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Results: 1 to 20 of 106

Similar articles for PubMed (Select 19046450)

1.

An artificial intelligence tool to predict fluid requirement in the intensive care unit: a proof-of-concept study.

Celi LA, Hinske LC, Alterovitz G, Szolovits P.

Crit Care. 2008;12(6):R151. doi: 10.1186/cc7140. Epub 2008 Dec 1.

2.

Trauma intensive care unit survival: how good is an educated guess?

Goettler CE, Waibel BH, Goodwin J, Watkins F, Toschlog EA, Sagraves SG, Schenarts PJ, Bard MR, Newell MA, Rotondo MF.

J Trauma. 2010 Jun;68(6):1279-87; discussion 1287-8. doi: 10.1097/TA.0b013e3181de3b99.

PMID:
20539170
3.
4.

Multiparameter Intelligent Monitoring in Intensive Care II: a public-access intensive care unit database.

Saeed M, Villarroel M, Reisner AT, Clifford G, Lehman LW, Moody G, Heldt T, Kyaw TH, Moody B, Mark RG.

Crit Care Med. 2011 May;39(5):952-60. doi: 10.1097/CCM.0b013e31820a92c6.

5.

Computerized prediction of intensive care unit discharge after cardiac surgery: development and validation of a Gaussian processes model.

Meyfroidt G, Güiza F, Cottem D, De Becker W, Van Loon K, Aerts JM, Berckmans D, Ramon J, Bruynooghe M, Van den Berghe G.

BMC Med Inform Decis Mak. 2011 Oct 25;11:64. doi: 10.1186/1472-6947-11-64.

6.

Computerized decision support system improves fluid resuscitation following severe burns: an original study.

Salinas J, Chung KK, Mann EA, Cancio LC, Kramer GC, Serio-Melvin ML, Renz EM, Wade CE, Wolf SE.

Crit Care Med. 2011 Sep;39(9):2031-8. doi: 10.1097/CCM.0b013e31821cb790.

PMID:
21532472
7.

Hypernatremia in the intensive care unit: an indicator of quality of care?

Polderman KH, Schreuder WO, Strack van Schijndel RJ, Thijs LG.

Crit Care Med. 1999 Jun;27(6):1105-8.

PMID:
10397213
8.

A model for increasing patient safety in the intensive care unit: increasing the implementation rates of proven safety measures.

Krimsky WS, Mroz IB, McIlwaine JK, Surgenor SD, Christian D, Corwin HL, Houston D, Robison C, Malayaman N.

Qual Saf Health Care. 2009 Feb;18(1):74-80. doi: 10.1136/qshc.2007.024844.

PMID:
19204137
9.

Microalbuminuria in the intensive care unit: Clinical correlates and association with outcomes in 431 patients.

Gosling P, Czyz J, Nightingale P, Manji M.

Crit Care Med. 2006 Aug;34(8):2158-66.

PMID:
16775565
10.

Assessing contemporary intensive care unit outcome: an updated Mortality Probability Admission Model (MPM0-III).

Higgins TL, Teres D, Copes WS, Nathanson BH, Stark M, Kramer AA.

Crit Care Med. 2007 Mar;35(3):827-35.

PMID:
17255863
11.

Reducing unnecessary lab testing in the ICU with artificial intelligence.

Cismondi F, Celi LA, Fialho AS, Vieira SM, Reti SR, Sousa JM, Finkelstein SN.

Int J Med Inform. 2013 May;82(5):345-58. doi: 10.1016/j.ijmedinf.2012.11.017. Epub 2012 Dec 28.

PMID:
23273628
12.

Neurologic intensive care resource use after brain tumor surgery: an analysis of indications and alternative strategies.

Ziai WC, Varelas PN, Zeger SL, Mirski MA, Ulatowski JA.

Crit Care Med. 2003 Dec;31(12):2782-7.

PMID:
14668615
13.

Intensive care telemedicine: evaluating a model for proactive remote monitoring and intervention in the critical care setting.

Groves RH Jr, Holcomb BW Jr, Smith ML.

Stud Health Technol Inform. 2008;131:131-46.

PMID:
18305328
14.

Hydroxyethyl starch for fluid resuscitation in critically ill patients.

Bagshaw SM, Chawla LS.

Can J Anaesth. 2013 Jul;60(7):709-13. doi: 10.1007/s12630-013-9936-4. Epub 2013 Apr 20.

PMID:
23604905
15.

[Blood glucose levels in the first 24 hours of admission is not a risk factor for mortality in critical care patients].

Blesa Malpica AL, Cubells Romeral M, Morales Sorribas E, Tejero Redondo A, Martínez Sagasti F, Martín Benítez JC, Garitacelaya Gorrochategui M, Ortuño Anderiz F.

Nutr Hosp. 2011 May-Jun;26(3):622-35. doi: 10.1590/S0212-16112011000300028. Spanish.

16.

Daily prognostic estimates for critically ill adults in intensive care units: results from a prospective, multicenter, inception cohort analysis.

Wagner DP, Knaus WA, Harrell FE, Zimmerman JE, Watts C.

Crit Care Med. 1994 Sep;22(9):1359-72.

PMID:
8062557
18.

N-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit.

Marafino BJ, Davies JM, Bardach NS, Dean ML, Dudley RA.

J Am Med Inform Assoc. 2014 Sep-Oct;21(5):871-5. doi: 10.1136/amiajnl-2014-002694. Epub 2014 Apr 30.

PMID:
24786209
19.

A Clinical Database-Driven Approach to Decision Support: Predicting Mortality Among Patients with Acute Kidney Injury.

Celi LA, Tang RJ, Villarroel MC, Davidzon GA, Lester WT, Chueh HC.

J Healthc Eng. 2011 Mar;2(1):97-110. Epub 2011 Apr 12.

20.

Sodium administration in critically ill patients in Australia and New Zealand: a multicentre point prevalence study.

Bihari S, Peake SL, Seppelt I, Williams P, Bersten A; George Institute for Global Health; Australian and New Zealand Intensive Care Society Clinical Trials Group.

Crit Care Resusc. 2013 Dec;15(4):294-300.

PMID:
24289511
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