DeepAI AI Chat
Log In Sign Up

Deep EHR: Chronic Disease Prediction Using Medical Notes

by   Jingshu Liu, et al.
NYU Langone Medical Center LLC
NYU college

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.


page 12

page 21

page 24


Characterizing the Value of Information in Medical Notes

Machine learning models depend on the quality of input data. As electron...

Predicting Severe Sepsis Using Text from the Electronic Health Record

Employing a machine learning approach we predict, up to 24 hours prior, ...

Hybrid Text Feature Modeling for Disease Group Prediction using Unstructured Physician Notes

Existing Clinical Decision Support Systems (CDSSs) largely depend on the...

Classifying medical notes into standard disease codes using Machine Learning

We investigate the automatic classification of patient discharge notes i...

DeepTag: inferring all-cause diagnoses from clinical notes in under-resourced medical domain

In many under-resourced settings, clinicians lack time and expertise to ...

Prediction Using Note Text: Synthetic Feature Creation with word2vec

word2vec affords a simple yet powerful approach of extracting quantitati...