[Paper Review] Deep EHR: Chronic Disease Prediction Using Medical Notes

Jingshu Liu, Zachariah Zhang+1 more|arXiv (Cornell University)|Aug 15, 2018

Machine Learning in Healthcare9 references48 citations

TL;DR

The paper introduces a multi-task deep learning framework that combines unstructured medical notes with structured data to predict onset of chronic diseases, showing notes improve performance and negation handling boosts accuracy.

ABSTRACT

Early detection of preventable diseases is important for better disease management, improved inter-ventions, and more efficient health-care resource allocation. Various machine learning approacheshave been developed to utilize information in Electronic Health Record (EHR) for this task. Majorityof previous attempts, however, focus on structured fields and lose the vast amount of information inthe unstructured notes. In this work we propose a general multi-task framework for disease onsetprediction that combines both free-text medical notes and structured information. We compareperformance of different deep learning architectures including CNN, LSTM and hierarchical models.In contrast to traditional text-based prediction models, our approach does not require disease specificfeature engineering, and can handle negations and numerical values that exist in the text. Ourresults on a cohort of about 1 million patients show that models using text outperform modelsusing just structured data, and that models capable of using numerical values and negations in thetext, in addition to the raw text, further improve performance. Additionally, we compare differentvisualization methods for medical professionals to interpret model predictions.

Motivation & Objective

• Motivate early detection of preventable diseases using the rich information in EHRs.
• Propose a general multi-task architecture that fuses free-text notes with structured numerical data.
• Evaluate various deep learning architectures (CNN, LSTM, hierarchical models) on large real-world EHR data.
• Address negations and provide visualization methods to aid clinical interpretability.

Proposed method

• Represent notes with word embeddings (PubMed and in-house StarSpace embeddings) and feed through CNN, LSTM, or hierarchical models.
• Incorporate numerical lab/vital sign values extracted from notes and demographics as additional inputs.
• Use a multi-task framework with disease-specific outputs and masked loss to handle patient-disease eligibility overlaps.
• Apply a negation tagging step (NegEx) to improve interpretation of negated findings.
• Compare baseline models (logistic regression with labs/demographics, TF-IDF text) to deep learning models.
• Provide visualization methods (log-odds based) to identify high-impact n-grams and phrases.

Experimental results

Research questions

• RQ1 Can unstructured medical notes improve prediction of disease onset beyond structured data alone?
• RQ2 Which deep learning architectures best leverage notes for multi-disease onset prediction?
• RQ3 How do negations and numerical values in notes affect predictive performance?
• RQ4 What visualization methods best help clinicians interpret model predictions?

Key findings

• Models using medical notes outperform those using only lab and demographic data.
• Deep learning models with notes achieve higher AUC than logistic regression baselines with TF-IDF features.
• Incorporating negation tagging and combining labs/demographics yields the best performance across CHF, KF, and stroke.
• Best model (BiLSTM with negation tagging, dense layer, plus lab/demographic inputs) achieved the highest AUC across all three diseases.
• Log-odds based visualizations provide more intuitive explanations than gradient-based methods.