Predicting intensive care unit readmissions using probabilistic fuzzy systems

A.S. Fialho; U. Kaymak; F. Cismondi; S.M. Vieira; S.R. Reti; J.M.C. Sousa; S.N. Finkelstein

doi:10.1109/FUZZ-IEEE.2013.6622414

Predicting intensive care unit readmissions using probabilistic fuzzy systems

A.S. Fialho
, U. Kaymak
, F. Cismondi
, S.M. Vieira
, S.R. Reti
, J.M.C. Sousa
, S.N. Finkelstein

Information Systems IE&IS

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

8 Citaten (Scopus)

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We propose the application of probabilistic fuzzy systems (PFS) to model the prediction of early readmission in intensive care unit patients and compare it with the gold-standard method - logistic regression based on the APACHE II score. PFS are characterized by the combination of the linguistic description of the system with the statistical properties of data. On one hand, results point that PFS models perform comparably to the gold-standard method, with AUC values of 0.66±0.03. On the other hand, results also show that PFS models use a significant lower number of variables which, from the clinical practice point of view, suggests improved gains in terms of simplicity

Originele taal-2	Engels
Titel	2013 IEEE International Conference on Fuzzy Systems (FUZZ - IEEE 2013), 7-10 July 2013, Hyderabad, India
Plaats van productie	Piscataway
Uitgeverij	Institute of Electrical and Electronics Engineers
Pagina's	1-7
ISBN van elektronische versie	978-1-4799-0022-0
ISBN van geprinte versie	978-1-4799-0021-3
DOI's	https://doi.org/10.1109/FUZZ-IEEE.2013.6622414
Status	Gepubliceerd - 2013
Evenement	2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2013) - Hyderabad International Convention Center, Hyderabad, India Duur: 7 jul. 2013 → 10 jul. 2013 http://www.isical.ac.in/~fuzzieee2013/

Congres

Congres	2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2013)
Verkorte titel	FUZZ-IEEE 2013
Land/Regio	India
Stad	Hyderabad
Periode	7/07/13 → 10/07/13
Internet adres	http://www.isical.ac.in/~fuzzieee2013/

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10.1109/FUZZ-IEEE.2013.6622414

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Fialho, A. S., Kaymak, U., Cismondi, F., Vieira, S. M., Reti, S. R., Sousa, J. M. C., & Finkelstein, S. N. (2013). Predicting intensive care unit readmissions using probabilistic fuzzy systems. In 2013 IEEE International Conference on Fuzzy Systems (FUZZ - IEEE 2013), 7-10 July 2013, Hyderabad, India (blz. 1-7). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/FUZZ-IEEE.2013.6622414

@inproceedings{4d2a24ea5820461a82f7d72991fc70f0,

title = "Predicting intensive care unit readmissions using probabilistic fuzzy systems",

abstract = "We propose the application of probabilistic fuzzy systems (PFS) to model the prediction of early readmission in intensive care unit patients and compare it with the gold-standard method - logistic regression based on the APACHE II score. PFS are characterized by the combination of the linguistic description of the system with the statistical properties of data. On one hand, results point that PFS models perform comparably to the gold-standard method, with AUC values of 0.66±0.03. On the other hand, results also show that PFS models use a significant lower number of variables which, from the clinical practice point of view, suggests improved gains in terms of simplicity",

author = "A.S. Fialho and U. Kaymak and F. Cismondi and S.M. Vieira and S.R. Reti and J.M.C. Sousa and S.N. Finkelstein",

year = "2013",

doi = "10.1109/FUZZ-IEEE.2013.6622414",

language = "English",

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booktitle = "2013 IEEE International Conference on Fuzzy Systems (FUZZ - IEEE 2013), 7-10 July 2013, Hyderabad, India",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2013), FUZZ-IEEE 2013 ; Conference date: 07-07-2013 Through 10-07-2013",

url = "http://www.isical.ac.in/\textasciitilde{}fuzzieee2013/",

}

Fialho, AS, Kaymak, U, Cismondi, F, Vieira, SM, Reti, SR, Sousa, JMC & Finkelstein, SN 2013, Predicting intensive care unit readmissions using probabilistic fuzzy systems. in 2013 IEEE International Conference on Fuzzy Systems (FUZZ - IEEE 2013), 7-10 July 2013, Hyderabad, India. Institute of Electrical and Electronics Engineers, Piscataway, blz. 1-7, 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2013), Hyderabad, India, 7/07/13. https://doi.org/10.1109/FUZZ-IEEE.2013.6622414

Predicting intensive care unit readmissions using probabilistic fuzzy systems. / Fialho, A.S.; Kaymak, U.; Cismondi, F. et al.
2013 IEEE International Conference on Fuzzy Systems (FUZZ - IEEE 2013), 7-10 July 2013, Hyderabad, India. Piscataway: Institute of Electrical and Electronics Engineers, 2013. blz. 1-7.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

TY - GEN

T1 - Predicting intensive care unit readmissions using probabilistic fuzzy systems

AU - Fialho, A.S.

AU - Kaymak, U.

AU - Cismondi, F.

AU - Vieira, S.M.

AU - Reti, S.R.

AU - Sousa, J.M.C.

AU - Finkelstein, S.N.

PY - 2013

Y1 - 2013

N2 - We propose the application of probabilistic fuzzy systems (PFS) to model the prediction of early readmission in intensive care unit patients and compare it with the gold-standard method - logistic regression based on the APACHE II score. PFS are characterized by the combination of the linguistic description of the system with the statistical properties of data. On one hand, results point that PFS models perform comparably to the gold-standard method, with AUC values of 0.66±0.03. On the other hand, results also show that PFS models use a significant lower number of variables which, from the clinical practice point of view, suggests improved gains in terms of simplicity

AB - We propose the application of probabilistic fuzzy systems (PFS) to model the prediction of early readmission in intensive care unit patients and compare it with the gold-standard method - logistic regression based on the APACHE II score. PFS are characterized by the combination of the linguistic description of the system with the statistical properties of data. On one hand, results point that PFS models perform comparably to the gold-standard method, with AUC values of 0.66±0.03. On the other hand, results also show that PFS models use a significant lower number of variables which, from the clinical practice point of view, suggests improved gains in terms of simplicity

U2 - 10.1109/FUZZ-IEEE.2013.6622414

DO - 10.1109/FUZZ-IEEE.2013.6622414

M3 - Conference contribution

SN - 978-1-4799-0021-3

SP - 1

EP - 7

BT - 2013 IEEE International Conference on Fuzzy Systems (FUZZ - IEEE 2013), 7-10 July 2013, Hyderabad, India

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

T2 - 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2013)

Y2 - 7 July 2013 through 10 July 2013

ER -

Fialho AS, Kaymak U, Cismondi F, Vieira SM, Reti SR, Sousa JMC et al. Predicting intensive care unit readmissions using probabilistic fuzzy systems. In 2013 IEEE International Conference on Fuzzy Systems (FUZZ - IEEE 2013), 7-10 July 2013, Hyderabad, India. Piscataway: Institute of Electrical and Electronics Engineers. 2013. blz. 1-7 doi: 10.1109/FUZZ-IEEE.2013.6622414

Predicting intensive care unit readmissions using probabilistic fuzzy systems

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