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awesome-ehr-deeplearning

Curated list of awesome papers for electronic health records(EHR) mining, machine learning, and deep learning.
https://github.com/hurcy/awesome-ehr-deeplearning

Last synced: 6 days ago
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      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Machine Learning Applications for Therapeutic Tasks with Genomics Data**, K. Huang, et al. 2021.
      • [pdf - **Patient similarity: methods and applications**, L. Dai, et al. 2020.
      • [pdf - **DeepHealth: Deep Learning for Health Informatics reviews, challenges, and opportunities on medical imaging,electronic health records, genomics, sensing, and online communication health**, G. H. Kwak, et al. 2019.
      • [pdf - **Reinforcement Learning in Healthcare: A Survey**, C. Yu, et al. 2019.
      • [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.
      • [pdf - **A multi-perspective combined recall and rank framework for Chinese procedure terminology normalization**, M. Liang et al. 2021.
      • [pdf - **Examining the impact of data quality and completeness of electronic health records on predictions of patients risks of cardiovascular disease**, Y. Li et al. 2019.
      • [pdf - **MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III**, S. Wang et al. 2019.
      • [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.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Improvement in Cardiovascular Risk Prediction with Electronic Health Records**, M. M. Pike et al. 2016.
      • [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.
      • [pdf - **Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals**, P. L. Teixeira et al. 2017.
      • [pdf - **Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data**, T. Wanyan et al. 2021.
      • [pdf - **Handling Non-ignorably Missing Features in Electronic Health Records Data Using Importance-Weighted Autoencoders**, D. K. Lim et al. 2021.
      • [pdf - **Heterogeneous Similarity Graph Neural Network on Electronic Health Records**, Z. Liu et al. 2021.
      • [pdf - **Predicting Patient Outcomes with Graph Representation Learning**, E. Rocheteau et al. 2021.
      • [pdf - **EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational Autoencoders**, S. Biswal et al. 2021.
      • [pdf - **Doctor2Vec: Dynamic Doctor Representation Learning for Clinical Trial Recruitment**, S. Biswal et al. 2019.
      • [pdf - **Modelling EHR timeseries by restricting feature interaction**, K. Zhang et al. 2019.
      • [pdf - **BEHRT: Transformer for Electronic Health Records**, Y. Li et al. 2019.
      • [pdf - **TAPER: Time-Aware Patient EHR Representation**, S. Darabi et al. 2019.
      • [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.
      • [pdf - **Graph Convolutional Transformer: Learning the Graphical Structure of Electronic Health Records**, E. Choi et al. 2019.
      • [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.
      • [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.
      • [pdf - **A Fully Private Pipeline for Deep Learning on Electronic Health Records**, E. Chou et al. 2019.
      • [pdf - **Neural Clinical Event Sequence Prediction through Personalized Online Adaptive Learning**, J. M. Lee et al. 2021.
      • [pdf - **DICE: Significance Clustering for Outcome-Aware Stratification**, Y. Huang et al. 2021.
      • [pdf - **Modeling Multivariate Clinical Event Time-series with Recurrent Temporal Mechanisms**, J. M. Lee et al. 2021.
      • [pdf - **Multi-scale Temporal Memory for Clinical Event Time-Series Prediction**, J. M. Lee et al. 2020.
      • [pdf - **Interpolation-Prediction Networks for Irregularly Sampled Time Series**, S. N. Shukla et al. 2019.
      • [pdf - **Improved Patient Classification with Language Model Pretraining Over Clinical Notes**, J. Kemp et al. 2019.
      • [pdf - **An attention based deep learning model of clinical events in the intensive care unit**, D. A. Kaji et al. 2019.
      • [pdf - **Early detection of sepsis utilizing deep learning on electronic health record event sequences**, S. M. Lauritsen et al. 2019.
      • [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 - **Recent context-based LSTM for Clinical Event Time-series Prediction**, J. Lee et al. 2018.
      • [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.
      • [pdf - **Modeling Missing Data in Clinical Time Series with RNNs**, Z. C. Lipton et al. 2016.
      • [pdf - **Risk factor identification for incident heart failure using neural network distillation and variable selection**, Y. Li et al. 2021.
      • [pdf - **An explainable Transformer-based deep learning model for the prediction of incident heart failure**, S. Rao et al. 2021.
      • [pdf - **Inference for the Case Probability in High-dimensional Logistic Regression**, Z. Guo et al. 2021.
      • [pdf - **Concept-based Model Explanations for Electronic Health Records**, S. Baur et al. 2020.
      • [pdf - **Modeling and Leveraging Analytic Focus During Exploratory Visual Analysis**, Z. Zhou et al. 2021.
      • [pdf - **Analyzing Time Attributes in Temporal Event Sequences**, J. Magallanes et al. 2019.
      • [pdf - **Selection Bias Tracking and Detailed Subset Comparison for High-Dimensional Data**, D. Borland et al. 2019.
      • [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.
      • [pdf - **Application of HL7 FHIR in a Microservice Architecture for Patient Navigation on Registration and Appointments**, G. N. Bettoni, et al. 2021.
      • [pdf - **A semi-autonomous approach to connecting proprietary EHR standards to FHIR**, M. Chapman, et al. 2019.
      • [pdf - **CREATE: Cohort Retrieval Enhanced by Analysis of Text from Electronic Health Records using OMOP Common Data Model**, S. Liu et al. 2019.
      • [pdf - **Trust Issues: Uncertainty Estimation Does Not Enable Reliable OOD Detection On Medical Tabular Data**, D. Ulmer, et al. 2020.
      • [pdf - **Machine Learning in Precision Medicine to Preserve Privacy via Encryption**, W. Briguglio, et al. 2021.
      • [pdf - **Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records**, Y. Yu, et al. 2021.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Multi-Time Attention Networks for Irregularly Sampled Time Series**, S. N. Shukla et al. 2021.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [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.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **HeteroMed: Heterogeneous Information Network for Medical Diagnosis**, A. Hosseini et al. 2018.
      • [pdf - **Countdown Regression: Sharp and Calibrated Survival Predictions**, A. Avati et al. 2018.
      • [pdf - **Inference for the Case Probability in High-dimensional Logistic Regression**, Z. Guo et al. 2021.
      • [pdf - **Modeling and Leveraging Analytic Focus During Exploratory Visual Analysis**, Z. Zhou et al. 2021.
      • [pdf - **Analyzing Time Attributes in Temporal Event Sequences**, J. Magallanes et al. 2019.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Big data from electronic health records for early and late translational cardiovascular research: challenges and potential**, H. Hemingway et al. 2017.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [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 - **Improved Patient Classification with Language Model Pretraining Over Clinical Notes**, J. Kemp et al. 2019.
      • [pdf - **MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records**, X. X. Zhang et al. 2019.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [pdf - **Risk factor identification for incident heart failure using neural network distillation and variable selection**, Y. Li et al. 2021.
      • [pdf - **PhenoStacks: Cross-Sectional Cohort Phenotype Comparison Visualizations**, M. Glueck et al. 2016.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [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.
      • [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.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Modelling EHR timeseries by restricting feature interaction**, K. Zhang et al. 2019.
      • [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 - **Federated and Differentially Private Learning for Electronic Health Records**, S. R. Pfohl et al. 2019.
      • [pdf - **A Fully Private Pipeline for Deep Learning on Electronic Health Records**, E. Chou et al. 2019.
      • [pdf - **Predicting Diabetes Disease Evolution Using Financial Records and Recurrent Neural Networks**, R. T. Sousa et al. 2019.
      • [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 - **CREATE: Cohort Retrieval Enhanced by Analysis of Text from Electronic Health Records using OMOP Common Data Model**, S. Liu et al. 2019.
      • [pdf - **Deep Diabetologist: Learning to Prescribe Hyperglycemia Medications with Hierarchical Recurrent Neural Networks**, J. Mei et al. 2018.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [pdf - **Generating Multi-label Discrete Patient Records using Generative Adversarial Networks**, E. Choi, 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.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Handling Non-ignorably Missing Features in Electronic Health Records Data Using Importance-Weighted Autoencoders**, D. K. Lim et al. 2021.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Measuring Patient Similarities via a Deep Architecture with Medical Concept Embedding**, L. Gligic et al. 2019.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Mining Electronic Health Records: A Survey**, P. Yadav et al. 2017.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [pdf - **Examining the impact of data quality and completeness of electronic health records on predictions of patients risks of cardiovascular disease**, Y. Li et al. 2019.
      • [pdf - **Development and validation of computable Phenotype to Identify and Characterize Kidney Health in Adult Hospitalized Patients**, T. Ozrazgat-Baslanti et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [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 - **Doctor2Vec: Dynamic Doctor Representation Learning for Clinical Trial Recruitment**, S. Biswal et al. 2019.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Risk Markers by Sex and Age Group for In-Hospital Mortality in Patients with STEMI or NSTEMI: an Approach based on Machine Learning**, B. Vázquez et al. 2021.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Heterogeneous Similarity Graph Neural Network on Electronic Health Records**, Z. Liu et al. 2021.
      • [pdf - **Predicting Patient Outcomes with Graph Representation Learning**, E. Rocheteau et al. 2021.
      • [pdf - **Graph Convolutional Transformer: Learning the Graphical Structure of Electronic Health Records**, E. Choi et al. 2019.
      • [pdf - **ClinicalVis: Supporting Clinical Task-Focused Design Evaluation**, M. Ghassemi et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **BEHRT: Transformer for Electronic Health Records**, Y. Li et al. 2019.
      • [pdf - **Using Visual Analytics to Interpret Predictive Machine Learning Models**, J. Krause et al. 2016.
      • [pdf - **Application of HL7 FHIR in a Microservice Architecture for Patient Navigation on Registration and Appointments**, G. N. Bettoni, et al. 2021.
      • [pdf - **Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records**, Y. Yu, et al. 2021.
      • [pdf - **High-throughput Phenotyping with Temporal Sequences**, H. Estiri et al. 2019.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Scalable and accurate deep learning with electronic health records**, A. Rajkomar et al. 2018.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **DICE: Significance Clustering for Outcome-Aware Stratification**, Y. Huang et al. 2021.
      • [pdf - **Mixed Effect Composite RNN-GP: A Personalized and Reliable Prediction Model for Healthcare**, I. Chung et al. 2018.
      • [pdf - **An explainable Transformer-based deep learning model for the prediction of incident heart failure**, S. Rao et al. 2021.
      • [pdf - **DPVis: Visual Exploration of Disease Progression Pathways**, B. C. Kwon et al. 2019.
      • [pdf - **DeepHealth: Deep Learning for Health Informatics reviews, challenges, and opportunities on medical imaging,electronic health records, genomics, sensing, and online communication health**, G. H. Kwak, et al. 2019.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Concept-based Model Explanations for Electronic Health Records**, S. Baur et al. 2020.
      • [pdf - **Machine Learning Applications for Therapeutic Tasks with Genomics Data**, K. Huang, et al. 2021.
      • [pdf - **Patient similarity: methods and applications**, L. Dai, et al. 2020.
      • [pdf - **Reinforcement Learning in Healthcare: A Survey**, C. Yu, et al. 2019.
      • [ref - **A guide to deep learning in healthcare**, A. Esteva et al. 2019.
      • [pdf - **Opportunities in Machine Learning for Healthcare**, M. Ghassemi et al. 2018.
      • [pdf - **A multi-perspective combined recall and rank framework for Chinese procedure terminology normalization**, M. Liang et al. 2021.
      • [pdf - **MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III**, S. Wang et al. 2019.
      • [ref - **Mining electronic health records: towards better research applications and clinical care**, P. B. Jensen et al. 2012.
      • [pdf - **Risk Markers by Sex and Age Group for In-Hospital Mortality in Patients with STEMI or NSTEMI: an Approach based on Machine Learning**, B. Vázquez et al. 2021.
      • [pdf - **Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data**, T. Wanyan et al. 2021.
      • [pdf - **EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational Autoencoders**, S. Biswal et al. 2021.
      • [pdf - **TAPER: Time-Aware Patient EHR Representation**, S. Darabi et al. 2019.
      • [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 - **Natural Language Generation for Electronic Health Records**, S. Lee. 2018.
      • [pdf - **Neural Clinical Event Sequence Prediction through Personalized Online Adaptive Learning**, J. M. Lee et al. 2021.
      • [pdf - **Multi-Time Attention Networks for Irregularly Sampled Time Series**, S. N. Shukla et al. 2021.
      • [pdf - **Interpolation-Prediction Networks for Irregularly Sampled Time Series**, S. N. Shukla et al. 2019.
      • [pdf - **Early detection of sepsis utilizing deep learning on electronic health record event sequences**, S. M. Lauritsen et al. 2019.
      • [pdf - **Selection Bias Tracking and Detailed Subset Comparison for High-Dimensional Data**, D. Borland et al. 2019.
      • [pdf - **Visualizing Patient Timelines in the Intensive Care Unit**, D. L. Lambert et al. 2018.
      • [pdf - **A semi-autonomous approach to connecting proprietary EHR standards to FHIR**, M. Chapman, et al. 2019.
      • [pdf - **Machine Learning in Precision Medicine to Preserve Privacy via Encryption**, W. Briguglio, et al. 2021.

  • Clinical Trial Recruitment

    • [pdf - **COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matching**, J. Gao, et al. 2020.
    • [pdf - **DeepEnroll: Patient-Trial Matching with Deep Embedding and Entailment Prediction**, X. Zhang, et al. 2020.
    • [pdf - **COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matching**, J. Gao, et al. 2020.
    • [pdf - **DeepEnroll: Patient-Trial Matching with Deep Embedding and Entailment Prediction**, X. Zhang, et al. 2020.
Categories
Uncategorized 340 Clinical Trial Recruitment 4
Sub Categories
Uncategorized 340