bharat123rox / 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.

https://hurcy.github.io/awesome-ehr-deeplearning/

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
• Statistics
• Machine learning
• Deep learning
• Visualization
• Standardization

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Survey

• [ref] - A guide to deep learning in healthcare, A. Esteva et al. 2019.

• [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

• [pdf] - Development and validation of computable Phenotype to Identify and Characterize Kidney Health in Adult Hospitalized Patients, T. Ozrazgat-Baslanti et al. 2019.

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

• [pdf] - Exploiting a Novel Algorithm and GPUs to Break the One Hundred Million Barrier for Time Series Motifs and Joins, Y. Zhu et al. 2016.

• [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.

Statistics

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

Machine learning

• [pdf] - High-throughput Phenotyping with Temporal Sequences, H. Estiri et al. 2019.

• [pdf] - The Medical Deconfounder: Assessing Treatment Effect with Electronic Health Records (EHRs), L. Zhang et al. 2019.

• [pdf] - Interpretation of machine learning predictions for patient outcomes in electronic health records, W. L. Cava et al. 2019.

• [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.

Deep learning

Embedding & Representation

• [pdf] - Identification of Predictive Sub-Phenotypes of Acute Kidney Injury using Structured and Unstructured Electronic Health Record Data with Memory Networks, Z. Xu et al. 2019.

• [pdf] - Learning Hierarchical Representations of Electronic Health Records for Clinical Outcome Prediction, L. Liu et al. 2019.

• [pdf] - Measuring Patient Similarities via a Deep Architecture with Medical Concept Embedding, L. Gligic et al. 2019.

• [pdf] - Application of Clinical Concept Embeddings for Heart Failure Prediction in UK EHR data, M. Agrawal et al. 2019.

• [pdf] - Embedding Electronic Health Records for Clinical Information Retrieval, X. Wei et al. 2019.

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

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

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

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

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

NLP

• [pdf] - A frame semantic overview of NLP-based information extraction for cancer-related EHR notes, S. Datta et al. 2019.

• [pdf] - Named Entity Recognition for Electronic Health Records: A Comparison of Rule-based and Machine Learning Approaches, W. L. Cava et al. 2019.

• [pdf] - Named Entity Recognition in Electronic Health Records Using Transfer Learning Bootstrapped Neural Networks, Z. Zhu et al. 2019.

• [pdf] - Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health Records, X. Liu et al. 2019.

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

Time series

• [pdf] - Modeling Irregularly Sampled Clinical Time Series, S. N. Shukla et al. 2019.

• [pdf] - TIFTI: A Framework for Extracting Drug Intervals from Longitudinal Clinic Notes, M. Agrawal et al. 2019.

Privacy

• [pdf] - A Fully Private Pipeline for Deep Learning on Electronic Health Records, E. Chou et al. 2019.

Prediction

• [pdf] - MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records, X. X. Zhang et al. 2019.

• [pdf] - Predicting Diabetes Disease Evolution Using Financial Records and Recurrent Neural Networks, R. T. Sousa et al. 2019.

• [pdf] - Deep Diabetologist: Learning to Prescribe Hyperglycemia Medications with Hierarchical Recurrent Neural Networks, J. Mei 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] - HeteroMed: Heterogeneous Information Network for Medical Diagnosis, A. Hosseini et al. 2018.

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

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

Visualization

• [pdf] - DPVis: Visual Exploration of Disease Progression Pathways, B. C. Kwon et al. 2019.

• [pdf] - MAQUI: Interweaving Queries and Pattern Mining for Recursive Event Sequence Exploration, P. Law et al. 2019.

• [pdf] - EventAction: A Visual Analytics Approach to Explainable Recommendation for Event Sequences, F. Du et al. 2018

• [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] - CoreFlow: Extracting and Visualizing Branching Patterns from Event Sequences, Z. Liu et al. 2017.

• [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.

Standardization

• [pdf] - CREATE: Cohort Retrieval Enhanced by Analysis of Text from Electronic Health Records using OMOP Common Data Model, S. Liu et al. 2019.

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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.