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

JMIR Medical Informatics

Volumn 3, Issue 1, 2015, Pages

  Tsoukalas, Athanasios ^a   Albertson, Timothy ^b,c   Tagkopoulos, Ilias ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c VETERANS AFFAIRS MEDICAL CENTER   (United States)

Author keywords

Cdss; Clinical decision support tool; Partially observable markov decision processes; PoMDp; Probabilistic modeling; Sepsis

Indexed keywords

EID: 85008247993     PISSN: None     EISSN: 22919694     Source Type: Journal    
DOI: 10.2196/medinform.3445     Document Type: Article

References (40)

1. Wood KA, Angus DC. Pharmacoeconomic implications of new therapies in sepsis. Pharmacoeconomics 2004;22(14):895-906. [Medline: 15362927].
2. Hall MJ, Williams SN, DeFrances CJ, Golosinskiy A. Inpatient Care for Septicemia or Sepsis: A Challenge for Patients and Hospitals. In: NCHS Data Brief. Hyattsville, MD: National Center for Health Statistics; 2011.
3. Sepsis facts. Sepsis facts. URL: Http://world-sepsis-day.org/../?MET=HOME&vLANGUAGE=EN [accessed 2015-02-09] [WebCite Cache ID 6WE7B0X2g].
4. Gunter TD, Terry NP. The emergence of national electronic health record architectures in the united states and australia: Models, costs, and questions. Journal of Medical Internet Research 2005;7(1).
5. Fonkych K, Taylor R. The state and pattern of health information technology adoption. Rand Corporation 2005;409.
6. Kellermann AL, Jones SS. What it will take to achieve the as-yet-unfulfilled promises of health information technology. Health Aff (Millwood) 2013 Jan;32(1):63-68. [doi: 10.1377/hlthaff.2012.0693] [Medline: 23297272].
7. Ouali A, Ramdane-Cherif Z, Ramdane-Cherif A, Levy N, Krebs M. Agent paradigm in clinical large-scale data mining environment. 2003 Presented at: 2nd IEEE International Conference on Cognitive Informatics (ICCI); August 18-20, 2003; London, UK p. 143-150.
8. Catley C, Frize M. A prototype XML-based implementation of an integrated intelligent neonatal intensive care unit. 2003 Aug 18 Presented at: 4th International IEEE Conference on Information Technology Applications in Biomedicine; 2003; London, UK p. 322-325.
9. Balter J, Labarre-Vila A, Ziébelin D, Garbay C. A knowledge-driven agent-centred framework for data mining in EMG. C R Biol 2002 Apr;325(4):375-382. [Medline: 12161917].
10. Clermont GC, Angus DC, DiRusso SM, Griffin M, Linde-Zwirble WT. Predicting hospital mortality for patients in the intensive care unit: A comparison of artificial neural networks with logistic regression models. Crit Care Med 2001 Feb;29(2):291-296. [Medline: 11246308].
11. Foster D, McGregor C, El-Masri S. A survey of agent-based intelligent decision support systems to support clinical management and research. 2005 Jul 25 Presented at: Fourth International Joint Conference on Autonomous Agents and Multiagent Systems; 2005; Ultrecht, Netherlands p. 16-34.
12. Wears RL, Berg M. Computer technology and clinical work: Still waiting for Godot. JAMA 2005 Mar 9;293(10):1261-1263. [doi: 10.1001/jama.293.10.1261] [Medline: 15755949].
13. Bellman RE. A markovian decision process. In: No. P-1066. SANTA MONICA CA: RAND CORP; 1957.
14. Kaelbling LP, Littman ML, Cassandra AR. Planning and acting in partially observable stochastic domains. Artificial intelligence 1998;101(1):99-134.
15. Argall BD, Chernova S, Veloso M, Browning B. A survey of robot learning from demonstration. Robotics and Autonomous Systems 2009;57(5):469-483.
16. Howard RA. Dynamic programming and markov processes. In: Technology Press. Boston, MA: Massachusetts Institute of Technology; 1960.
17. Watkins CJ, Dayan P. Q-learning. Mach Learning 1992;8(3-4):279-292.
18. Alagoz O, Maillart LM, Schaefer AJ, Roberts MS. The optimal timing of living-donor liver transplantation. Management Science 2004;50(10):1420-1430.
19. Alagoz O, Maillart LM, Schaefer AJ, Roberts MS. Determining the acceptance of cadaveric livers using an implicit model of the waiting list. Oper Res 2007;55(1):24-36.
20. Alagoz O, Maillart LM, Schaefer AJ, Roberts MS. Choosing among living-donor and cadaveric livers. Management Science 2007;53(11):1702-1715.
21. Shechter SM, Bailey MD, Schaefer AJ, Roberts MS. The optimal time to initiate HIV therapy under ordered health states. Oper Res 2008;56(1):20-33.
22. Maillart LM, Ivy JS, Ransom S, Diehl K. Assessing dynamic breast cancer screening policies. Oper Res 2008;56(6):1411-1427.
23. Faissol DM, Griffin PM. Timing of testing and treatment of hepatitis C and other diseases. In: Proceedings. 2007 Presented at: INFORMS international meeting; July 2007; Puerto Rico p. 11.
24. Denton BT, Kurt M, Shah ND, Bryant SC, Smith SA. Optimizing the start time of statin therapy for patients with diabetes. Med Decis Making 2009;29(3):351-367. [doi: 10.1177/0272989X08329462] [Medline: 19429836].
25. Alagoz O, Hsu H, Schaefer AJ, Roberts MS. Markov decision processes: A tool for sequential decision making under uncertainty. Med Decis Making 2010;30(4):474-483 [FREE Full text] [doi: 10.1177/0272989X09353194] [Medline: 20044582.
26. Hauskrecht M, Fraser H. Modeling treatment of ischemic heart disease with partially observable Markov decision processes. In: Proc AMIA Symp. 1998 Presented at: Proc AMIA Symp; 1998 p. 538-542 URL: Http://europepmc.org/abstract/MED/9929277.
27. Hauskrecht M, Fraser H. Planning treatment of ischemic heart disease with partially observable Markov decision processes. Artif Intell Med 2000 Mar;18(3):221-244. [Medline: 10675716].
28. Ayer T, Alagoz O, Stout NK. A POMDP approach to personalize mammography screening decisions. Operations Research 2012 Jun 12;60(5):1019-1034.
29. Kreke JE. Modeling disease management decisions for patients with pneumonia-related sepsis. Pittsburgh, PA: University of Pittsburgh; 2007.
30. Kreke JE, Bailey MD, Schaefer AJ, Angus DC, Roberts MS. Modeling hospital discharge policies for patients with pneumonia-related sepsis. IIE Transactions 2008;40(9):853-860.
31. Bone RC, Balk RA, Cerra FB, Dellinger RP, Fein AM, Knaus WA, et al. Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. Crit Care Med 1992;20(6):864-874.
32. Spaan MTJ., Vlassis N.. Perseus: Randomized Point-based Value Iteration for POMDPs. Journal of Artificial Intelligence Research 2005 Jul 01;24(1):195-220 [FREE Full text].
33. Wang SL, Wu F, Wang BH. Prediction of severe sepsis using SVM model. Adv Exp Med Biol 2010;680:75-81. [doi: 10.1007/978-1-4419-5913-3-9] [Medline: 20865488].
34. Tang CH, Middleton PM, Savkin AV, Chan GS, Bishop S, Lovell NH. Non-invasive classification of severe sepsis and systemic inflammatory response syndrome using a nonlinear support vector machine: A preliminary study. Physiol Meas 2010 Jun;31(6):775-793. [doi: 10.1088/0967-3334/31/6/004] [Medline: 20453293].
35. Vapnik V. Statistical Learning Theory. New York: John Wiley & Sons; 1998.
36. Hsu CW, Lin CJ. A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 2002;13(2):415-425. [doi: 10.1109/72.991427] [Medline: 18244442].
37. Saeys Y, Inza I, Larranaga P. A review of feature selection techniques in bioinformatics. Bioinformatics 2007;23:A.
38. Jolliffe IT. Principle Component Analysis. New York: Springer; 2005.
39. Wang R, Tang K. Feature selection for maximizing the area under the ROC curve. 2009 Dec 06 Presented at: IEEE International Conference on Data Mining Workshops; 2009; Florida, USA p. 400-405.
40. Vincent JL, Moreno R, Takala J, Willatts S, De Mendoncą A, Bruining H, et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med 1996 Jul;22(7):707-710. [Medline: 8844239].