The widespread adoption of Electronic Health Record (EHR) systems in healthcare institutes has generated vast amounts of medical data, offering significant opportunities for improving healthcare services through deep learning techniques. However, the complex and diverse modalities and feature structures in real-world EHR data pose great challenges for deep learning model design. To address the multi-modality challenge in EHR data, current approaches primarily rely on hand-crafted model architectures based on intuition and empirical experiences, leading to sub-optimal model architectures and limited performance. Therefore, to automate the process of model design for mining EHR data, we propose a novel neural architecture search (NAS) framework named AutoFM, which can automatically search for the optimal model architectures for encoding diverse input modalities and fusion strategies. We conduct thorough experiments on real-world multi-modal EHR data and prediction tasks, and the results demonstrate that our framework not only achieves significant performance improvement over existing state-of-the-art methods but also discovers meaningful network architectures effectively.

Health risk prediction aims to forecast the potential health risks that patients may face using their historical Electronic Health Records (EHR). Although several effective models have developed, data insufficiency is a key issue undermining their effectiveness. Various data generation and augmentation methods have been introduced to mitigate this issue by expanding the size of the training data set through learning underlying data distributions. However, the performance of these methods is often limited due to their task-unrelated design. To address these shortcomings, this paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. Furthermore, MedDiffusion discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data. Experimental evaluation on four real-world medical datasets demonstrates that MedDiffusion outperforms 14 cutting-edge baselines in terms of PR-AUC, F1, and Cohen's Kappa. We also conduct ablation studies and benchmark our model against GAN-based alternatives to further validate the rationality and adaptability of our model design. Additionally, we analyze generated data to offer fresh insights into the model's interpretability. The source code is available via https://shorturl.at/aerT0.

Health risk prediction is a challenge task that aims to predict whether patients would suffer from a certain disease/condition in the near future based on their historical EHR data. Although existing approaches can achieve better performance, none of them can deal with the noise existing in the EHR data explicitly. In this paper, we hypothesize that automatically removing noise from EHR data should help the models further improve the performance. Correspondingly, we propose a novel model named MedSkim, which is able to automatically rule out irrelevant visits and codes by effectively skimming through the EHR data. In particular, the proposed model has a code selection module that can directly make a skipping decision to each individual diagnosis codes and then remove the target-irrelevant ones. A backward probing RNN (BPRNN) is designed to reversely process the EHR data and provide a coarse grained representation learning for visits. Besides, a forward skipping RNN (FSRNN) is proposed to read the EHR in a preceding way and dynamically select important visits and codes based on the results of previous two modules. Finally, the risk prediction module uses the output hidden states from FSRNN for generating the final representation to make predictions. Additionally, we also design an extra regularization term based on the skip rate of the model and combine it with standard cross entropy loss to train the model in an end-to-end setting. Experimental results show that MedSkim achieves the best performance on three real-world datasets compared with the state-of-the-art baselines in terms of PR-AUC, F1 and Cohen’s Kappa. Moreover, the ablation study and case study confirm that the proposed MedSkim is reasonable and effective for removing noise from EHR data1.1The source code of the proposed MedSkim is available at https://github.com/SH-Src/MedSkim

The broad adoption of electronic health record (EHR) systems and the advances of deep learning technology have motivated the development of health risk prediction models, which mainly depend on the expressiveness and temporal modeling capacity of deep neural networks (DNNs) to improve prediction performance. Some further augment the prediction by using external knowledge, however, a great deal of EHR information inevitably loses during the knowledge mapping. In addition, prediction made by existing models usually lacks reliable interpretation, which undermines their reliability in guiding clinical decision-making. To solve these challenges, we propose MedRetriever, an effective and flexible framework that leverages unstructured medical text collected from authoritative websites to augment health risk prediction as well as to provide understandable interpretation. Besides, MedRetriever explicitly takes the target disease documents into consideration, which provide key guidance for the model to learn in a target-driven direction, i.e., from the target disease to the input EHR. To specify, MedRetriever can flexibly choose its backbone from major predictive models to learn the EHR embedding for each visit. After that, the EHR embedding and features of target disease documents are aggregated into a query by self-attention to retrieve highly relevant text segments from the medical text pool, which is stored in the dynamically updated text memory. Finally, the comprehensive EHR embedding and the text memory are used for prediction and interpretation. We evaluate MedRetriever against nine state-of-the-art approaches across three real-world EHR datasets, which consistently achieves the best performance in AUC and recall metrics and outperforms the best baseline by at least 4.8% in recall on three test datasets. Furthermore, we conduct case studies to show the easy-to-understand interpretation by MedRetriever.

The broad adoption of electronic health records (EHR) data and the availability of biomedical knowledge graphs (KGs) on the web have provided clinicians and researchers unprecedented resources and opportunities for conducting health risk predictions to improve healthcare quality and medical resource allocation. Existing methods have focused on improving the EHR feature representations using attention mechanisms, time-aware models, or external knowledge. However, they ignore the importance of using personalized information to make predictions. Besides, the reliability of their prediction interpretations needs to be improved since their interpretable attention scores are not explicitly reasoned from disease progression paths. In this paper, we propose MedPath to solve these challenges and augment existing risk prediction models with the ability to use personalized information and provide reliable interpretations inferring from disease progression paths. Firstly, MedPath extracts personalized knowledge graphs (PKGs) containing all possible disease progression paths from observed symptoms to target diseases from a large-scale online medical knowledge graph. Next, to augment existing EHR encoders for achieving better predictions, MedPath learns a PKG embedding by conducting multi-hop message passing from symptom nodes to target disease nodes through a graph neural network encoder. Since MedPath reasons disease progression by paths existing in PKGs, it can provide explicit explanations for the prediction by pointing out how observed symptoms can finally lead to target diseases. Experimental results on three real-world medical datasets show that MedPath is effective in improving the performance of eight state-of-the-art methods with higher F1 scores and AUCs. Our case study also demonstrates that MedPath can greatly improve the explicitness of the risk prediction interpretation.1