Mining Electronic Health Records (EHRs): A Survey

Yadav, Pranjul, Steinbach, Michael, Kumar, Vipin, Simon, Gyorgy·2018·ACM Computing Surveys

Cites (432)

Items 401–435 of 435

  1. A Data Mining Approach to Determine Sepsis Guideline Impact on Inpatient Mortality and Complications.
    Lisiane Pruinelli, Pranjul Yadav, Andrew Hangsleben, Jakob Johnson, Sanjoy Dey, Maribet C. McCarty, Vipin Kumar, Connie White Delaney, Michael S. Steinbach, Bonnie L. Westra et al. · 2016 · PubMed Central
  2. Multi-layer Representation Learning for Medical Concepts
    Edward Choi, Mohammad Taha Bahadori, Elizabeth Searles · 2016 · arXiv (Cornell University)
  3. The role of the electronic medical record in the assessment of health related quality of life.
    Serguei Pakhomov, Nilay D. Shah, Holly K. Van Houten, Penny Hanson, Steven A. Smith · 2011 · PubMed Central
  4. Exploring Patient Risk Groups with Incomplete Knowledge
    Xiang Wang, Fei Wang, Jun Wang, Buyue Qian, Jianying Hu · 2013 · Proceedings
  5. SELECTION OF NEIGHBORHOOD CONTROLS FOR A STUDY OF CORONARY ARTERY DISEASE
    Eyriey Jacqueline, Corleen J. Thompson, John R. Crouse · 1989 · American Journal of Epidemiology
  6. Learning causal and predictive clinical practice guidelines from data.
    Subramani Mani, Constantin Aliferis, Shanthi Krishnaswami, Theodore A. Kotchen · 2007 · PubMed
  7. Nonparametric Discovery of Learning Patterns and Autism Subgroups from Therapeutic Data
    Pratibha Vellanki, T.V. Duong, Svetha Venkatesh, Dinh Phung · 2014 · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition
  8. Privacy-Preserving Predictive Models for Lung Cancer Survival Analysis
    Glenn Fung, Shipeng Yu, Cary Oberije, Dirk De Ruysscher, Philippe Lambin, Sriram Krishnan, Rohit Bharat · 2008
  9. Recreational injuries and deaths in northern Wisconsin: analysis of injuries and fatalities from snowmobiles over 3 years.
    T Gabert, Stueland Dt · 1993 · PubMed
  10. Admission duration model for infant treatment (ADMIT)
    Keith Feldman, Nitesh V. Chawla · 2014 · IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
  11. Impact of Density of Lab Data in EHR for Prediction of Potentially Preventable Events
    Chandrima Sarkar, Jaideep Srivastava · 2013 · IEEE International Conference on Healthcare Informatics (ICHI)
  12. Using semantic web technologies for quality measure phenotyping algorithm representation and automatic execution on EHR data
    Feichen Shen, Dingcheng Li, Hongfang Liu, Yugyung Lee, Jyotishman Pathak, Christopher G. Chute, Cui Tao · 2014 · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...)
  13. SN algorithm: analysis of temporal clinical data for mining periodic patterns and impending augury
    Dipankar Sengupta, Pradeep Kumar Naik · 2013 · Journal of Clinical Bioinformatics
  14. Mining Anti-coagulant Drug-Drug Interactions from Electronic Health Records Using Linked Data
    Jyotishman Pathak, Richard C. Kiefer, Christopher G. Chute · 2013 · Lecture notes in computer science
  15. A Heterogeneous Multi-Task Learning for Predicting RBC Transfusion and Perioperative Outcomes
    Che G. Ngufor, Sudhindra G. Upadhyaya, Dennis H. Murphree, Nageswar R. Madde, Daryl Jon Kor, Jyotishman Pathak · 2015 · Lecture notes in computer science
  16. SCALING PERSONALIZED HEALTHCARE WITH BIG DATA
    Keith Feldman, Nitesh V. Chawla · 2014
  17. Trends in Potentially Preventable Hospital Admissions among Adults and Children, 2005–2010: Statistical Brief #151
    Celeste Marie Torio, Anne Elixhauser, Andrews Rm · 2013 · Europe PMC (PubMed Central)
  18. DISEASE AND MEDICATION NETWORKS: AN INSIGHT INTO DISEASE-DRUG INTERACTIONS
    Dipanwita Dasgupta, Nitesh V. Chawla · 2014
  19. Sparse Linear Dynamical System with Its Application in Multivariate Clinical Time Series
    Zitao Liu, Miloš Hauskrecht · 2013 · arXiv (Cornell University)
  20. Sparse Linear Dynamical System with Its Application in Multivariate Clinical Time Series
    Zitao Liu, Miloš Hauskrecht · 2013 · arXiv (Cornell University)
  21. Prediction of Incident Diabetes Mellitus in Middle-aged Adults: The Framingham Offspring Study
    Wilson, Peter W. F. · 2007 · Archives of Internal Medicine
  22. Bayesian modeling of temporal properties of infectious disease in a college student population
    Zhengming Xing, Bradley P. Nicholson, Monica Jimenez, Timothy Veldman, Lori L. Hudson, Joseph E. Lucas, David B. Dunson, Aimee K. Zaas, Christopher W. Woods, Geoffrey S. Ginsburg et al. · 2013 · Journal of Applied Statistics
  23. DiseaseAtlas: Multi-facet visual analytics for online disease articles
    Jimeng Sun, David Gotz, Nan Cao · 2010 · Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society
  24. Causal Pattern Mining in Highly Heterogeneous and Temporal EHRs Data
    Pranjul Yadav · 2017 · University of Minnesota Digital Conservancy (University of Minnesota)
  25. A multivariate probabilistic method for comparing two clinical datasets
    Yuriy Sverchkov, Shyam Visweswaran, Gilles Clermont, Miloš Hauskrecht, Gregory F. Cooper · 2012 · International Health Informatics Symposium
  26. Oestrogen treatment and endometrial carcinoma
    B. E. C. Nordin · 1977 · BMJ
  27. Clinical Decision Making: A Framework for Predicting Rx Response
    Aarti Sathyanarayana, Jyotishman Pathak, Rozalina G. McCoy, Santiago Romero Brufau, Maryam Panaziahar, Jaideep Srivastava · 2014 · IEEE ... International Conference on Data Mining workshops
  28. Clinical Epidemiology: The Architecture of Clinical Research.
    Gruber Sb · 1986 · The Yale Journal of Biology and Medicine
  29. Stabilizing Sparse Cox Model using Clinical Structures in Electronic Medical Records
    Shivapratap Gopakumar, Truyen Tran, Dinh Phung, Svetha Venkatesh · 2014 · arXiv (Cornell University)
  30. A Hierarchical Ensemble of α-Trees for Predicting Expensive Hospital Visits
    Yubin Park, Joydeep Ghosh · 2014 · Lecture notes in computer science
  31. Data Mining Methodologies to Discover Best practices for Diabetic Patients with Health Disparities.
    Lisiane Pruinelli, Sanjoy Dey, György Simon, Pranjul Yadav, Andrew Hangsleben, Katherine Hauwiller, Vipin Kumar, Connie White-Delaney, Michael S. Steinbach, Bonnie L. Westra · 2014 · AMIA
  32. Predicting the Factors of Improvement of Health Status of Home Health Care Patients: A Holistic Data Mining Approach.
    Sanjoy Dey, Katherine Hauwiller, Pranjul Yadav, Michael S. Steinbach, György Simon, Vipin Kumar, Connie White-Delaney, Bonnie L. Westra · 2015 · AMIA
  33. Clustering Health Data to Discover EBP Interventions for Sepsis Prevention and Treatment for Health Disparities.
    Lisiane Pruinelli, Pranjul Yadav, Andrew Hangsleben, Kevin Schiroo, Sanjoy Dey, György Simon, Maribet C. McCarty, Vipin Kumar, Connie White-Delaney, Michael S. Steinbach et al. · 2015 · AMIA
  34. Causal Inference in Observational Data
    Pranjul Yadav, Lisiane Prunelli, Hoff, Alexander · 2016 · arXiv (Cornell University)
  35. Untitled work
    Tibshirani · 1997 · 10.1002/(sici)1097-0258(19970228)16:4