Predicting severe clinical events by learning about life-saving actions and outcomes using distant supervision

[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.