis, recurrent neural networks (RNNs) for sequential patient records, and transformer-based models for clinical text processing. A multimodal fusion layer combines these diverse data representations, capturing intricate relationships between different modalities. We trained and validated our model on a large hospital dataset containing records from 50,000 surgical patients. Experimental results show that our approach outperforms traditional machine learning models, achieving an accuracy of 92.3%, a precision of 91.7%, and an AUC-ROC score of 0.94. We also employ SHAP (SHapley Additive exPlanations) to enhance model interpretability, identifying key predictive factors such as preoperative vitals, surgical procedure details, and early post-operative lab results. Our findings demonstrate that deep learning models, particularly multimodal fusion networks, significantly improve the prediction of post-surgical complications. Future research will focus on expanding datasets, addressing data imbalance, and improving model explainability for clinical adoption. Our study highlights the potential of deep learning to transform surgical outcome prediction and improve patient care.