Published on in Vol 3, No 1 (2015): Jan-Mar

From Data to Optimal Decision Making: A Data-Driven, Probabilistic Machine Learning Approach to Decision Support for Patients With Sepsis

From Data to Optimal Decision Making: A Data-Driven, Probabilistic Machine Learning Approach to Decision Support for Patients With Sepsis

From Data to Optimal Decision Making: A Data-Driven, Probabilistic Machine Learning Approach to Decision Support for Patients With Sepsis

Journals

  1. Perner A, Gordon A, Angus D, Lamontagne F, Machado F, Russell J, Timsit J, Marshall J, Myburgh J, Shankar-Hari M, Singer M. The intensive care medicine research agenda on septic shock. Intensive Care Medicine 2017;43(9):1294 View
  2. Kim M, Tagkopoulos I. Data integration and predictive modeling methods for multi-omics datasets. Molecular Omics 2018;14(1):8 View
  3. Horng S, Sontag D, Halpern Y, Jernite Y, Shapiro N, Nathanson L, Groza T. Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning. PLOS ONE 2017;12(4):e0174708 View
  4. Taneja I, Reddy B, Damhorst G, Dave Zhao S, Hassan U, Price Z, Jensen T, Ghonge T, Patel M, Wachspress S, Winter J, Rappleye M, Smith G, Healey R, Ajmal M, Khan M, Patel J, Rawal H, Sarwar R, Soni S, Anwaruddin S, Davis B, Kumar J, White K, Bashir R, Zhu R. Combining Biomarkers with EMR Data to Identify Patients in Different Phases of Sepsis. Scientific Reports 2017;7(1) View
  5. Horiguchi H, Loftus T, Hawkins R, Raymond S, Stortz J, Hollen M, Weiss B, Miller E, Bihorac A, Larson S, Mohr A, Brakenridge S, Tsujimoto H, Ueno H, Moore F, Moldawer L, Efron P. Innate Immunity in the Persistent Inflammation, Immunosuppression, and Catabolism Syndrome and Its Implications for Therapy. Frontiers in Immunology 2018;9 View
  6. Ocampo-Quintero N, Vidal-Cortés P, del Río Carbajo L, Fdez-Riverola F, Reboiro-Jato M, Glez-Peña D. Enhancing sepsis management through machine learning techniques: A review. Medicina Intensiva 2022;46(3):140 View
  7. Nibbelink C, Young J, Carrington J, Brewer B. Informatics Solutions for Application of Decision-Making Skills. Critical Care Nursing Clinics of North America 2018;30(2):237 View
  8. KENNEDY D, P. PHILBIN S. The imperative need to develop guidelines to manage human versus machine intelligence. Frontiers of Engineering Management 2018;0(0):0 View
  9. Belard A, Buchman T, Dente C, Potter B, Kirk A, Elster E. The Uniformed Services University’s Surgical Critical Care Initiative (SC2i): Bringing Precision Medicine to the Critically Ill. Military Medicine 2018;183(suppl_1):487 View
  10. Belard A, Buchman T, Forsberg J, Potter B, Dente C, Kirk A, Elster E. Precision diagnosis: a view of the clinical decision support systems (CDSS) landscape through the lens of critical care. Journal of Clinical Monitoring and Computing 2017;31(2):261 View
  11. Rittmann B, Stevens M. Clinical Decision Support Systems and Their Role in Antibiotic Stewardship: a Systematic Review. Current Infectious Disease Reports 2019;21(8) View
  12. Ruminski C, Clark M, Lake D, Kitzmiller R, Keim-Malpass J, Robertson M, Simons T, Moorman J, Calland J. Impact of predictive analytics based on continuous cardiorespiratory monitoring in a surgical and trauma intensive care unit. Journal of Clinical Monitoring and Computing 2019;33(4):703 View
  13. Harrison A, Herasevich V, Gajic O. Automated Sepsis Detection, Alert, and Clinical Decision Support. Critical Care Medicine 2015;43(8):1776 View
  14. Celi L, Marshall J, Lai Y, Stone D. Disrupting Electronic Health Records Systems: The Next Generation. JMIR Medical Informatics 2015;3(4):e34 View
  15. Sebat C, Sinigayan V, Albertson T. Hospital Rapid Response Systems. Hospital Medicine Clinics 2017;6(4):480 View
  16. Wellner B, Grand J, Canzone E, Coarr M, Brady P, Simmons J, Kirkendall E, Dean N, Kleinman M, Sylvester P. Predicting Unplanned Transfers to the Intensive Care Unit: A Machine Learning Approach Leveraging Diverse Clinical Elements. JMIR Medical Informatics 2017;5(4):e45 View
  17. Peiffer-Smadja N, Rawson T, Ahmad R, Buchard A, Georgiou P, Lescure F, Birgand G, Holmes A. Machine learning for clinical decision support in infectious diseases: a narrative review of current applications. Clinical Microbiology and Infection 2020;26(5):584 View
  18. Khazaei H, McGregor C, Eklund J, El-Khatib K. Real-Time and Retrospective Health-Analytics-as-a-Service: A Novel Framework. JMIR Medical Informatics 2015;3(4):e36 View
  19. Beeksma M, Verberne S, van den Bosch A, Das E, Hendrickx I, Groenewoud S. Predicting life expectancy with a long short-term memory recurrent neural network using electronic medical records. BMC Medical Informatics and Decision Making 2019;19(1) View
  20. Berger J, Valera E, Jankelow A, Garcia C, Akhand M, Heredia J, Ghonge T, Liu C, Font-Bartumeus V, Oshana G, Tiao J, Bashir R. Simultaneous electrical detection of IL-6 and PCT using a microfluidic biochip platform. Biomedical Microdevices 2020;22(2) View
  21. Bradley R, Tagkopoulos I, Kim M, Kokkinos Y, Panagiotakos T, Kennedy J, De Meyer G, Watson P, Elliott J. Predicting early risk of chronic kidney disease in cats using routine clinical laboratory tests and machine learning. Journal of Veterinary Internal Medicine 2019;33(6):2644 View
  22. Sakib N, Ahamed S, Khan R, Griffin P, Haque M. Unpacking Prevalence and Dichotomy in Quick Sequential Organ Failure Assessment and Systemic Inflammatory Response Syndrome Parameters: Observational Data–Driven Approach Backed by Sepsis Pathophysiology. JMIR Medical Informatics 2020;8(12):e18352 View
  23. Schwartz J, Moy A, Rossetti S, Elhadad N, Cato K. Clinician involvement in research on machine learning–based predictive clinical decision support for the hospital setting: A scoping review. Journal of the American Medical Informatics Association 2021;28(3):653 View
  24. Chicco D, Oneto L. Data analytics and clinical feature ranking of medical records of patients with sepsis. BioData Mining 2021;14(1) View
  25. Fenner B, Darden D, Kelly L, Rincon J, Brakenridge S, Larson S, Moore F, Efron P, Moldawer L. Immunological Endotyping of Chronic Critical Illness After Severe Sepsis. Frontiers in Medicine 2021;7 View
  26. Anahtar M, Yang J, Kanjilal S, McAdam A. Applications of Machine Learning to the Problem of Antimicrobial Resistance: an Emerging Model for Translational Research. Journal of Clinical Microbiology 2021;59(7) View
  27. Giordano C, Brennan M, Mohamed B, Rashidi P, Modave F, Tighe P. Accessing Artificial Intelligence for Clinical Decision-Making. Frontiers in Digital Health 2021;3 View
  28. Park J, Hsu T, Hu J, Chen C, Hsu W, Lee M, Ho J, Lee C. Predicting Sepsis Mortality in a Population-Based National Database: Machine Learning Approach. Journal of Medical Internet Research 2022;24(4):e29982 View
  29. Bacchi S, Tan Y, Oakden‐Rayner L, Jannes J, Kleinig T, Koblar S. Machine learning in the prediction of medical inpatient length of stay. Internal Medicine Journal 2022;52(2):176 View
  30. Bishop J, Javed H, el-Bouri R, Zhu T, Taylor T, Peto T, Watkinson P, Eyre D, Clifton D, Chen T. Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge. PLOS ONE 2021;16(11):e0260476 View
  31. Zhang H. Analytical Solution to a Discrete-Time Model for Dynamic Learning and Decision Making. Management Science 2022;68(8):5924 View
  32. Ocampo-Quintero N, Vidal-Cortés P, del Río Carbajo L, Fdez-Riverola F, Reboiro-Jato M, Glez-Peña D. Enhancing sepsis management through machine learning techniques: A review. Medicina Intensiva (English Edition) 2022;46(3):140 View
  33. Rosenstrom E, Meshkinfam S, Ivy J, Goodarzi S, Capan M, Huddleston J, Romero-Brufau S. Optimizing the First Response to Sepsis: An Electronic Health Record-Based Markov Decision Process Model. Decision Analysis 2022;19(4):265 View
  34. Liang D, Deng H, Liu Y. The treatment of sepsis: an episodic memory-assisted deep reinforcement learning approach. Applied Intelligence 2023;53(9):11034 View
  35. Jazayeri A, Capan M, Ivy J, Arnold R, Yang C. Proximity of Cellular and Physiological Response Failures in Sepsis. IEEE Journal of Biomedical and Health Informatics 2021;25(11):4089 View
  36. Kokkinos Y, Morrison J, Bradley R, Panagiotakos T, Ogeer J, Chew D, O’Flynn C, De Meyer G, Watson P, Tagkopoulos I. An early prediction model for canine chronic kidney disease based on routine clinical laboratory tests. Scientific Reports 2022;12(1) View
  37. Parra-Rodriguez L, Guillamet M. Antibiotic Decision-Making in the ICU. Seminars in Respiratory and Critical Care Medicine 2022;43(01):141 View
  38. Ali T, Ahmed S, Aslam M. Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation. Antibiotics 2023;12(3):523 View
  39. Liu R, Hunold K, Caterino J, Zhang P. Estimating treatment effects for time-to-treatment antibiotic stewardship in sepsis. Nature Machine Intelligence 2023;5(4):421 View
  40. Machado J, Rodrigues C, Sousa R, Gomes L. Drug–drug interaction extraction‐based system: An natural language processing approach. Expert Systems 2023 View
  41. Barrett C, Suzuki Y, Hussein S, Garg L, Tumolo A, Sandhu A, West J, Zipse M, Aleong R, Varosy P, Tzou W, Banaei‐Kashani F, Rosenberg M. Evaluation of Quantitative Decision‐Making for Rhythm Management of Atrial Fibrillation Using Tabular Q‐Learning. Journal of the American Heart Association 2023;12(9) View
  42. Otten M, Jagesar A, Dam T, Biesheuvel L, den Hengst F, Ziesemer K, Thoral P, de Grooth H, Girbes A, François-Lavet V, Hoogendoorn M, Elbers P. Does Reinforcement Learning Improve Outcomes for Critically Ill Patients? A Systematic Review and Level-of-Readiness Assessment. Critical Care Medicine 2024;52(2):e79 View
  43. Hempel L, Sadeghi S, Kirsten T. Prediction of Intensive Care Unit Length of Stay in the MIMIC-IV Dataset. Applied Sciences 2023;13(12):6930 View
  44. Zhang Y, Xu W, Yang P, Zhang A. Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis. BMC Medical Informatics and Decision Making 2023;23(1) View

Books/Policy Documents

  1. Sethi T. Guide to Big Data Applications. View
  2. Utomo C, Kurniawati H, Li X, Pokharel S. Advanced Data Mining and Applications. View
  3. Cox L, Popken D, Sun R. Causal Analytics for Applied Risk Analysis. View
  4. Tegenaw G, Amenu D, Ketema G, Verbeke F, Cornelis J, Jansen B. Wireless Mobile Communication and Healthcare. View
  5. Wang R, Liu J, Chen Z, Gong M, Li C, Guo W. Artificial Intelligence in Medicine. View