Predicting Practice Setting Using Topic Modeling

Liwei Wang; Yanshan Wang; Feichen Shen; Majid Rastegar-Mojarad; Hongfang Liu

doi:10.1109/ICHI-W.2018.00020

Predicting Practice Setting Using Topic Modeling

Liwei Wang, Yanshan Wang, Feichen Shen, Majid Rastegar-Mojarad, Hongfang Liu

Quantitative Health Sciences

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

Abstract

The implementation of problem lists in EHRs has a potential to help practitioners to provide customized care to patients. However, it remains an open question on how to leverage problem lists in different practice settings to provide tailored care, of which the bottleneck lies in the associations between problem list and practice setting. In this study, we investigated their association and predicted practice setting based on problem list using topic modeling.

Original language	English (US)
Title of host publication	Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	62-63
Number of pages	2
ISBN (Electronic)	9781538667774
DOIs	https://doi.org/10.1109/ICHI-W.2018.00020
State	Published - Jul 16 2018
Event	6th IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018 - New York, United States Duration: Jun 4 2018 → Jun 7 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018

Other

Other	6th IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018
Country/Territory	United States
City	New York
Period	6/4/18 → 6/7/18

Keywords

latent dirichlet allocation
practice setting
problem list
topic modeling

ASJC Scopus subject areas

Information Systems and Management
Health Informatics

Access to Document

10.1109/ICHI-W.2018.00020

Cite this

Wang, L., Wang, Y., Shen, F., Rastegar-Mojarad, M., & Liu, H. (2018). Predicting Practice Setting Using Topic Modeling. In Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018 (pp. 62-63). [8411806] (Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICHI-W.2018.00020

Predicting Practice Setting Using Topic Modeling. / Wang, Liwei; Wang, Yanshan; Shen, Feichen et al.
Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 62-63 8411806 (Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018).

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

Wang, L, Wang, Y, Shen, F, Rastegar-Mojarad, M & Liu, H 2018, Predicting Practice Setting Using Topic Modeling. in Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018., 8411806, Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018, Institute of Electrical and Electronics Engineers Inc., pp. 62-63, 6th IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018, New York, United States, 6/4/18. https://doi.org/10.1109/ICHI-W.2018.00020

Wang L, Wang Y, Shen F, Rastegar-Mojarad M, Liu H. Predicting Practice Setting Using Topic Modeling. In Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 62-63. 8411806. (Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018). doi: 10.1109/ICHI-W.2018.00020

Wang, Liwei ; Wang, Yanshan ; Shen, Feichen et al. / Predicting Practice Setting Using Topic Modeling. Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 62-63 (Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018).

@inproceedings{08a16e1abd414d74a362b15caebf86ca,

title = "Predicting Practice Setting Using Topic Modeling",

abstract = "The implementation of problem lists in EHRs has a potential to help practitioners to provide customized care to patients. However, it remains an open question on how to leverage problem lists in different practice settings to provide tailored care, of which the bottleneck lies in the associations between problem list and practice setting. In this study, we investigated their association and predicted practice setting based on problem list using topic modeling.",

keywords = "latent dirichlet allocation, practice setting, problem list, topic modeling",

author = "Liwei Wang and Yanshan Wang and Feichen Shen and Majid Rastegar-Mojarad and Hongfang Liu",

note = "Funding Information: A CKNOWLEDGMENT The authors gratefully acknowledge the support from the National Institute of Health (NIH) grant 5R01GM102282-03, 5R01LM011934-02 and 1K99LM012021-01A1.; 6th IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018 ; Conference date: 04-06-2018 Through 07-06-2018",

year = "2018",

month = jul,

day = "16",

doi = "10.1109/ICHI-W.2018.00020",

language = "English (US)",

series = "Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018",

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

pages = "62--63",

booktitle = "Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018",

}

TY - GEN

T1 - Predicting Practice Setting Using Topic Modeling

AU - Wang, Liwei

AU - Wang, Yanshan

AU - Shen, Feichen

AU - Rastegar-Mojarad, Majid

AU - Liu, Hongfang

N1 - Funding Information: A CKNOWLEDGMENT The authors gratefully acknowledge the support from the National Institute of Health (NIH) grant 5R01GM102282-03, 5R01LM011934-02 and 1K99LM012021-01A1.

PY - 2018/7/16

Y1 - 2018/7/16

N2 - The implementation of problem lists in EHRs has a potential to help practitioners to provide customized care to patients. However, it remains an open question on how to leverage problem lists in different practice settings to provide tailored care, of which the bottleneck lies in the associations between problem list and practice setting. In this study, we investigated their association and predicted practice setting based on problem list using topic modeling.

AB - The implementation of problem lists in EHRs has a potential to help practitioners to provide customized care to patients. However, it remains an open question on how to leverage problem lists in different practice settings to provide tailored care, of which the bottleneck lies in the associations between problem list and practice setting. In this study, we investigated their association and predicted practice setting based on problem list using topic modeling.

KW - latent dirichlet allocation

KW - practice setting

KW - problem list

KW - topic modeling

UR - http://www.scopus.com/inward/record.url?scp=85051036261&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85051036261&partnerID=8YFLogxK

U2 - 10.1109/ICHI-W.2018.00020

DO - 10.1109/ICHI-W.2018.00020

M3 - Conference contribution

AN - SCOPUS:85051036261

T3 - Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018

SP - 62

EP - 63

BT - Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018

Y2 - 4 June 2018 through 7 June 2018

ER -

Predicting Practice Setting Using Topic Modeling

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this