Matches in SemOpenAlex for { <https://semopenalex.org/work/W2017680319> ?p ?o ?g. }
- W2017680319 abstract "Different methods have recently been proposed for predicting morbidity in intensive care units (ICU). The aim of the present study was to critically review a number of approaches for developing models capable of estimating the probability of morbidity in ICU after heart surgery. The study is divided into two parts. In this first part, popular models used to estimate the probability of class membership are grouped into distinct categories according to their underlying mathematical principles. Modelling techniques and intrinsic strengths and weaknesses of each model are analysed and discussed from a theoretical point of view, in consideration of clinical applications.Models based on Bayes rule, k-nearest neighbour algorithm, logistic regression, scoring systems and artificial neural networks are investigated. Key issues for model design are described. The mathematical treatment of some aspects of model structure is also included for readers interested in developing models, though a full understanding of mathematical relationships is not necessary if the reader is only interested in perceiving the practical meaning of model assumptions, weaknesses and strengths from a user point of view.Scoring systems are very attractive due to their simplicity of use, although this may undermine their predictive capacity. Logistic regression models are trustworthy tools, although they suffer from the principal limitations of most regression procedures. Bayesian models seem to be a good compromise between complexity and predictive performance, but model recalibration is generally necessary. k-nearest neighbour may be a valid non parametric technique, though computational cost and the need for large data storage are major weaknesses of this approach. Artificial neural networks have intrinsic advantages with respect to common statistical models, though the training process may be problematical.Knowledge of model assumptions and the theoretical strengths and weaknesses of different approaches are fundamental for designing models for estimating the probability of morbidity after heart surgery. However, a rational choice also requires evaluation and comparison of actual performances of locally-developed competitive models in the clinical scenario to obtain satisfactory agreement between local needs and model response. In the second part of this study the above predictive models will therefore be tested on real data acquired in a specialized ICU." @default.
- W2017680319 created "2016-06-24" @default.
- W2017680319 creator A5014725590 @default.
- W2017680319 creator A5017361422 @default.
- W2017680319 creator A5041929997 @default.
- W2017680319 creator A5055739492 @default.
- W2017680319 creator A5070132036 @default.
- W2017680319 creator A5074360715 @default.
- W2017680319 date "2007-11-22" @default.
- W2017680319 modified "2023-09-26" @default.
- W2017680319 title "A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part I: model planning" @default.
- W2017680319 cites W1486210018 @default.
- W2017680319 cites W1920585257 @default.
- W2017680319 cites W1979102891 @default.
- W2017680319 cites W1981976602 @default.
- W2017680319 cites W1983841285 @default.
- W2017680319 cites W1984471054 @default.
- W2017680319 cites W1990534247 @default.
- W2017680319 cites W1995792743 @default.
- W2017680319 cites W2008448636 @default.
- W2017680319 cites W2008790842 @default.
- W2017680319 cites W2010131456 @default.
- W2017680319 cites W2014480116 @default.
- W2017680319 cites W2020307189 @default.
- W2017680319 cites W2021287136 @default.
- W2017680319 cites W2023557964 @default.
- W2017680319 cites W2026028785 @default.
- W2017680319 cites W2029426756 @default.
- W2017680319 cites W2029694543 @default.
- W2017680319 cites W2030400109 @default.
- W2017680319 cites W2033885216 @default.
- W2017680319 cites W2033925475 @default.
- W2017680319 cites W2036131137 @default.
- W2017680319 cites W2044848461 @default.
- W2017680319 cites W2047634553 @default.
- W2017680319 cites W2048993934 @default.
- W2017680319 cites W2052888026 @default.
- W2017680319 cites W2053509658 @default.
- W2017680319 cites W2059737744 @default.
- W2017680319 cites W2061880523 @default.
- W2017680319 cites W2063507658 @default.
- W2017680319 cites W2064066211 @default.
- W2017680319 cites W2067747050 @default.
- W2017680319 cites W2068443247 @default.
- W2017680319 cites W2071419812 @default.
- W2017680319 cites W2093032787 @default.
- W2017680319 cites W2099706665 @default.
- W2017680319 cites W2112670427 @default.
- W2017680319 cites W2115358726 @default.
- W2017680319 cites W2120800556 @default.
- W2017680319 cites W2129241699 @default.
- W2017680319 cites W2129925362 @default.
- W2017680319 cites W2132048003 @default.
- W2017680319 cites W2135443979 @default.
- W2017680319 cites W2135542802 @default.
- W2017680319 cites W2136283380 @default.
- W2017680319 cites W2152419043 @default.
- W2017680319 cites W2157825442 @default.
- W2017680319 cites W2165139489 @default.
- W2017680319 cites W2166979232 @default.
- W2017680319 cites W2195081412 @default.
- W2017680319 cites W2400674531 @default.
- W2017680319 cites W2414078188 @default.
- W2017680319 cites W2427881153 @default.
- W2017680319 cites W2469193778 @default.
- W2017680319 cites W4233014035 @default.
- W2017680319 cites W4247943214 @default.
- W2017680319 cites W4293242440 @default.
- W2017680319 cites W4299689471 @default.
- W2017680319 cites W4361865037 @default.
- W2017680319 doi "https://doi.org/10.1186/1472-6947-7-35" @default.
- W2017680319 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/2212627" @default.
- W2017680319 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/18034872" @default.
- W2017680319 hasPublicationYear "2007" @default.
- W2017680319 type Work @default.
- W2017680319 sameAs 2017680319 @default.
- W2017680319 citedByCount "27" @default.
- W2017680319 countsByYear W20176803192012 @default.
- W2017680319 countsByYear W20176803192013 @default.
- W2017680319 countsByYear W20176803192014 @default.
- W2017680319 countsByYear W20176803192015 @default.
- W2017680319 countsByYear W20176803192016 @default.
- W2017680319 countsByYear W20176803192017 @default.
- W2017680319 countsByYear W20176803192018 @default.
- W2017680319 countsByYear W20176803192019 @default.
- W2017680319 countsByYear W20176803192020 @default.
- W2017680319 crossrefType "journal-article" @default.
- W2017680319 hasAuthorship W2017680319A5014725590 @default.
- W2017680319 hasAuthorship W2017680319A5017361422 @default.
- W2017680319 hasAuthorship W2017680319A5041929997 @default.
- W2017680319 hasAuthorship W2017680319A5055739492 @default.
- W2017680319 hasAuthorship W2017680319A5070132036 @default.
- W2017680319 hasAuthorship W2017680319A5074360715 @default.
- W2017680319 hasBestOaLocation W20176803191 @default.
- W2017680319 hasConcept C107673813 @default.
- W2017680319 hasConcept C111472728 @default.
- W2017680319 hasConcept C119857082 @default.
- W2017680319 hasConcept C124101348 @default.
- W2017680319 hasConcept C138885662 @default.
- W2017680319 hasConcept C151956035 @default.