An intelligent Decision Support System for the treatment of patients receiving ventricular assist device (VAD) support

The scope of this paper is to present the Specialist's Decision Support System (SDSS), part of the overall Decision Support Framework that is developed under the SensorART platform. The SensorART platform focuses on the management and remote treatment of patients suffering from end-stage heart failure. The SDSS assists specialists on designing the best treatment plan for their patients before and after VAD implantation, analyzing patients' data, extracting new knowledge, and making informative decisions. It creates a hallmark in the field, supporting medical and VAD experts through the different phases of VAD therapy.

[1]  J. R. Fitzpatrick,et al.  Risk score derived from pre-operative data analysis predicts the need for biventricular mechanical circulatory support. , 2008, The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation.

[2]  D C Torney,et al.  Discovery of association rules in medical data , 2001, Medical informatics and the Internet in medicine.

[3]  M. Cruz-cunha,et al.  Handbook of Research on Developments in E-health and Telemedicine: Technological and Social Perspectives , 2009 .

[4]  M.H. Hassoun,et al.  Fundamentals of Artificial Neural Networks , 1996, Proceedings of the IEEE.

[5]  Chan-Yu Lin,et al.  Evaluation of outcome scoring systems for patients on extracorporeal membrane oxygenation. , 2007, The Annals of thoracic surgery.

[6]  Roland Hetzer,et al.  Heart failure reversal by ventricular unloading in patients with chronic cardiomyopathy: criteria for weaning from ventricular assist devices , 2010, European heart journal.

[7]  Wayne C Levy,et al.  Can the Seattle heart failure model be used to risk-stratify heart failure patients for potential left ventricular assist device therapy? , 2009, The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation.

[8]  A. Holland,et al.  Effects of exercise training on exercise capacity and quality of life in patients with a left ventricular assist device: a preliminary randomized controlled trial. , 2012, The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation.

[9]  K Araki,et al.  Sensorless controlling method for a continuous flow left ventricular assist device. , 2000, Artificial organs.

[10]  J.R. Boston,et al.  A rule-based controller based on suction detection for rotary blood pumps , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  Nigel H Lovell,et al.  Identification and classification of physiologically significant pumping states in an implantable rotary blood pump. , 2006, Artificial organs.

[12]  Karen Ulisney,et al.  INTERMACS profiles of advanced heart failure: the current picture. , 2009, The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation.

[13]  N. Oda,et al.  Which factors predict the recovery of natural heart function after insertion of a left ventricular assist system? , 2008, The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation.

[14]  Reza Langari,et al.  Fuzzy Control: Synthesis and Analysis , 2000 .

[15]  Jonathan W Haft,et al.  Model for End-Stage Liver Disease Score Predicts Left Ventricular Assist Device Operative Transfusion Requirements, Morbidity, and Mortality , 2010, Circulation.

[16]  Marek J Druzdzel,et al.  Development of a hybrid decision support model for optimal ventricular assist device weaning. , 2010, The Annals of thoracic surgery.

[17]  K Araki,et al.  Detection of total assist and sucking points based on the pulsatility of a continuous flow artificial heart: in vivo evaluation. , 1998, ASAIO journal.

[18]  Dimitrios I. Fotiadis,et al.  A treatment decision support system for patients receiving Ventricular Assist Device (VAD) therapy , 2012, Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics.

[19]  Glenn Shafer,et al.  Perspectives on the theory and practice of belief functions , 1990, Int. J. Approx. Reason..

[20]  S Takatani,et al.  Detection of suction and regurgitation of the implantable centrifugal pump based on the motor current waveform analysis and its application to optimization of pump flow. , 1999, Artificial organs.

[21]  L. A. Bonet,et al.  ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure 2012 , 2010, European journal of heart failure.

[22]  Mikhail Skliar,et al.  Modeling and Control of a Brushless DC Axial Flow Ventricular Assist Device , 2002, ASAIO journal.

[23]  Mark S. Slaughter,et al.  Outcomes of Left Ventricular Assist Device Implantation as Destination Therapy in the Post-REMATCH Era: Implications for Patient Selection , 2007, Circulation.

[24]  Nigel H. Lovell,et al.  Noninvasive Detection of Suction in an implantable Rotary Blood Pump Using Neural Networks , 2008, Int. J. Comput. Intell. Appl..

[25]  K. Pfeiffer,et al.  Future Development of Medical Informatics from the Viewpoint of Health Telematics , 2009, Methods of Information in Medicine.

[26]  S. Silver,et al.  Heart Failure , 1937, The New England journal of medicine.

[27]  Yorgos Goletsis,et al.  A Gaussian Mixture Model to detect suction events in rotary blood pumps , 2012, 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE).

[28]  Nigel H Lovell,et al.  Developments in control systems for rotary left ventricular assist devices for heart failure patients: a review , 2013, Physiological measurement.

[29]  Ulrich Güntzer,et al.  Algorithms for association rule mining — a general survey and comparison , 2000, SKDD.

[30]  F Antaki,et al.  Speed control system for implanted blood pump , 1997 .

[31]  Marwan A. Simaan,et al.  A discriminant-analysis-based suction detection system for rotary blood pumps , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[32]  Yoshifumi Naka,et al.  Revised screening scale to predict survival after insertion of a left ventricular assist device. , 2003, The Journal of thoracic and cardiovascular surgery.

[33]  Kiyotaka Fukamachi,et al.  Predictors of Severe Right Ventricular Failure After Implantable Left Ventricular Assist Device Insertion: Analysis of 245 Patients , 2002, Circulation.

[34]  Hiroshi Oyama,et al.  A technique for identifying three diagnostic findings using association analysis , 2006, Medical & Biological Engineering & Computing.

[35]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[36]  Hendrik Blockeel,et al.  Web mining research: a survey , 2000, SKDD.

[37]  G Bearnson,et al.  Motor feedback physiological control for a continuous flow ventricular assist device. , 1999, Artificial organs.

[38]  F. Pagani,et al.  The right ventricular failure risk score a pre-operative tool for assessing the risk of right ventricular failure in left ventricular assist device candidates. , 2008, Journal of the American College of Cardiology.

[39]  Nader Moazami,et al.  Right ventricular failure in patients with the HeartMate II continuous-flow left ventricular assist device: incidence, risk factors, and effect on outcomes. , 2010, The Journal of thoracic and cardiovascular surgery.

[40]  Heart failure monitoring with implantable defibrillators , 2012, Biomedizinische Technik. Biomedical engineering.

[41]  J. Schwartz,et al.  Development and prospective validation of a clinical index to predict survival in ambulatory patients referred for cardiac transplant evaluation. , 1996, Circulation.

[42]  Gorczynska Krystyna,et al.  A Hybrid (Hydro-numerical) Cardiovascular Model: Application to Investigate Continuous-flow Pump Assistance Effect , 2012 .

[43]  K Ohe,et al.  An ontology-based mediator of clinical information for decision support systems: a prototype of a clinical alert system for prescription. , 2008, Methods of information in medicine.

[44]  Marwan A. Simaan,et al.  Hierarchical control of heart-assist devices , 2003, IEEE Robotics Autom. Mag..

[45]  Douglas C. McConahy Application of Multiobjective Optimization to Determining an Optimal Left Ventricular Assist Device (LVAD) Pump speed , 2007 .

[46]  W. Frishman,et al.  Left Ventricular Assist Device and Drug Therapy for the Reversal of Heart Failure , 2007 .

[47]  William L Holman,et al.  INTERMACS: interval analysis of registry data. , 2009, Journal of the American College of Surgeons.

[48]  Marwan A. Simaan,et al.  Detection of ventricular suction in an implantable rotary blood pump using support vector machines , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[49]  Peter Urbach,et al.  Scientific Reasoning: The Bayesian Approach , 1989 .

[50]  J. Howie-Esquivel,et al.  Improving Heart Failure Symptom Recognition: A Diary Analysis , 2010, The Journal of cardiovascular nursing.

[51]  Magdi H Yacoub,et al.  Left ventricular assist device and drug therapy for the reversal of heart failure. , 2006, The New England journal of medicine.

[52]  Mikhail Skliar,et al.  Nonlinear controller for ventricular assist devices. , 2002, Artificial organs.

[53]  J. B. Brooke,et al.  SUS: A 'Quick and Dirty' Usability Scale , 1996 .

[54]  Robert L Kormos,et al.  Second INTERMACS annual report: more than 1,000 primary left ventricular assist device implants. , 2010, The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation.

[55]  James F. Antaki,et al.  Prognosis of Right Ventricular Failure in Patients With Left Ventricular Assist Device Based on Decision Tree With SMOTE , 2012, IEEE Transactions on Information Technology in Biomedicine.

[56]  Shao Hui Chen,et al.  BAROREFLEX-BASED PHYSIOLOGICAL CONTROL OF A LEFT VENTRICULAR ASSIST DEVICE , 2006 .

[57]  Ruijuan Hu Medical Data Mining Based on Association Rules , 2010, Comput. Inf. Sci..

[58]  J R Boston,et al.  Controller for an Axial Flow Blood Pump. , 1996, Artificial organs.

[59]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[60]  J M Fuqua,et al.  Progress in the Development of a Transcutaneously Powered Axial Flow Blood Pump Ventricular Assist System , 1997, ASAIO journal.

[61]  Carolyn Penstein Rosé,et al.  A Classification Approach for Risk Prognosis of Patients on Mechanical Ventricular Assistance , 2010, 2010 Ninth International Conference on Machine Learning and Applications.

[62]  Guruprasad A. Giridharan,et al.  Physiological control of blood pumps without implantable sensors , 2003, Proceedings of the 2003 American Control Conference, 2003..

[63]  Natarajan Sriraam,et al.  DATA MINING APPROACHES FOR KIDNEY DIALYSIS TREATMENT , 2006 .

[64]  M C Oz,et al.  Screening scale predicts patients successfully receiving long-term implantable left ventricular assist devices. , 1995, Circulation.

[65]  Nigel H Lovell,et al.  Classification of Physiologically Significant Pumping States in an Implantable Rotary Blood Pump: Patient Trial Results , 2007, ASAIO journal.

[66]  Magdi H. Yacoub,et al.  Reversal of Severe Heart Failure With a Continuous-Flow Left Ventricular Assist Device and Pharmacological Therapy: A Prospective Study , 2011, Circulation.