Publications

PEDAL publications

• Bennett TD, Spaeder MC, Matos RI, Watson RS, Typpo KV, Khemani RG, Crow S, Bennyworth BD, Thiagarajan RR, Dean JM, Markowitz BP. Existing data analysis in pediatric critical care research. Front Pediatr 2014;2:79.
• Typpo K, Watson RS, Bennett TD, Farris RWD, Spaeder MC, Petersen NJ. Outcomes in day 1 multiple organ dysfunction syndrome in the PICU. Pediatr Crit Care Med 2019;20:914-922.
• Dziorny AC, Heneghan JA, Bhat MA, Karavite DJ, Sanchez-Pinto LN, McArthur J, Muthu N; Pediatric Data Science and Analytics (PEDAL) Subgroup of the Pediatric Acute Lung Injury and Sepsis Investigators (PALISI) Network. Clinical Decision Support in the PICU: Implications for Design and Evaluation. Pediatr Crit Care Med 2022;23(8):e392-e396. 
  

PEDAL-adjacent publications

• Bennett TD, Dean JM, Keenan HT, McGlincy MH, Thomas AM, Cook LJ. Linked records of children with traumatic brain injury. Probabilistic linkage without use of protected health information. Methods Inf Med 2015;54:328-337.
  
• Sanchez-Pinto LN, Khemani RG. Development of a prediction model of early acute kidney injury in critically ill children using electronic health record data. Pediatr Crit Care Med 2016;17:508-515.
  
• Sanchez-Pinto LN, Luo Y, Churpek MM. Big data and data science in critical care. Chest 2018;154:1239-1248.
  
• Sanchez-Pinto LN, Venable LR, Fahrenbach J, Churpek MM. Comparison of variable selection methods for clinical predictive modeling. Int J Med Inform 2018; 116:10-17.
  
• Kamaleswaran, R. et al. Applying artificial intelligence to identify physiomarkers predicting severe sepsis in the PICU. Pediatr Crit Care Med 2018;19:e495–e503.
  
• Williams JB, Ghosh D, Wetzel RC. Applying Machine Learning to Pediatric Critical Care Data. Pediatr Crit Care Med. 2018 Jul;19(7):599-608.
  
• Zimmerman LP, Reyfman PA, Smith ADR, Zeng Z, Kho A, Sanchez-Pinto LN, Luo Y. Early prediction of acute kidney injury following ICU admission using a multivariate panel of physiologic measurements. BMC Med Inform Dec Mak 2019;19(Suppl 1):16.
  
• Bennett TD, Callaghan TJ, Feinstein JA, Ghosh D, Lakhani SA, Spaeder MC, Szefler SJ, Kahn MG. Data science for child health. J Pediatr 2019;208:12-22.
  
• Mayampurath A, Sanchez-Pinto LN, Carey KA, Venable LR, Churpek M. Combining patient visual timelines with deep learning to predict mortality. PLoS One 2019;14:e0220640.
  
• Spaeder MC, Moorman JR, Tran CA, Keim-Malpass J, Zschaebitz JV, Lake DE, Clark MT. Predictive analytics in the pediatric intensive care unit for early identification of sepsis: capturing the context of age. Pediatr Res 2019;86:655-661.
  
• Eickelberg G, Sanchez-Pinto LN, Luo Y. Predictive modeling of bacterial infection and antibiotic therapy needs in critically ill adults. J Biomed Inform 2020;109:103540.
  
• Le S, Hoffman J, Barton C, Fitzgerald JC, Allen A, Pellegrini E, Calvert J, Das R. Pediatric Severe Sepsis Prediction Using Machine Learning. Front Pediatr. 2019 Oct 11;7:413. doi: 10.3389/fped.2019.00413.
  
• Dziorny AC, Lindell RB, Bennett TD, Bailey LC. Joining datasets without identifiers: probabilistic linkage of Virtual Pediatric Systems and PEDSnet. Pediatr Crit Care Med 2020;21:e628-e634.
  
• Mohammed A, Podila PSB, Davis RL, Ataga KI, Hankins JS, Kamaleswaran R. Using Machine Learning to Predict Early Onset Acute Organ Failure in Critically Ill Intensive Care Unit Patients With Sickle Cell Disease: Retrospective Study. J Med Internet Res 2020 May 13;22(5):e14693.
  
• Sanchez-Pinto LN, Stroup EK, Pendergrast T, Pinto N, Luo Y. Derivation and Validation of Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children. JAMA Network Open. 2020 Aug 3;3(8):e209271-.
  
• Winter MC, Day TE, Ledbetter DR, Aczon MD, Newth SJL, Wetzel RC, Ross PA. Machine learning to predict cardiac death within 1 hour after terminal extubation. Pediatr Crit Care Med 2021;22:161-171.
  
• Badke CM, Marsillio LE, Carroll MS, Weese-Mayer DE, Sanchez-Pinto LN. Development of a heart variability risk score to predict organ dysfunction and death in critically ill children. Pediatr Crit Care Med 2021; 22:e437-e447.
  
• Aczon MD, Ledbetter DR, Laksana E, Ho LV, Wetzel RC. Continuous prediction of mortality in the PICU: a recurrent neural network model in a single-center dataset. Pediatr Crit Care Med 2021;22:519-529.
  
• Sanchez-Pinto LN, Bembea MM, Farris RW, Hartman ME, Odetola FO, Spaeder MC, Watson RS, Zimmerman JJ, Bennett TD; Pediatric Organ Dysfunction Information Update Mandate (PODIUM) Collaborative. Patterns of Organ Dysfunction in Critically Ill Children Based on PODIUM Criteria. Pediatrics 2022; 149(1 Suppl 1):S103-S110.
  
• Badke CM, Carroll MS, Weese-Mayer DE, Sanchez-Pinto LN. Association Between Heart Rate Variability and Inflammatory Biomarkers in Critically Ill Children. Pediatr Crit Care Med 2022 Mar 16 Epub ahead of print.
  
• Mai MV, Muthu N, Carroll B, Costello A, West DC, Dziorny AC. Measuring Training Disruptions Using an Informatics Based Tool. Acad Pediatr 2022; S1876-2859.
• Walker SB, Badke CM, Carroll MS, Honegger KS, Fawcett A, Weese-Mayer DE, Sanchez-Pinto LN. Novel approaches to capturing and using continuous cardiorespiratory physiological data in hospitalized children. Pediatr Res 2022 Nov 3. doi: 10.1038/s41390-022-02359-3. Epub ahead of print. PMID: 36329224.
• Spaeder MC, Moorman J.R, Moorman LP, Adu-Darko MA, Keim-Malpass J, Lake DE and Clark MT (2022) Signatures of illness in children requiring unplanned intubation in the pediatric intensive care unit: A retrospective cohort machine-learning study. Front. Pediatr 10:1016269. doi: 10.3389/fped.2022.1016269.

Selected data science publications

• Moorman, J. R. et al. Predictive monitoring for early detection of subacute potentially catastrophic illnesses in critical care. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011, 5515–5518.
• Bright TJ, Wong A, Dhurjati R, et al. Effect of clinical decisionsupport systems: a systematic review. Ann Intern Med. 2012;157(1): 29-43
• Celi LA, Mark RG, Stone DJ, Montgomery RA. “Big data” in the intensive care unit. Closing the data loop. Am J Respir Critic Care Med. 2013;187(11):1157-1160.
• Provost F, Fawcett T. Data science and its relationship to big data and data-driven decision making. Big Data. 2013;1(1):51-59.
• Fairchild, K. D. Predictive monitoring for early detection of sepsis in neonatal ICU patients. Curr. Opin. Pediatr. 25, 172–179 (2013).
• Murdoch TB, Detsky AS. The inevitable application of big data to health care. JAMA. 2013;309(13):1351-1352.
• Badawi O, Brennan T, Celi LA, et al. Making big data useful for health care: a summary of the inaugural mit critical data conference. JMIR Med Inform. 2014;2(2):e22.
• Calfee CS, Delucchi K, Parsons PE, et al. Subphenotypes in acute respiratory distress syndrome: latent class analysis of data from two randomised controlled trials. Lancet Respir Med. 2014;2(8):611-620.
• Iwashyna TJ, Liu V. What’s so different about big data?. A primer for clinicians trained to think epidemiologically. Ann Am Thorac Soc. 2014;11(7):1130-1135.
• Deo RC. Machine learning in medicine. Circulation. 2015;132(20): 1920-1930.
• Ghassemi M, Celi LA, Stone DJ. State of the art review: the data revolution in critical care. Crit Care. 2015;19:118.
• Buchman TG, Billiar TR, Elster E, et al. Precision medicine for critical illness and injury. Crit Care Med. 2016;44(9):1635-1638.
• Moss, T. J. et al. Signatures of subacute potentially catastrophic illness in the ICU: model development and validation. Crit. Care Med 2016;44:1639–1648.
• Rusin, CG. et al. Prediction of imminent, severe deterioration of children with parallel circulations using real-time processing of physiologic data. J Thorac Cardiovasc 2016;152:171–177.
• Johnson AE, Ghassemi MM, Nemati S, Niehaus KE, Clifton DA, Clifford GD. Machine learning and decision support in critical care. Proceedings IEEE. 2016;104(2):444-466.
• Churpek MM, Yuen TC, Winslow C, Meltzer DO, Kattan MW, Edelson DP. Multicenter comparison of machine learning methods and conventional regression for predicting clinical deterioration on the wards. Crit Care Med. 2016;44(2):368-374.
• Suresh S. Big Data and Predictive Analytics: Applications in the Care of Children. Pediatr Clin North Am. 2016 Apr;63(2):357-66.
• Vranas KC, Jopling JK, Sweeney TE, et al. Identifying distinct subgroups of ICU patients: a machine learning approach. Crit Care Med. 2017;45(10):1607-1615.
• Nemati S, Holder A, Razmi F, Stanley MD, Clifford GD, Buchman TG. An interpretable machine learning model for accurate prediction of sepsis in the ICU. Critical Care Medicine. 2018;46(4): 547-553.
• Keim-Malpass, J. et al. Advancing continuous predictive analytics monitoring: moving from implementation to clinical action in a learning health system. Crit Care Nurs Clin N. Am. 2018;30:273–287.
• Seymour CW, Kennedy JN, Wang S, et al. Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis. JAMA. 2019;321(20):2003–2017. doi:10.1001/jama.2019.5791
• Leisman DE, Harhay MO, Lederer DJ, et al. Development and Reporting of Prediction Models: Guidance for Authors From Editors of Respiratory, Sleep, and Critical Care Journals. Crit Care Med. 2020 May;48(5):623-633.
• Sinha P, Churpek MM, Calfee CS. Machine Learning Classifier Models Can Identify Acute Respiratory Distress Syndrome Phenotypes Using Readily Available Clinical Data. Am J Respir Crit Care Med. 2020 Oct 1;202(7):996-1004.
• Sinha P, Calfee CS, Delucchi KL. Practitioner’s Guide to Latent Class Analysis: Methodological Considerations and Common Pitfalls. Crit Care Med. 2020 Nov 9. Epub ahead of print.
• Shah N, Arshad A, Mazer MB, Carroll CL, Shein SL, Remy KE. The use of machine learning and artificial intelligence within pediatric critical care. Pediatr Res 2022 Nov 14:1–8. doi: 10.1038/s41390-022-02380-6.