A comparison of supervised machine learning techniques for predicting short-term in-hospital length of stay among diabetic patients

April Morton; Eman Marzban; Georgios Giannoulis; Ayush Patel; Rajender Aparasu; Ioannis A. Kakadiaris

doi:10.1109/ICMLA.2014.76

A comparison of supervised machine learning techniques for predicting short-term in-hospital length of stay among diabetic patients

April Morton, Eman Marzban, Georgios Giannoulis, Ayush Patel, Rajender Aparasu, Ioannis A. Kakadiaris

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

54 Scopus citations

Abstract

Diabetes is a life-altering medical condition that affects millions of people and results in many hospitalizations per year. Consequently, predicting the length of stay of in-hospital diabetic patients has become increasingly important for staffing and resource planning. Although statistical methods have been used to predict length of stay in hospitalized patients, many powerful machine learning techniques have not yet been explored. In this paper, we compare and discuss the performance of various supervised machine learning algorithms (i.e., Multiple linear regression, support vector machines, multi-task learning, and random forests) for predicting long versus short-term length of stay of hospitalized diabetic patients.

Original language	English (US)
Title of host publication	Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014
Editors	Cesar Ferri, Guangzhi Qu, Xue-wen Chen, M. Arif Wani, Plamen Angelov, Jian-Huang Lai
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	428-431
Number of pages	4
ISBN (Electronic)	9781479974153
DOIs	https://doi.org/10.1109/ICMLA.2014.76
State	Published - Feb 5 2014
Event	2014 13th International Conference on Machine Learning and Applications, ICMLA 2014 - Detroit, United States Duration: Dec 3 2014 → Dec 6 2014

Publication series

Name	Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014

Conference

Conference	2014 13th International Conference on Machine Learning and Applications, ICMLA 2014
Country/Territory	United States
City	Detroit
Period	12/3/14 → 12/6/14

Keywords

Diabetes
In-Hospital Length of Stay Prediction
Multi-Task Learning
Random Forests
Supervised Machine Learning
Support Vector Machines
Support Vector Machines Plus

ASJC Scopus subject areas

Computer Science Applications
Human-Computer Interaction

Access to Document

10.1109/ICMLA.2014.76

Cite this

Morton, A., Marzban, E., Giannoulis, G., Patel, A., Aparasu, R., & Kakadiaris, I. A. (2014). A comparison of supervised machine learning techniques for predicting short-term in-hospital length of stay among diabetic patients. In C. Ferri, G. Qu, X. Chen, M. A. Wani, P. Angelov, & J.-H. Lai (Eds.), Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014 (pp. 428-431). Article 7033154 (Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA.2014.76

A comparison of supervised machine learning techniques for predicting short-term in-hospital length of stay among diabetic patients. / Morton, April; Marzban, Eman; Giannoulis, Georgios et al.
Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014. ed. / Cesar Ferri; Guangzhi Qu; Xue-wen Chen; M. Arif Wani; Plamen Angelov; Jian-Huang Lai. Institute of Electrical and Electronics Engineers Inc., 2014. p. 428-431 7033154 (Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Morton, A, Marzban, E, Giannoulis, G, Patel, A, Aparasu, R & Kakadiaris, IA 2014, A comparison of supervised machine learning techniques for predicting short-term in-hospital length of stay among diabetic patients. in C Ferri, G Qu, X Chen, MA Wani, P Angelov & J-H Lai (eds), Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014., 7033154, Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014, Institute of Electrical and Electronics Engineers Inc., pp. 428-431, 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014, Detroit, United States, 12/3/14. https://doi.org/10.1109/ICMLA.2014.76

Morton A, Marzban E, Giannoulis G, Patel A, Aparasu R, Kakadiaris IA. A comparison of supervised machine learning techniques for predicting short-term in-hospital length of stay among diabetic patients. In Ferri C, Qu G, Chen X, Wani MA, Angelov P, Lai JH, editors, Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 428-431. 7033154. (Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014). doi: 10.1109/ICMLA.2014.76

Morton, April ; Marzban, Eman ; Giannoulis, Georgios et al. / A comparison of supervised machine learning techniques for predicting short-term in-hospital length of stay among diabetic patients. Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014. editor / Cesar Ferri ; Guangzhi Qu ; Xue-wen Chen ; M. Arif Wani ; Plamen Angelov ; Jian-Huang Lai. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 428-431 (Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014).

@inproceedings{0f2719192a694635870fb05049c0a7b8,

title = "A comparison of supervised machine learning techniques for predicting short-term in-hospital length of stay among diabetic patients",

abstract = "Diabetes is a life-altering medical condition that affects millions of people and results in many hospitalizations per year. Consequently, predicting the length of stay of in-hospital diabetic patients has become increasingly important for staffing and resource planning. Although statistical methods have been used to predict length of stay in hospitalized patients, many powerful machine learning techniques have not yet been explored. In this paper, we compare and discuss the performance of various supervised machine learning algorithms (i.e., Multiple linear regression, support vector machines, multi-task learning, and random forests) for predicting long versus short-term length of stay of hospitalized diabetic patients.",

keywords = "Diabetes, In-Hospital Length of Stay Prediction, Multi-Task Learning, Random Forests, Supervised Machine Learning, Support Vector Machines, Support Vector Machines Plus",

author = "April Morton and Eman Marzban and Georgios Giannoulis and Ayush Patel and Rajender Aparasu and Kakadiaris, {Ioannis A.}",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014 ; Conference date: 03-12-2014 Through 06-12-2014",

year = "2014",

month = feb,

day = "5",

doi = "10.1109/ICMLA.2014.76",

language = "English (US)",

series = "Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "428--431",

editor = "Cesar Ferri and Guangzhi Qu and Xue-wen Chen and Wani, {M. Arif} and Plamen Angelov and Jian-Huang Lai",

booktitle = "Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014",

address = "United States",

}

TY - GEN

T1 - A comparison of supervised machine learning techniques for predicting short-term in-hospital length of stay among diabetic patients

AU - Morton, April

AU - Marzban, Eman

AU - Giannoulis, Georgios

AU - Patel, Ayush

AU - Aparasu, Rajender

AU - Kakadiaris, Ioannis A.

PY - 2014/2/5

Y1 - 2014/2/5

N2 - Diabetes is a life-altering medical condition that affects millions of people and results in many hospitalizations per year. Consequently, predicting the length of stay of in-hospital diabetic patients has become increasingly important for staffing and resource planning. Although statistical methods have been used to predict length of stay in hospitalized patients, many powerful machine learning techniques have not yet been explored. In this paper, we compare and discuss the performance of various supervised machine learning algorithms (i.e., Multiple linear regression, support vector machines, multi-task learning, and random forests) for predicting long versus short-term length of stay of hospitalized diabetic patients.

AB - Diabetes is a life-altering medical condition that affects millions of people and results in many hospitalizations per year. Consequently, predicting the length of stay of in-hospital diabetic patients has become increasingly important for staffing and resource planning. Although statistical methods have been used to predict length of stay in hospitalized patients, many powerful machine learning techniques have not yet been explored. In this paper, we compare and discuss the performance of various supervised machine learning algorithms (i.e., Multiple linear regression, support vector machines, multi-task learning, and random forests) for predicting long versus short-term length of stay of hospitalized diabetic patients.

KW - Diabetes

KW - In-Hospital Length of Stay Prediction

KW - Multi-Task Learning

KW - Random Forests

KW - Supervised Machine Learning

KW - Support Vector Machines

KW - Support Vector Machines Plus

UR - http://www.scopus.com/inward/record.url?scp=84939183820&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84939183820&partnerID=8YFLogxK

U2 - 10.1109/ICMLA.2014.76

DO - 10.1109/ICMLA.2014.76

M3 - Conference contribution

AN - SCOPUS:84939183820

T3 - Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014

SP - 428

EP - 431

BT - Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014

A2 - Ferri, Cesar

A2 - Qu, Guangzhi

A2 - Chen, Xue-wen

A2 - Wani, M. Arif

A2 - Angelov, Plamen

A2 - Lai, Jian-Huang

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014

Y2 - 3 December 2014 through 6 December 2014

ER -

A comparison of supervised machine learning techniques for predicting short-term in-hospital length of stay among diabetic patients

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this