Published on 24.02.15 in Vol 3, No 1 (2015): Jan-Mar
Works citing "From Data to Optimal Decision Making: A Data-Driven, Probabilistic Machine Learning Approach to Decision Support for Patients With Sepsis"
According to Crossref, the following articles are citing this article (DOI 10.2196/medinform.3445):
(note that this is only a small subset of citations)
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Perner A, Gordon AC, Angus DC, Lamontagne F, Machado F, Russell JA, Timsit J, Marshall JC, Myburgh J, Shankar-Hari M, Singer M. The intensive care medicine research agenda on septic shock. Intensive Care Medicine 2017;43(9):1294
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Kim M, Tagkopoulos I. Data integration and predictive modeling methods for multi-omics datasets. Molecular Omics 2018;14(1):8
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Horng S, Sontag DA, Halpern Y, Jernite Y, Shapiro NI, Nathanson LA, Groza T. Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning. PLOS ONE 2017;12(4):e0174708
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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)
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Horiguchi H, Loftus TJ, Hawkins RB, Raymond SL, Stortz JA, Hollen MK, Weiss BP, Miller ES, Bihorac A, Larson SD, Mohr AM, Brakenridge SC, Tsujimoto H, Ueno H, Moore FA, Moldawer LL, Efron PA. Innate Immunity in the Persistent Inflammation, Immunosuppression, and Catabolism Syndrome and Its Implications for Therapy. Frontiers in Immunology 2018;9
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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
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Nibbelink CW, Young JR, Carrington JM, Brewer BB. Informatics Solutions for Application of Decision-Making Skills. Critical Care Nursing Clinics of North America 2018;30(2):237
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KENNEDY D, P. PHILBIN S. The imperative need to develop guidelines to manage human versus machine intelligence. Frontiers of Engineering Management 2018;0
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Belard A, Buchman T, Dente CJ, Potter BK, 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
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Belard A, Buchman T, Forsberg J, Potter BK, Dente CJ, 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
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Rittmann B, Stevens MP. Clinical Decision Support Systems and Their Role in Antibiotic Stewardship: a Systematic Review. Current Infectious Disease Reports 2019;21(8)
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Ruminski CM, Clark MT, Lake DE, Kitzmiller RR, Keim-Malpass J, Robertson MP, Simons TR, Moorman JR, Calland JF. 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
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Harrison AM, Herasevich V, Gajic O. Automated Sepsis Detection, Alert, and Clinical Decision Support. Critical Care Medicine 2015;43(8):1776
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Celi LA, Marshall JD, Lai Y, Stone DJ. Disrupting Electronic Health Records Systems: The Next Generation. JMIR Medical Informatics 2015;3(4):e34
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Sebat C, Sinigayan V, Albertson T. Hospital Rapid Response Systems. Hospital Medicine Clinics 2017;6(4):480
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Wellner B, Grand J, Canzone E, Coarr M, Brady PW, 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
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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
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Khazaei H, McGregor C, Eklund JM, El-Khatib K. Real-Time and Retrospective Health-Analytics-as-a-Service: A Novel Framework. JMIR Medical Informatics 2015;3(4):e36
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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)
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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)
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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
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Sakib N, Ahamed SI, Khan RA, Griffin PM, Haque MM. 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
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Schwartz JM, Moy AJ, Rossetti SC, Elhadad N, Cato KD. 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
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Chicco D, Oneto L. Data analytics and clinical feature ranking of medical records of patients with sepsis. BioData Mining 2021;14(1)
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Fenner BP, Darden DB, Kelly LS, Rincon J, Brakenridge SC, Larson SD, Moore FA, Efron PA, Moldawer LL. Immunological Endotyping of Chronic Critical Illness After Severe Sepsis. Frontiers in Medicine 2021;7
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Anahtar MN, Yang JH, Kanjilal S, McAdam AJ. Applications of Machine Learning to the Problem of Antimicrobial Resistance: an Emerging Model for Translational Research. Journal of Clinical Microbiology 2021;59(7)
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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
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Park JY, 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
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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
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Bishop JA, Javed HA, el-Bouri R, Zhu T, Taylor T, Peto T, Watkinson P, Eyre DW, Clifton DA, 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
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. Analytical Solution to a Discrete-Time Model for Dynamic Learning and Decision Making. Management Science 2022;68(8):5924
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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
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Rosenstrom E, Meshkinfam S, Ivy JS, Goodarzi SH, 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
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Liang D, Deng H, Liu Y. The treatment of sepsis: an episodic memory-assisted deep reinforcement learning approach. Applied Intelligence 2023;53(9):11034
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Jazayeri A, Capan M, Ivy J, Arnold R, Yang CC. Proximity of Cellular and Physiological Response Failures in Sepsis. IEEE Journal of Biomedical and Health Informatics 2021;25(11):4089
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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)
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Parra-Rodriguez L, Guillamet MCV. Antibiotic Decision-Making in the ICU. Seminars in Respiratory and Critical Care Medicine 2022;43(01):141
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Ali T, Ahmed S, Aslam M. Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation. Antibiotics 2023;12(3):523
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Liu R, Hunold KM, Caterino JM, Zhang P. Estimating treatment effects for time-to-treatment antibiotic stewardship in sepsis. Nature Machine Intelligence 2023;5(4):421
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Machado J, Rodrigues C, Sousa R, Gomes LM. Drug–drug interaction extraction‐based system: An natural language processing approach. Expert Systems 2023;
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Barrett CD, Suzuki Y, Hussein S, Garg L, Tumolo A, Sandhu A, West JJ, Zipse M, Aleong R, Varosy P, Tzou WS, Banaei‐Kashani F, Rosenberg MA. Evaluation of Quantitative Decision‐Making for Rhythm Management of Atrial Fibrillation Using Tabular Q‐Learning. Journal of the American Heart Association 2023;12(9)
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Otten M, Jagesar AR, Dam TA, Biesheuvel LA, den Hengst F, Ziesemer KA, Thoral PJ, de Grooth H, Girbes AR, François-Lavet V, Hoogendoorn M, Elbers PW. Does Reinforcement Learning Improve Outcomes for Critically Ill Patients? A Systematic Review and Level-of-Readiness Assessment. Critical Care Medicine 2024;52(2):e79
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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
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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)
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Tuli FA, Varghese A, Ande JRPK. Data-Driven Decision Making: A Framework for Integrating Workforce Analytics and Predictive HR Metrics in Digitalized Environments. Global Disclosure of Economics and Business 2018;7(2):109
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According to Crossref, the following books are citing this article (DOI 10.2196/medinform.3445):
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. Guide to Big Data Applications. 2018. Chapter 14:371
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Utomo CP, Kurniawati H, Li X, Pokharel S. Advanced Data Mining and Applications. 2019. Chapter 47:648
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Cox LA, Popken DA, Sun RX. Causal Analytics for Applied Risk Analysis. 2018. Chapter 1:3
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Tegenaw GS, Amenu D, Ketema G, Verbeke F, Cornelis J, Jansen B. Wireless Mobile Communication and Healthcare. 2022. Chapter 13:186
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Wang R, Liu J, Chen Z, Gong M, Li C, Guo W. Artificial Intelligence in Medicine. 2022. Chapter 31:321
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