Patient ADE Risk Prediction through Hierarchical Time-Aware Neural Network Using Claim Codes

Jinhe Shi; Xiangyu Gao; Chenyu Ha; Yage Wang; Guodong Gao; Yi Chen

doi:10.1109/BigData50022.2020.9378336

Patient ADE Risk Prediction through Hierarchical Time-Aware Neural Network Using Claim Codes

Jinhe Shi, Xiangyu Gao, Chenyu Ha, Yage Wang, Guodong Gao, Yi Chen

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

Abstract

Adverse drug events (ADEs) are a serious health problem that can be life-threatening. While a lot of work on detecting correlation between a drug and an ADE, limited studies have been conducted on personalized ADE risk prediction. Avoiding the drugs with high likelihood of causing severe ADEs helps physicians to provide safer treatments to patients. The goal of this study is to assess personalized ADE risks that a target drug may induce on a target patient, based on patient medical history recorded in claim codes, which provide information about diagnosis, drugs taken, related medical supplies besides billing information. We developed a HTNNR model (Hierarchical Time-aware Neural Network for ADE Risk) that captures characteristics of claim codes and their relationship. Eempirical evaluation shows that the proposed HTNNR model substantially outperforms the comparison methods.

Original language	English (US)
Title of host publication	Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
Editors	Xintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1388-1393
Number of pages	6
ISBN (Electronic)	9781728162515
DOIs	https://doi.org/10.1109/BigData50022.2020.9378336
State	Published - Dec 10 2020
Event	8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States Duration: Dec 10 2020 → Dec 13 2020

Publication series

Name	Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

Conference

Conference	8th IEEE International Conference on Big Data, Big Data 2020
Country/Territory	United States
City	Virtual, Atlanta
Period	12/10/20 → 12/13/20

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Information Systems
Information Systems and Management
Safety, Risk, Reliability and Quality

Keywords

Adverse Drug Events
Claim Codes
Neural Networks
Personalized Risk Assessment

Access to Document

10.1109/BigData50022.2020.9378336

Cite this

Shi, J., Gao, X., Ha, C., Wang, Y., Gao, G., & Chen, Y. (2020). Patient ADE Risk Prediction through Hierarchical Time-Aware Neural Network Using Claim Codes. In X. Wu, C. Jermaine, L. Xiong, X. T. Hu, O. Kotevska, S. Lu, W. Xu, S. Aluru, C. Zhai, E. Al-Masri, Z. Chen, & J. Saltz (Eds.), Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020 (pp. 1388-1393). [9378336] (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData50022.2020.9378336

Shi, Jinhe ; Gao, Xiangyu ; Ha, Chenyu et al. / Patient ADE Risk Prediction through Hierarchical Time-Aware Neural Network Using Claim Codes. Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. editor / Xintao Wu ; Chris Jermaine ; Li Xiong ; Xiaohua Tony Hu ; Olivera Kotevska ; Siyuan Lu ; Weijia Xu ; Srinivas Aluru ; Chengxiang Zhai ; Eyhab Al-Masri ; Zhiyuan Chen ; Jeff Saltz. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 1388-1393 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020).

@inproceedings{068a18ac33554777a7695c95099cbc99,

title = "Patient ADE Risk Prediction through Hierarchical Time-Aware Neural Network Using Claim Codes",

abstract = "Adverse drug events (ADEs) are a serious health problem that can be life-threatening. While a lot of work on detecting correlation between a drug and an ADE, limited studies have been conducted on personalized ADE risk prediction. Avoiding the drugs with high likelihood of causing severe ADEs helps physicians to provide safer treatments to patients. The goal of this study is to assess personalized ADE risks that a target drug may induce on a target patient, based on patient medical history recorded in claim codes, which provide information about diagnosis, drugs taken, related medical supplies besides billing information. We developed a HTNNR model (Hierarchical Time-aware Neural Network for ADE Risk) that captures characteristics of claim codes and their relationship. Eempirical evaluation shows that the proposed HTNNR model substantially outperforms the comparison methods.",

keywords = "Adverse Drug Events, Claim Codes, Neural Networks, Personalized Risk Assessment",

author = "Jinhe Shi and Xiangyu Gao and Chenyu Ha and Yage Wang and Guodong Gao and Yi Chen",

note = "Funding Information: The work is partially supported by the Leir Foundation, a grant from the National Institutes of Health (UL1TR003017), a Google Faculty Research Award, and a Faculty Seed Grant from the Henry J. and Erna D. Leir Research Institute for Business, Technology, and Society at NJIT. Publisher Copyright: {\textcopyright} 2020 IEEE.; 8th IEEE International Conference on Big Data, Big Data 2020 ; Conference date: 10-12-2020 Through 13-12-2020",

year = "2020",

month = dec,

day = "10",

doi = "10.1109/BigData50022.2020.9378336",

language = "English (US)",

series = "Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020",

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

pages = "1388--1393",

editor = "Xintao Wu and Chris Jermaine and Li Xiong and Hu, {Xiaohua Tony} and Olivera Kotevska and Siyuan Lu and Weijia Xu and Srinivas Aluru and Chengxiang Zhai and Eyhab Al-Masri and Zhiyuan Chen and Jeff Saltz",

booktitle = "Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020",

address = "United States",

}

Shi, J, Gao, X, Ha, C, Wang, Y, Gao, G & Chen, Y 2020, Patient ADE Risk Prediction through Hierarchical Time-Aware Neural Network Using Claim Codes. in X Wu, C Jermaine, L Xiong, XT Hu, O Kotevska, S Lu, W Xu, S Aluru, C Zhai, E Al-Masri, Z Chen & J Saltz (eds), Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020., 9378336, Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020, Institute of Electrical and Electronics Engineers Inc., pp. 1388-1393, 8th IEEE International Conference on Big Data, Big Data 2020, Virtual, Atlanta, United States, 12/10/20. https://doi.org/10.1109/BigData50022.2020.9378336

Patient ADE Risk Prediction through Hierarchical Time-Aware Neural Network Using Claim Codes. / Shi, Jinhe; Gao, Xiangyu; Ha, Chenyu et al.

Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. ed. / Xintao Wu; Chris Jermaine; Li Xiong; Xiaohua Tony Hu; Olivera Kotevska; Siyuan Lu; Weijia Xu; Srinivas Aluru; Chengxiang Zhai; Eyhab Al-Masri; Zhiyuan Chen; Jeff Saltz. Institute of Electrical and Electronics Engineers Inc., 2020. p. 1388-1393 9378336 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020).

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

TY - GEN

T1 - Patient ADE Risk Prediction through Hierarchical Time-Aware Neural Network Using Claim Codes

AU - Shi, Jinhe

AU - Gao, Xiangyu

AU - Ha, Chenyu

AU - Wang, Yage

AU - Gao, Guodong

AU - Chen, Yi

N1 - Funding Information: The work is partially supported by the Leir Foundation, a grant from the National Institutes of Health (UL1TR003017), a Google Faculty Research Award, and a Faculty Seed Grant from the Henry J. and Erna D. Leir Research Institute for Business, Technology, and Society at NJIT. Publisher Copyright: © 2020 IEEE.

PY - 2020/12/10

Y1 - 2020/12/10

N2 - Adverse drug events (ADEs) are a serious health problem that can be life-threatening. While a lot of work on detecting correlation between a drug and an ADE, limited studies have been conducted on personalized ADE risk prediction. Avoiding the drugs with high likelihood of causing severe ADEs helps physicians to provide safer treatments to patients. The goal of this study is to assess personalized ADE risks that a target drug may induce on a target patient, based on patient medical history recorded in claim codes, which provide information about diagnosis, drugs taken, related medical supplies besides billing information. We developed a HTNNR model (Hierarchical Time-aware Neural Network for ADE Risk) that captures characteristics of claim codes and their relationship. Eempirical evaluation shows that the proposed HTNNR model substantially outperforms the comparison methods.

AB - Adverse drug events (ADEs) are a serious health problem that can be life-threatening. While a lot of work on detecting correlation between a drug and an ADE, limited studies have been conducted on personalized ADE risk prediction. Avoiding the drugs with high likelihood of causing severe ADEs helps physicians to provide safer treatments to patients. The goal of this study is to assess personalized ADE risks that a target drug may induce on a target patient, based on patient medical history recorded in claim codes, which provide information about diagnosis, drugs taken, related medical supplies besides billing information. We developed a HTNNR model (Hierarchical Time-aware Neural Network for ADE Risk) that captures characteristics of claim codes and their relationship. Eempirical evaluation shows that the proposed HTNNR model substantially outperforms the comparison methods.

KW - Adverse Drug Events

KW - Claim Codes

KW - Neural Networks

KW - Personalized Risk Assessment

UR - http://www.scopus.com/inward/record.url?scp=85103820472&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85103820472&partnerID=8YFLogxK

U2 - 10.1109/BigData50022.2020.9378336

DO - 10.1109/BigData50022.2020.9378336

M3 - Conference contribution

AN - SCOPUS:85103820472

T3 - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

SP - 1388

EP - 1393

BT - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

A2 - Wu, Xintao

A2 - Jermaine, Chris

A2 - Xiong, Li

A2 - Hu, Xiaohua Tony

A2 - Kotevska, Olivera

A2 - Lu, Siyuan

A2 - Xu, Weijia

A2 - Aluru, Srinivas

A2 - Zhai, Chengxiang

A2 - Al-Masri, Eyhab

A2 - Chen, Zhiyuan

A2 - Saltz, Jeff

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 8th IEEE International Conference on Big Data, Big Data 2020

Y2 - 10 December 2020 through 13 December 2020

ER -

Shi J, Gao X, Ha C, Wang Y, Gao G, Chen Y. Patient ADE Risk Prediction through Hierarchical Time-Aware Neural Network Using Claim Codes. In Wu X, Jermaine C, Xiong L, Hu XT, Kotevska O, Lu S, Xu W, Aluru S, Zhai C, Al-Masri E, Chen Z, Saltz J, editors, Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 1388-1393. 9378336. (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020). doi: 10.1109/BigData50022.2020.9378336

Patient ADE Risk Prediction through Hierarchical Time-Aware Neural Network Using Claim Codes

Abstract

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All Science Journal Classification (ASJC) codes

Keywords

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