Use of a Neural Network as a Predictive Instrument for Length of Stay in the Intensive Care Unit Following Cardiac Surgery

Tu, Jack V.; Guerriere, Michael

doi:10.1006/cbmr.1993.1015

Cited by 107 publications

(38 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Initial analyses selected an LOS value as an exceedence threshold 1323 [7,8,11], and others have used higher thresholds, such as 20 or 30 days [12,13,14,15,16]. Among homogeneous populations with low median ICU stays, for example, postcoronary artery bypass surgery, thresholds for prolonged stay as low as 2 days have been employed [17,18]. In the present study a threshold of 10 days was chosen based on visual analysis of the ªtailº of the distribution (Fig.…”

Section: Discussionmentioning

confidence: 99%

Analyzing the impact of long-term patients on ICU bed utilization

Weissman

2000

Intensive Care Med

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Analyzing the impact of long-term patients on ICU bed utilization

Weissman

2000

Intensive Care Med

View full text Add to dashboard Cite

show abstract

“…Neural networks have also been successfully applied in clinical outcome prediction of trauma mortality [39], surgical decision making on traumatic brain injury patients [40], recovery from surgery [41,42], pediatric meningococcal disease [43], transplantation outcome [44] Alzheimer's [45] and Dementia [46]. In addition some more technical comparison between statistical methods and artificial intelligence techniques for medical data exist [45][46][47][48][49][50][51][52][53][54][55].…”

Section: Clinical Applicationmentioning

confidence: 99%

Nonlinear association between serum testosterone levels and coronary artery disease in Iranian men

et al. 2009

View full text Add to dashboard Cite

Previous studies have shown controversial results about the role of androgens in coronary artery disease (CAD). We performed this study to examine and compare the relationship between androgenic hormones and CAD using conventional linear statistical techniques as well as novel non-linear approaches. The study was conducted on 502 consecutive men who were referred for selective coronary angiography at Tehran Heart Center due to different indications. We studied the relationship between androgenic hormones and CAD by using the generalized linear models, generalized additive models, and neural networks. Free testosterone (fT), total testosterone (tT) and dehydroepiandrosterone sulfate levels in patients with significant CAD versus normal individuals were 6.69 +/- 3.20 pg/ml, 16.60 +/- 6.66 nm/l, and 113.38 +/- 72.9 microg/dl versus 7.12 +/- 3.58 pg/ml, 15.82 +/- 7.26 nm/l, and 109.03 +/- 68.19 microg/dl, respectively (P > 0.05). The Generalized linear models was unable to show any significant relationship between androgenic hormones and CAD, while generalized additive model and neural networks supported the significant effect of androgenic hormones on CAD. This finding suggests a nonlinear association of tT levels with CAD: lower levels have a preventive effect on CAD, whereas higher values increase the risk of CAD. Emphasizing the non-linearity of the variables may provide new insight into the possible explanation of the effect of androgenic hormones on CAD.

show abstract

“…ANN have been applied to prognostic classification in different areas of clinical medicine; however, just a few attention has been given to their possible role as an aid in predicting outcome in miscellaneous areas of interest to cardiovascular medicine [20][21][22]. In particular, Ennis et al [20] applied a battery of adaptive nonlinear learning methods, including ANN, to the large database of the GUSTO study [31] to predict 30-day mortality following acute myocardial infarction from many potential risk factors.…”

Section: Comparison With Previous Studiesmentioning

confidence: 99%

“…In particular, they have been used in the effort to solve several problems in the noninvasive diagnosis [9] of coronary artery disease and, more specifically, of acute myocardial infarction (AMI) [10,11]. However, although the application of ANN to survival data has been attempted and discussed in different areas of clinical medicine [12][13][14][15][16][17][18][19], their use for prognostic purposes was given just a few attention in cardiovascular medicine [20][21][22]. Patients with uncomplicated myocardial infarction generally have a low probability of hard events [23], which makes their risk stratification quite difficult using both classical and Bayesian statistical approaches.…”

Section: Introductionmentioning

confidence: 99%