EvolveFNN: An Interpretable Framework for Early Detection Using Longitudinal Electronic Health Record Data

The extensive adoption of artificial intelligence in clinical decision support systems requires greater model interpretability. Hence, we introduce EvolveFNN, an interpretable model based on the recurrent neural network that merges fuzzy logic principles with recurrent units. This model is designed to train precise and understandable models using high-dimensional longitudinal electronic health records data. Through supervised learning, our method allows the identification of variable encoding functions and significant rules. To demonstrate performance and capabilities in classification and rule discovery, we first test our method on a simulated dataset. The proposed methods achieve the best model performance compared to other methods, and the rules learned are almost identical to what we used to generate the synthetic data. Furthermore, we showcase a pilot application that proves its potential in the early detection of cardiac event onset. Our proposed algorithm obtains a comparable model performance to vanilla GRU models and remains relatively stable when the prediction window size changes. Examining the rules generated by our proposed model, we find that the extracted rules not only align with clinical practices and existing literature but also provide potential risk factors not explored in the population. The additional experiments on the MIMIC-III benchmark dataset show the algorithm's generalizability. In conclusion, our proposed approach can effectively train accurate, interpretable, and reliable models using large longitudinal electronic health records, offering clinicians valuable insights. Source code is available at https://github.com/kayvanlabs/EvolveFNN.