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01000naa a22002652 4500 |
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NLM109954858 |
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DE-627 |
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20231222152439.0 |
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tu |
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231222s2000 xx ||||| 00| ||eng c |
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Lacson, R C
|e verfasserin
|4 aut
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|a Major complications after angioplasty in patients with chronic renal failure
|b a comparison of predictive models
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|c 2000
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|a Text
|b txt
|2 rdacontent
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|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
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|a Band
|b nc
|2 rdacarrier
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|a Date Completed 08.03.2001
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|a Date Revised 10.12.2019
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|a published: Print
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|a Citation Status MEDLINE
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|a Novel modeling approaches were investigated to predict major complications in patients with chronic renal failure (CRF) or end-stage renal disease (ESRD) undergoing percutaneous transluminal coronary angioplasty (PTCA). The following hypotheses were explored: (1) Pre-angioplasty patient risk factors, demographic characteristics and procedural information may be used to predict major complications after PTCA; and (2) Rough sets and artificial neural nets (ANN) may be used to build models that are better than standard logistic regression models. Several variables were found to be predictive of major complications for patients with CRF or ESRD undergoing PTCA. The presence of shock at presentation portends poor outcome but congestive heart failure and prior history of myocardial infarction increases the risk tenfold and 25-fold, respectively. The discriminatory ability of the ANN model was better than both Rough Sets and Logistic Regression for the test set
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|a Comparative Study
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|a Journal Article
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|a Research Support, U.S. Gov't, P.H.S.
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|a Ohno-Machado, L
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Proceedings. AMIA Symposium
|d 1998
|g (2000) vom: 01., Seite 457-61
|w (DE-627)NLM098642928
|x 1531-605X
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| 773 |
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|g year:2000
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|g pages:457-61
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|a AR
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|j 2000
|b 01
|h 457-61
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