Predicting severe clinical events by learning about life-saving actions and outcomes using distant supervision
暂无分享,去创建一个
Eric Horvitz | Lucy Vanderwende | Meliha Yetisgen-Yildiz | Dae Hyun Lee | Dae Hyun Lee | E. Horvitz | Lucy Vanderwende | Meliha Yetisgen-Yildiz | D. H. Lee
[1] N. Shah,et al. Implementing Machine Learning in Health Care - Addressing Ethical Challenges. , 2018, The New England journal of medicine.
[2] Jenna Wiens,et al. Machine Learning for Healthcare: On the Verge of a Major Shift in Healthcare Epidemiology , 2018, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.
[3] Collin M. Stultz,et al. Machine Learning Improves Risk Stratification After Acute Coronary Syndrome , 2017, Scientific Reports.
[4] Gerasimos S Filippatos,et al. 2017 ACC/AHA/HFSA Focused Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America. , 2017, Journal of the American College of Cardiology.
[5] M. Manns,et al. EASL Clinical Practical Guidelines on the management of acute (fulminant) liver failure. , 2017, Journal of hepatology.
[6] Eric Horvitz,et al. Predicting Mortality of Intensive Care Patients via Learning about Hazard , 2017, AAAI.
[7] Melissa Aczon,et al. Dynamic Mortality Risk Predictions in Pediatric Critical Care Using Recurrent Neural Networks , 2017, ArXiv.
[8] Uli K. Chettipally,et al. Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach , 2016, JMIR medical informatics.
[9] Reynold Xin,et al. Introduction to Spark 2.0 for Database Researchers , 2016, SIGMOD Conference.
[10] Mihaela van der Schaar,et al. ForecastICU: A Prognostic Decision Support System for Timely Prediction of Intensive Care Unit Admission , 2016, ICML.
[11] Nigam H. Shah,et al. Learning statistical models of phenotypes using noisy labeled training data , 2016, J. Am. Medical Informatics Assoc..
[12] Peter Szolovits,et al. MIMIC-III, a freely accessible critical care database , 2016, Scientific Data.
[13] Ameet Talwalkar,et al. MLlib: Machine Learning in Apache Spark , 2015, J. Mach. Learn. Res..
[14] P. Kamath,et al. Acute-on-Chronic Liver Failure. , 2015, Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association.
[15] Hisashi Kashima,et al. Simultaneous Modeling of Multiple Diseases for Mortality Prediction in Acute Hospital Care , 2015, KDD.
[16] Johannes Gehrke,et al. Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission , 2015, KDD.
[17] P. Pronovost,et al. A targeted real-time early warning score (TREWScore) for septic shock , 2015, Science Translational Medicine.
[18] J. Roy,et al. Validity of diagnostic codes and laboratory tests of liver dysfunction to identify acute liver failure events , 2015, Pharmacoepidemiology and drug safety.
[19] G. Lip,et al. Thrombotic complications in heart failure: an underappreciated challenge. , 2014, Circulation.
[20] D. Bates,et al. Big data in health care: using analytics to identify and manage high-risk and high-cost patients. , 2014, Health affairs.
[21] Sung Wook Baik,et al. Prioritization of brain MRI volumes using medical image perception model and tumor region segmentation , 2013, Comput. Biol. Medicine.
[22] Cosmin Adrian Bejan,et al. Pneumonia identification using statistical feature selection , 2012, J. Am. Medical Informatics Assoc..
[23] H. Rabb,et al. The distant organ effects of acute kidney injury. , 2012, Kidney international.
[24] Summary of Recommendation Statements , 2012, Kidney international supplements.
[25] P. Papadakos,et al. Acute Respiratory Failure Complicating Advanced Liver Disease , 2012, Seminars in Respiratory and Critical Care Medicine.
[26] Kevin M. Heard,et al. Implementation of a real-time computerized sepsis alert in nonintensive care unit patients* , 2011, Critical care medicine.
[27] Andrew McCallum,et al. Modeling Relations and Their Mentions without Labeled Text , 2010, ECML/PKDD.
[28] Daniel Jurafsky,et al. Distant supervision for relation extraction without labeled data , 2009, ACL.
[29] C. Parikh,et al. Acute kidney injury in cirrhosis , 2008, Hepatology.
[30] L. Brochard,et al. Effects of inspiratory pause on CO2 elimination and arterial PCO2 in acute lung injury. , 2008, Journal of applied physiology.
[31] J. McGaughey,et al. Outreach and Early Warning Systems (EWS) for the prevention of intensive care admission and death of critically ill adult patients on general hospital wards. , 2007, The Cochrane database of systematic reviews.
[32] M. Falagas,et al. Bacterial Infection-Related Morbidity and Mortality in Cirrhosis , 2007, The American Journal of Gastroenterology.
[33] Gary Nichols,et al. Processes, facies and architecture of fluvial distributary system deposits , 2007 .
[34] J. Gardner-Thorpe,et al. The value of Modified Early Warning Score (MEWS) in surgical in-patients: a prospective observational study. , 2006, Annals of the Royal College of Surgeons of England.
[35] Mark Goadrich,et al. The relationship between Precision-Recall and ROC curves , 2006, ICML.
[36] J. Zimmerman,et al. Acute Physiology and Chronic Health Evaluation (APACHE) IV: Hospital mortality assessment for today’s critically ill patients* , 2006, Critical care medicine.
[37] R. Paterson,et al. Prediction of in-hospital mortality and length of stay using an early warning scoring system: clinical audit. , 2006, Clinical medicine.
[38] M. Bauer,et al. Implementation of an evidence-based “standard operating procedure” and outcome in septic shock* , 2006, Critical care medicine.
[39] F. Gao,et al. The impact of compliance with 6-hour and 24-hour sepsis bundles on hospital mortality in patients with severe sepsis: a prospective observational study , 2005, Critical care.
[40] Joseph V Bonventre,et al. Acute kidney injury, mortality, length of stay, and costs in hospitalized patients. , 2005, Journal of the American Society of Nephrology : JASN.
[41] J. Vincent,et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure , 1996, Intensive Care Medicine.
[42] C. Subbe,et al. Effect of introducing the Modified Early Warning score on clinical outcomes, cardio‐pulmonary arrests and intensive care utilisation in acute medical admissions * , 2003, Anaesthesia.
[43] J. Steiner,et al. Gender, age, and heart failure with preserved left ventricular systolic function. , 2003, Journal of the American College of Cardiology.
[44] Robert E. Schapire,et al. The Boosting Approach to Machine Learning An Overview , 2003 .
[45] Bo Pang,et al. Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.
[46] J. Friedman. Stochastic gradient boosting , 2002 .
[47] M. Cowie,et al. Fortnightly review: anticoagulation in heart disease. , 1999, BMJ.
[48] Tin Kam Ho,et al. The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[49] J. Hintze,et al. Violin plots : A box plot-density trace synergism , 1998 .
[50] C. Sprung,et al. Multiple organ dysfunction score: a reliable descriptor of a complex clinical outcome. , 1995, Critical care medicine.
[51] Janelle Klar,et al. Customized probability models for early severe sepsis in adult intensive care patients. Intensive Care Unit Scoring Group. , 1995, JAMA.
[52] Sankey V. Williams,et al. Hospital and Patient Characteristics Associated With Death After Surgery: A Study of Adverse Occurrence and Failure to Rescue , 1992, Medical care.
[53] W. Knaus,et al. Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. , 1992, Chest.
[54] J. L. Gall,et al. A simplified acute physiology score for ICU patients , 1984, Critical care medicine.