Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks (1706.05764v1)

Abstract: Predicting the future health information of patients from the historical Electronic Health Records (EHR) is a core research task in the development of personalized healthcare. Patient EHR data consist of sequences of visits over time, where each visit contains multiple medical codes, including diagnosis, medication, and procedure codes. The most important challenges for this task are to model the temporality and high dimensionality of sequential EHR data and to interpret the prediction results. Existing work solves this problem by employing recurrent neural networks (RNNs) to model EHR data and utilizing simple attention mechanism to interpret the results. However, RNN-based approaches suffer from the problem that the performance of RNNs drops when the length of sequences is large, and the relationships between subsequent visits are ignored by current RNN-based approaches. To address these issues, we propose {\sf Dipole}, an end-to-end, simple and robust model for predicting patients' future health information. Dipole employs bidirectional recurrent neural networks to remember all the information of both the past visits and the future visits, and it introduces three attention mechanisms to measure the relationships of different visits for the prediction. With the attention mechanisms, Dipole can interpret the prediction results effectively. Dipole also allows us to interpret the learned medical code representations which are confirmed positively by medical experts. Experimental results on two real world EHR datasets show that the proposed Dipole can significantly improve the prediction accuracy compared with the state-of-the-art diagnosis prediction approaches and provide clinically meaningful interpretation.

Diagnosis Prediction in Healthcare via Dipole: An Overview

The paper "Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks" presents a methodology for predicting patient diagnoses using Electronic Health Records (EHR) data. The authors introduce Dipole, an end-to-end model designed to leverage bidirectional recurrent neural networks (BRNNs) combined with attention mechanisms, aiming to improve predictive accuracy and interpretability in healthcare informatics.

Methodology

Dipole addresses the challenges of temporality and high dimensionality in EHR data. Typically, EHR data consists of sequential patient visits, each encoded with various medical codes. Traditional RNN approaches struggle with long sequences due to their inherent forgetfulness, and often overlook the temporal relationships between visits.

Dipole employs BRNNs to model both past and future visits, enhancing its ability to memorize and utilize comprehensive visit information. This dual-directional approach captures dependencies more effectively than unidirectional models. Additionally, Dipole incorporates three attention mechanisms—location-based, general, and concatenation-based—to assign importance to different visits, enriching its interpretability by elucidating the contribution of past visits to predictions.

Experimental Results

The model was evaluated on two datasets: Diabetes claims and Medicaid claims. It demonstrated notable advancements in prediction accuracy compared to prior models like RETAIN and Med2Vec. Particularly, Dipole achieved enhanced accuracy by considering bidirectional visit information and leveraging diverse attention techniques. These improvements were consistent across both datasets, underscoring the robustness of the approach.

Interpretability and Clinical Relevance

A key feature of Dipole is its capability to provide clinically meaningful interpretations. By analyzing attention weights, the model identifies and highlights significant visits that influence predictions. The paper also details the interpretability of learned medical code representations, validated by clinical experts. This facet is critical in healthcare, where understanding model reasoning can inform risk assessments and therapeutic strategies.

Implications and Future Directions

The integration of BRNNs with sophisticated attention mechanisms in Dipole presents significant implications for personalized healthcare. Its ability to predict future diagnoses with greater accuracy can enhance decision-making processes and patient outcomes. Furthermore, the interpretability of the model fosters trust and utility in clinical settings.

Future developments could explore extensions of Dipole to incorporate patient demographics, comorbid conditions, and other structured data sources to improve sensitivity and specificity. Additionally, adapting this framework to real-time prediction systems could be a promising direction, aiding in proactive healthcare interventions.

Overall, Dipole represents a substantial contribution to the application of deep learning in healthcare, offering both enhanced predictive performance and interpretability, essential for successful clinical implementation.