└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # An-Incomplete-ML-Paper-Collection-for-BioMedical-Applications 2 | This is a repository that collects machine learning papers on biomedical application. 3 | 4 | [Download](http://pan.baidu.com/s/1o8G6Gxs) 5 | 6 | # Table of Contents 7 | 1. [Review](#review) 8 | 2. [Phenotype](#phenotype) 9 | 3. [NER-RE-KG](#ner-re-kg) 10 | 4. [Medical Concept Representation](#medical-concept-representation) 11 | 5. [Cancer](#cancer) 12 | 5. [ECG](#ecg) 13 | 5. [Image Interpretation](#image-interpretation) 14 | 5. [QA](#qa) 15 | 5. [Readmission Rate](#readmission-rate) 16 | 17 | ## Review 18 | 19 | |Title|Year|Journal|Team| 20 | |---|---|---|---| 21 | | What Can Natural Language Processing do For Clinical Decision Support? | 2009 | Journal of Biomedical Informatics |Dina Fushman@NIH| 22 | |Deep Learning for Health Informatics|2017|Journal of Biomedical and Health Informatics|杨广中@ICL| 23 | |Big Data for Health|2015|Journal of Biomedical and Health Informatics|杨广中@ICL| 24 | |Mining Electronic Health Records: Towards Better Research Applications and Clinical Care|2012|Nature Reviews Genetics|| 25 | |A Review of Approaches to Identifying Patient Phenotype Cohorts Using Electronic Health Records|2014|JIMIA|| 26 | |Machine Learning and Decision Support in Critical Care|2016|Proceedings of the IEEE || 27 | |Mining Electronic Health Records- A Survey|2016|ACM Computing Surveys|| 28 | 29 | 30 | ## Phenotype 31 | 32 | |Title|Year|Journal|Team| 33 | |---|---|---|---| 34 | |Deep Patient－ An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records|2016|Scientific Reports|Riccardo Miotto /Joel T. Dudley@Icahn School of Medicine at Mount Sinai| 35 | |Identification of type 2 diabetes subgroups through topological analysis of patient similarity|2015||Li Li /Joel T. Dudley@Icahn School of Medicine at Mount Sinai| 36 | |Doctor AI- Predicting Clinical Events via Recurrent Neural Networks|2016|JMLR|Edward Choi/Jimeng Sun@GIT| 37 | |Deepr: A Convolutional Net for Medical Records |2017|Journal of Biomedical and Health Informatics|| 38 | |A Comparison of Rule-Based and Deep Learning Models for Patient Phenotyping |2017| | | 39 | |A Shared Task Involving Multi-Label Classification of Clinical Free Text|2007| | | 40 | |Condensed Memory Networks for Clinical Diagnostic Inferencing|2017| | | 41 | |Deep Learning for Healthcare Decision Making with EMRs|2014| | | 42 | |Deep Recurrent Neural Networks for Predicting Intraoperative and Postoperative Outcomes and Trends|2017| | | 43 | |Distill Knowledge from Deep Networks with Application to Healthcare Domain|2015| |Zhengping Che/Yan Liu@University of Southern California| 44 | |Estimating Patient’s Health State Using Latent Structure Inferred from Clinical Time Series and Text|2017| | | 45 | |Exploiting Convolutional Neural Network for Risk Prediction with Medical Feature Embedding|2017| |Zhengping Che/Yan Liu@University of Southern California| 46 | |GRAM－ Graph-Based Attention Model for Healthcare Representation Learning|2017|KDD|Edward Choi/Jimeng Sun 47 | GIT| 48 | |Impact of Data Fragmentation across healthcare centers on the accuracy of a high-throughput clinical phenotyping algorithm for specifying subjects With Type 2 Diabetes Mellitus|2012|JAMIA| | 49 | |Learning to Diagnose with LSTM Recurrent Neural Networks|2016|ICLR|Zachary C. Lipton/David C. Kale@UCSD| 50 | |PheKB- a Catalog and Workflow for Creating Electronic Phenotype Algorithms for Transportability|Year|Journal|Team| 51 | |Predicting Clinical Events by Combining Static and Dynamic Information using Recurrent Neural Networks|2016| | | 52 | |Recurrent Neural Networks for Multivariate Time Series with Missing Values|2016| | | 53 | |Relational Machine Learning for Electronic Health Record-Driven Phenotyping|2014|Journal of Biomedical Informatics| | 54 | |RETAIN－ An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism|2016|NIPS|Edward Choi/Jimeng Sun@GIT| 55 | |Risk Prediction with Electronic Health Records- A Deep Learning Approach|2015| | | 56 | |Structured Prediction Models for RNN based Sequence Labeling in Clinical Text|2016|EMNLP| | 57 | |Temporal Pattern and Association Discovery of Diagnosis Codes Using Deep Learning|2015| | | 58 | |Validation of Electronic Medical Record-Based Phenotyping Algorithm- Results and Lessons Learned from the eMERGE Network|2013|JAMIA| | 59 | 60 | ## NER-RE-KG 61 | 62 | |Title|Year|Journal|Team| 63 | |---|---|---|---| 64 | |A Study of Active Learning Methods for Named Entity Recognition in Clinical Text|2015|Journal of Biomedical Informatics|YuKun Chen/Hua Xu@UTHealth| 65 | |A Study of Neural Word Embedding for Named Entity Recognition in Clinical Text|2015||Yonghui Wu/Hua Xu@UTHealth| 66 | |Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network|2015|IMIA|Yonghui Wu/Hua Xu@UTHealth| 67 | |Unsupervised Biomedical Named Entity Recognition- Experiments with Clinical and Biological Texts|2013|Journal of Biomedical Informatics|Journal of Biomedical Informatics| 68 | |Automatic Extraction of Relations Between Medical Concepts in Clinical Texts|2011|JAMIA|| 69 | |Detecting Concept Relations in Clinical Text- Insights from a State-of-the-Art Model|2013|Journal of Biomedical Informatics|| 70 | |Extraction of Medical Term Relations from Text|2008||| 71 | |Medical Relation Extraction with Maniflod Models|2014|ACL|Chang Wang/James Fan@IBM Watson| 72 | |Relation Extraction from Clinical Texts using Domain Invariant Convolutional Neural Network|2016|ACL|| 73 | |Learning a Health Knowledge Graph from Electronic Medical Records|2017|Scientific Reports|NYU/MIT| 74 | |Extracting Clinical Entities and their Assertions from Chinese Electronic Medical Records Based on Machine Learning|2016| | | 75 | |Extracting Information from Textual Documents in the Electronic Health Record- A Review of Recent Research|2008| | | 76 | 77 | 78 | ## Medical Concept Representation 79 | 80 | |Title|Year|Journal|Team| 81 | |---|---|---|---| 82 | | Learning Low-Dimensional Representations of Medical Concepts | 2016 | JAMIA |Y Choi/David Sontag@NYU| 83 | |Multi-layer Representation Learning for Medical Concepts| 2016 | KDD |Edward Choi/Jimeng Sun@GIT| 84 | |Using Deep Learning Towards Biomedical Knowledge Discovery| 2017 | | | 85 | 86 | ## Cancer 87 | 88 | |Title|Year|Journal|Team| 89 | |---|---|---|---| 90 | |Biomedical Text Mining and its Applications in Cancer Research| 2013 | Journal of Biomedical Informatics | | 91 | |Text Mining of Cancer-Related Information- Review of Current Status and Future Directions| 2014 | | | 92 | 93 | ## ECG 94 | 95 | |Title|Year|Journal|Team| 96 | |---|---|---|---| 97 | | Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks | 2017 | |Pranav Rajpurkar/Andrew Y. Ng@Stanford| 98 | 99 | ## Image Interpretation 100 | 101 | |Title|Year|Journal|Team| 102 | |---|---|---|---| 103 | | Interleaved TextImage Deep Mining on a Large-Scale Radiology Database for Automated Image Interpretation | 2016 | JMLR |HC Shin/RM. Summers@NIH| 104 | 105 | ## QA 106 | 107 | |Title|Year|Journal|Team| 108 | |---|---|---|---| 109 | | Disease Inference from Health-Related Questions via Sparse Deep Learning | 2015 | | | 110 | |Reliable Medical Diagnosis from Crowdsourcing- Discover Trustworthy Answers from Non-Experts| 2017 | | | 111 | 112 | ## Readmission Rate 113 | 114 | |Title|Year|Journal|Team| 115 | |---|---|---|---| 116 | | A comparison of models for predicting early hospital readmissions| 2015 | Journal of Biomedical Informatics |Joseph Futoma/Joseph Lucas@DukeU| 117 | |Predicting 30-Day All-Cause Readmissions from Hospital Inpatient Discharge Data| 2016 | |Chengliang Yang/Sanjay Ranka@FloridaU| 118 | |Predictive Modeling of Hospital Readmission Rates using Electronic Medical Record-Wide Machine Learning- A Case-Study using Mount Sinai Heart Failure Cohort| 2017 ||Icahn School of Medicine at Mount Sinai | 119 | 120 | --------------------------------------------------------------------------------