Predicting line of therapy transition via similar patient augmentation.

Analyzing large EHR databases to predict cancer progression and treatments has become a hot trend in recent years. An increasing number of modern deep learning models have been proposed to find the milestones of essential patient medical journey characteristics to predict their disease status and give healthcare professionals valuable insights. However, most of the existing methods are lack of consideration for the inter-relationship among different patients. We believe that more valuable information can be extracted, especially when patients with similar disease statuses visit the same doctors. Towards this end, a similar patient augmentation-based approach named SimPA is proposed to enhance the learning of patient representations and further predict lines of therapy transition. Our experiment results on a real-world multiple myeloma dataset show that our proposed approach outperforms state-of-the-art baseline approaches in terms of standard evaluation metrics for classification tasks.

Hierarchical Pretraining on Multimodal Electronic Health Records.

Pretraining has proven to be a powerful technique in natural language processing (NLP), exhibiting remarkable success in various NLP downstream tasks. However, in the medical domain, existing pretrained models on electronic health records (EHR) fail to capture the hierarchical nature of EHR data, limiting their generalization capability across diverse downstream tasks using a single pretrained model. To tackle this challenge, this paper introduces a novel, general, and unified pretraining framework called MedHMP, specifically designed for hierarchically multimodal EHR data. The effectiveness of the proposed MedHMP is demonstrated through experimental results on eight downstream tasks spanning three levels. Comparisons against eighteen baselines further highlight the efficacy of our approach.

AutoMed: Automated Medical Risk Predictive Modeling on Electronic Health Records.

Electronic health records (EHR) have been widely applied to various tasks in the medical domain such as risk predictive modeling, which aims to predict further health conditions by analyzing patients' historical EHR. Existing work mainly focuses on modeling the sequential and temporal characteristics of EHR data with advanced deep learning techniques. However, the network architectures of these models are all manually designed based on experts' prior knowledge, which largely impedes non-experts from exploring this task. To address this issue, in this paper, we propose a novel automated risk prediction model named AutoMed to automatically search the optimal model architecture for modeling the complex EHR data and improving the performance of the risk prediction task. In particular, we follow the idea of neural architecture search to design a search space that contains three separate searchable modules. Two of them are used for analyzing sequential and temporal features of EHR data, respectively. The third is to automatically fuse both features together. Besides these three modules, AutoMed contains an embedding module and a prediction module. All the three searchable modules are jointly optimized in the search stage to derive the optimal model architecture. In such a way, the model design can be automatically achieved with few human interventions. Experimental results on three real-world datasets show that AutoMed outperforms state-of-the-art baselines in terms of PR-AUC, F1, and Cohen's Kappa. Moreover, the ablation study shows that AutoMed can obtain reasonable model architectures and offer useful insights to the future risk prediction model design.