paulheider / awesome-ehr-deeplearning

Curated list of awesome papers for electronic health records(EHR) mining, machine learning, and deep learning.

Awesome Deep Learning and EHRs

Curated list of awesome papers for electronic health records (EHR) mining, machine learning, and deep learning.

Background

Over the past decade, the volume of EHR has exploded. This data has great potential. Thanks to advances in machine learning and deep learning techniques, health records have been converted into mathematical representation. We make a collection of must-read papers on various EHR topics - recent research trends, applications to predict patient outcomes, visualization of complex data.

Contents

• Survey
• Data mining
• Machine learning
• Deep learning
• Visualization

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Survey

• [pdf] - Deep EHR: A survey of Recent Advances on Deep Learning Techniques for Electronic Health Record(EHR) Analysis, B. Shickel et al. 2018.

• [pdf] - Opportunities in Machine Learning for Healthcare, M. Ghassemi et al. 2018.

• [pdf] - Big Data and Machine Learning in Health Care, A. L. Beam et al. 2018.

• [pdf] - Big data from electronic health records for early and late translational cardiovascular research: challenges and potential, H. Hemingway et al. 2017.

• [pdf] - Mining Electronic Health Records: A Survey, P. Yadav et al. 2017.

Data mining

• [ref] - Disease Heritability Inferred from Familial Relationships Reported in Medical Records, F. Polubriaginof et al. 2017.

• [pdf] - Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets, J. Chen et al. 2016.

• [pdf] - Modeling temporal relationships in large scale clinical associations, D. Hanauer et al. 2012.

Machine learning & Deep learning

• [pdf] - Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record, J. Zhang et al. 2018.

• [pdf] - Deep Diabetologist: Learning to Prescribe Hyperglycemia Medications with Hierarchical Recurrent Neural Networks, J. Mei et al. 2018.

• [pdf] - MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare, E. Choi et al. 2018.

• [pdf] - Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) Utilizing Free-Text Clinical Narratives, I. Banerjee et al. 2018.

• [pdf] - Biomedical Question Answering via Weighted Neural Network Passage Retrieval, F. Galkó et al. 2018.

• [pdf] - Expert System for Diagnosis of Chest Diseases Using Neural Networks, I. Kayali et al. 2018.

• [pdf] - Scalable and accurate deep learning with electronic health records, A. Rajkomar et al. 2018.

• [pdf] - Deep Representation for Patient Visits from Electronic Health Records, J. Escudie et al. 2018.

• [pdf] - HeteroMed: Heterogeneous Information Network for Medical Diagnosis, A. Hosseini et al. 2018.

• [pdf] - Natural Language Generation for Electronic Health Records, S. Lee. 2018.

• [pdf] - Generating Multi-label Discrete Patient Records using Generative Adversarial Networks, E. Choi, et al. 2018.

• [pdf] - A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data, S. B. Golas et al. 2018.

• [pdf] - Countdown Regression: Sharp and Calibrated Survival Predictions, A. Avati et al. 2018.

• [pdf] - Natural Language Processing for EHR-Based Computational Phenotyping, Z. Zeng et al. 2018.

• [pdf] - Using Clinical Narratives and Structured Data to Identify Distant Recurrences in Breast Cancer, Z. Zeng et al. 2018.

• [pdf] - Mixed Effect Composite RNN-GP: A Personalized and Reliable Prediction Model for Healthcare, I. Chung et al. 2018.

• [pdf] - Uncertainty-Aware Attention for Reliable Interpretation and Prediction, J. Heo et al. 2018.

• [pdf] - Learning Patient Representations from Text, D. Dligach, et al. 2018.

• [pdf] - Countdown Regression: Sharp and Calibrated Survival Predictions, A. Avati, et al. 2018.

• [pdf] - Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation, L. Wang, et al. 2018.

• [pdf] - A Deep Learning Interpretable Classifier for Diabetic Retinopathy Disease Grading, J. Torre et al. 2017.

• [pdf] - Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology, A. Holzinger et al. 2017.

• [pdf] - Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records, R. Miotto et al. 2016.

• [pdf] - Improvement in Cardiovascular Risk Prediction with Electronic Health Records, M. M. Pike et al. 2016.

Visualization

• [pdf] - ClinicalVis: Supporting Clinical Task-Focused Design Evaluation, M. Ghassemi et al. 2018.

• [pdf] - CarePre: An Intelligent Clinical Decision Assistance System, Z. Jin et al. 2018.

• [pdf] - Visualizing Patient Timelines in the Intensive Care Unit, D. L. Lambert et al. 2018.

• [pdf] - PhenoStacks: Cross-Sectional Cohort Phenotype Comparison Visualizations, M. Glueck et al. 2016.

• [pdf] - Using Visual Analytics to Interpret Predictive Machine Learning Models, J. Krause et al. 2016.

• [pdf] - Iterative cohort analysis and exploration, Z. Zhang et al. 2014.

• [pdf] - An Evaluation of Visual Analytics Approaches to Comparing Cohorts of Event Sequences, S. Malik et al. 2014.

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Acknowledgement

Thank you for all your contributions. Please make sure to read the contributing guide before you make a pull request.