Machine learning in critical care: state-of-the-art and a sepsis case study
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Alfredo Vellido | Vicent Ribas | Carles Morales | Adolfo Ruiz Sanmartín | Juan Carlos Ruiz Rodríguez | V. Ribas | A. Vellido | Carles Morales | Adolfo Ruiz Sanmartín
[1] Richard E. Neapolitan,et al. Probabilistic Methods for Bioinformatics: with an Introduction to Bayesian Networks , 2009 .
[2] Kavishwar B. Wagholikar,et al. Modeling Paradigms for Medical Diagnostic Decision Support: A Survey and Future Directions , 2012, Journal of Medical Systems.
[3] Fan Wu,et al. The Prediction of Severe Sepsis Using SVM Model , 2009, BIOCOMP.
[4] Mukta Paliwal,et al. Neural networks and statistical techniques: A review of applications , 2009, Expert Syst. Appl..
[5] Rüdiger W. Brause,et al. Neural Networks for Sepsis Prediction - the MEDAN-Project 1 , 2004 .
[6] Rüdiger W. Brause,et al. Data quality aspects of a database for abdominal septic shock patients , 2004, Comput. Methods Programs Biomed..
[7] Joseph V. Hajnal,et al. Machine-learning to characterise neonatal functional connectivity in the preterm brain , 2016, NeuroImage.
[8] Helio J. C. Barbosa,et al. NICeSim: An open-source simulator based on machine learning techniques to support medical research on prenatal and perinatal care decision making , 2014, Artif. Intell. Medicine.
[9] Alfredo Vellido,et al. Severe sepsis mortality prediction with logistic regression over latent factors , 2012, Expert Syst. Appl..
[10] Mitchell M. Levy,et al. 2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference , 2003, Intensive Care Medicine.
[11] Reza Safdari,et al. Developing a Fuzzy Expert System to Predict the Risk of Neonatal Death , 2016, Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH.
[12] C. Schuh,et al. Sepsis and Septic Shock Analysis using Neural Networks , 2007, NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society.
[13] Maurizio Vichi,et al. Studies in Classification Data Analysis and knowledge Organization , 2011 .
[14] Linda M. Eerikäinen,et al. Reduction of false arrhythmia alarms using signal selection and machine learning , 2016, Physiological measurement.
[15] L. Celi,et al. Disease-based Modeling to Predict Fluid Response in Intensive Care Units , 2013, Methods of Information in Medicine.
[16] Mahdi Mahfouf,et al. A Fuzzy Decision Support System for Therapy Administration in Cardiovascular Intensive Care Patients , 2007, 2007 IEEE International Fuzzy Systems Conference.
[17] E. Draper,et al. APACHE II: A severity of disease classification system , 1985, Critical care medicine.
[18] Alfredo Vellido,et al. Severe sepsis mortality prediction with relevance vector machines , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[19] R. Iniesta,et al. Machine learning, statistical learning and the future of biological research in psychiatry , 2016, Psychological Medicine.
[20] Dwayne R. Westenskow,et al. A breathing circuit alarm system based on neural networks , 1989, Journal of Clinical Monitoring.
[21] Shamim Nemati,et al. Machine Learning and Decision Support in Critical Care , 2016, Proceedings of the IEEE.
[22] G. Lightbody,et al. Grading hypoxic–ischemic encephalopathy severity in neonatal EEG using GMM supervectors and the support vector machine , 2016, Clinical Neurophysiology.
[23] Michele C Walsh,et al. The potential for harm from alarm fatigue in single‐room NICUs , 2015, Acta paediatrica.
[24] Herwig Gerlach,et al. Reply to Zandstra and van der Voort , 2004, Intensive Care Medicine.
[25] Peter Szolovits,et al. Continuous State-Space Models for Optimal Sepsis Treatment: a Deep Reinforcement Learning Approach , 2017, MLHC.
[26] Paulo J. G. Lisboa,et al. Neural Networks and Other Machine Learning Methods in Cancer Research , 2007, IWANN.
[27] P Niederer,et al. Improved monitoring of preterm infants by Fuzzy Logic. , 1996, Technology and health care : official journal of the European Society for Engineering and Medicine.
[28] D. A. Linkens,et al. Development of a knowledge-based simulator for haemodynamic support of septic shock , 1998 .
[29] T. H. Kyaw,et al. Multiparameter Intelligent Monitoring in Intensive Care II: A public-access intensive care unit database* , 2011, Critical care medicine.
[30] João Maroco,et al. Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests , 2011, BMC Research Notes.
[31] Kayvan Najarian,et al. Biomedical Informatics for Computer-Aided Decision Support Systems: A Survey , 2013, TheScientificWorldJournal.
[32] David J. Hand,et al. Modern Data Analysis: A Clash of Paradigms , 2002 .
[33] Jeff Gilchrist,et al. Neonatal mortality prediction using real-time medical measurements , 2011, 2011 IEEE International Symposium on Medical Measurements and Applications.
[34] Fred H. Hamker,et al. Septic Shock Diagnosis by Neural Networks and Rule Based Systems , 2002 .
[35] Jonathan M. Teich,et al. Grand challenges in clinical decision support , 2008, J. Biomed. Informatics.
[36] François Hemery,et al. Mortality prediction using SAPS II: an update for French intensive care units , 2005, Critical care.
[37] Seth Flaxman,et al. European Union Regulations on Algorithmic Decision-Making and a "Right to Explanation" , 2016, AI Mag..
[38] William P. Marnane,et al. Clinical implementation of a neonatal seizure detection algorithm , 2015, Decis. Support Syst..
[39] Alfredo Vellido,et al. Applying Conditional Independence Maps to Improve Sepsis Prognosis , 2016, 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).
[40] Katharina Morik,et al. Knowledge discovery and knowledge validation in intensive care , 2000, Artif. Intell. Medicine.
[41] Jeff Gilchrist,et al. PPADS: Physician-PArent Decision-Support for Neonatal Intensive Care , 2013, MedInfo.
[42] T. Lasko,et al. Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data , 2013, PloS one.
[43] Atul J Butte,et al. Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis , 2010, Critical care.
[44] L S Wong,et al. A comparison of ICU mortality prediction using the APACHE II scoring system and artificial neural networks. , 1998, Anaesthesia.
[45] Peter Szolovits,et al. MIMIC-III, a freely accessible critical care database , 2016, Scientific Data.
[46] M Fieschi,et al. Medical Decision Support Systems: Old Dilemmas and new Paradigms? , 2003, Methods of Information in Medicine.
[47] G. Clermont,et al. Predicting hospital mortality for patients in the intensive care unit: A comparison of artificial neural networks with logistic regression models , 2001, Critical care medicine.
[48] Dimitrios Koutsouris,et al. Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future , 2016, Biomedical engineering and computational biology.
[49] David A. Clifton,et al. Patient physiological monitoring with machine learning , 2016 .
[50] Charles E. Heckler,et al. Applied Multivariate Statistical Analysis , 2005, Technometrics.
[51] Mevlut Ture,et al. Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease , 2008, Expert Syst. Appl..
[52] Eric R. Ziegel,et al. The Elements of Statistical Learning , 2003, Technometrics.
[53] Paulo J. G. Lisboa,et al. A review of evidence of health benefit from artificial neural networks in medical intervention , 2002, Neural Networks.
[54] Brendan J. Frey,et al. Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets , 2016, Proceedings of the IEEE.
[55] R G Mark,et al. MIMIC II: a massive temporal ICU patient database to support research in intelligent patient monitoring , 2002, Computers in Cardiology.
[56] Margaret M Parker,et al. Surviving Sepsis Campaign guidelines for management of severe sepsis and septic shock , 2004, Critical care medicine.
[57] V. Marx. Biology: The big challenges of big data , 2013, Nature.
[58] Geraldine B. Boylan,et al. Neonatal seizure detection using convolutional neural networks , 2017, 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP).
[59] Peter J Haug,et al. Modeling Glucose Homeostasis and Insulin Dosing in an Intensive Care Unit using Dynamic Bayesian Networks. , 2010, AMIA ... Annual Symposium proceedings. AMIA Symposium.
[60] Daniel B. Neill,et al. Using Artificial Intelligence to Improve Hospital Inpatient Care , 2013, IEEE Intelligent Systems.
[61] Alfredo Vellido,et al. On the Use of Graphical Models to Study ICU Outcome Prediction in Septic Patients Treated with Statins , 2011, CIBB.
[62] Jürgen Paetz. Metric rule generation with septic shock patient data , 2001, Proceedings 2001 IEEE International Conference on Data Mining.
[63] Trevor Hastie,et al. The Elements of Statistical Learning , 2001 .
[64] Jeff Gilchrist,et al. Integration of outcome estimations with a clinical decision support system: Application in the neonatal intensive care unit (NICU) , 2015, 2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings.
[65] Gregory R. Grant,et al. Bioinformatics - The Machine Learning Approach , 2000, Comput. Chem..
[66] J. Vincent,et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure , 1996, Intensive Care Medicine.
[67] Athanasios V. Vasilakos,et al. Neural networks for computer-aided diagnosis in medicine: A review , 2016, Neurocomputing.
[68] Alfredo Vellido,et al. Sepsis mortality prediction with the Quotient Basis Kernel , 2014, Artif. Intell. Medicine.
[69] D R Westenskow,et al. Differential features for a neural network based anesthesia alarm system. , 1992, Biomedical sciences instrumentation.
[70] Ying Zhang,et al. Patient-specific learning in real time for adaptive monitoring in critical care , 2008, J. Biomed. Informatics.
[71] Paulo J. G. Lisboa,et al. Data Mining in Cancer Research [Application Notes] , 2010, IEEE Computational Intelligence Magazine.
[72] R. Ripoll,et al. On the intelligent management of sepsis in the intensive care unit , 2013 .
[73] Grace M. Mirsky,et al. Reduction of false cardiac arrhythmia alarms through the use of machine learning techniques , 2015, 2015 Computing in Cardiology Conference (CinC).
[74] A. Duhamel,et al. SES (septicemia expert system): knowledge validation from data analysis , 1988, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[75] Sabina Leonelli,et al. Data-Centric Biology: A Philosophical Study , 2016 .
[76] P Laursen. Event detection on patient monitoring data using Causal Probabilistic Networks. , 1994, Methods of information in medicine.
[77] James M. Blum,et al. Temporal Features and Kernel Methods for Predicting Sepsis in Postoperative Patients , 2010 .
[78] Alfredo Vellido,et al. On the use of decision trees for ICU outcome prediction in sepsis patients treated with statins , 2011, 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).
[79] Byunghan Lee,et al. Deep learning in bioinformatics , 2016, Briefings Bioinform..
[80] Nigel H Lovell,et al. Non-invasive classification of severe sepsis and systemic inflammatory response syndrome using a nonlinear support vector machine: a preliminary study , 2010, Physiological measurement.
[81] R. Bellomo,et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). , 2016, JAMA.
[82] M. Imhoff,et al. Alarm Algorithms in Critical Care Monitoring , 2006, Anesthesia and analgesia.
[83] R. Dybowski,et al. Prediction of outcome in critically ill patients using artificial neural network synthesised by genetic algorithm , 1996, The Lancet.
[84] Sabine Van Huffel,et al. Pattern discovery in critical alarms originating from neonates under intensive care , 2016, Physiological measurement.
[85] Katharina Morik,et al. Development of Decision Support Algorithms for Intensive Care Medicine: A New Approach Combining Time Series Analysis and a Knowledge Base System with Learning and Revision Capabilities , 1999, KI.
[86] C. Hanson,et al. Artificial intelligence applications in the intensive care unit , 2001, Critical care medicine.
[87] Seth Flaxman,et al. EU regulations on algorithmic decision-making and a "right to explanation" , 2016, ArXiv.
[88] Abraham Z. Snyder,et al. Prediction of brain maturity in infants using machine-learning algorithms , 2016, NeuroImage.
[89] W Michalowski,et al. A Task-based Support Architecture for Developing Point-of-care Clinical Decision Support Systems for the Emergency Department , 2012, Methods of Information in Medicine.
[90] Jason H T Bates,et al. Applying fuzzy logic to medical decision making in the intensive care unit. , 2003, American journal of respiratory and critical care medicine.
[91] Constantin F. Aliferis,et al. Causal Explorer: A Causal Probabilistic Network Learning Toolkit for Biomedical Discovery , 2003, METMBS.
[92] William Stafford Noble,et al. Machine learning applications in genetics and genomics , 2015, Nature Reviews Genetics.
[93] Shamim Nemati,et al. Identifying outcome-discriminative dynamics in multivariate physiological cohort time series , 2015 .
[94] Jürgen Paetz. Intersection based generalization rules for the analysis of symbolic septic shock patient data , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..
[95] Constantin F. Aliferis,et al. Medical decision support using machine learning for early detection of late-onset neonatal sepsis , 2014, J. Am. Medical Informatics Assoc..
[96] L. Citi,et al. PhysioNet 2012 Challenge: Predicting mortality of ICU patients using a cascaded SVM-GLM paradigm , 2012, 2012 Computing in Cardiology.
[97] L. Hayden,et al. Ten Commandments for Effective Clinical Decision Support: Making the Practice of Evidence-based Medicine a Reality , 2011 .