Disease predictive modeling for healthcare management system

Khulood Nakhat; Fatima Khalique; Shoab Ahmed Khan

doi:10.1145/3418094.3418134

Disease predictive modeling for healthcare management system

Khulood Nakhat
, Fatima Khalique
, Shoab Ahmed Khan

National University of Sciences and Technology Pakistan

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

Abstract

This study attempts to perform predictive analytics for decision makers in healthcare management systems using surveillance data from multiple sources for formulating intervention programs based on the results. With the availability of big data in health from multiple sources including electronic health records, it is possible to integrate data and perform near real-time predictive analysis for incoming streams of disease incidences. We use a temporal predictive Auto-Regressive Integrated Moving Averaging model (ARIMA) in combination with a minimum size moving window to forecast the disease incidences over a data collection and integration framework. We applied our model for predictive analysis of Hepatitis C incidences in Vehari District of Punjab province in Pakistan. Model performance is evaluated based on Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The model is capable of finding trends of any disease to aid timely decision making in the healthcare management context.

Original language	English
Title of host publication	Proceedings of the 4th International Conference on Medical and Health Informatics, ICMHI 2020
Publisher	Association for Computing Machinery
Pages	37-44
Number of pages	8
ISBN (Electronic)	9781450377768
DOIs	https://doi.org/10.1145/3418094.3418134
Publication status	Published - 14 Aug 2020
Externally published	Yes
Event	4th International Conference on Medical and Health Informatics, ICMHI 2020 - Virtual, Online, Japan Duration: 14 Aug 2020 → 16 Aug 2020

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	4th International Conference on Medical and Health Informatics, ICMHI 2020
Country/Territory	Japan
City	Virtual, Online
Period	14/08/20 → 16/08/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

ARIMA
Forecast
Predictive analysis
Public health management
Stochastic modeling
Time series

Access to Document

10.1145/3418094.3418134

Cite this

@inproceedings{1a713b7cc3d6440ca0ccb4aee8098c66,

title = "Disease predictive modeling for healthcare management system",

abstract = "This study attempts to perform predictive analytics for decision makers in healthcare management systems using surveillance data from multiple sources for formulating intervention programs based on the results. With the availability of big data in health from multiple sources including electronic health records, it is possible to integrate data and perform near real-time predictive analysis for incoming streams of disease incidences. We use a temporal predictive Auto-Regressive Integrated Moving Averaging model (ARIMA) in combination with a minimum size moving window to forecast the disease incidences over a data collection and integration framework. We applied our model for predictive analysis of Hepatitis C incidences in Vehari District of Punjab province in Pakistan. Model performance is evaluated based on Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The model is capable of finding trends of any disease to aid timely decision making in the healthcare management context.",

keywords = "ARIMA, Forecast, Predictive analysis, Public health management, Stochastic modeling, Time series",

author = "Khulood Nakhat and Fatima Khalique and Khan, \{Shoab Ahmed\}",

note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 4th International Conference on Medical and Health Informatics, ICMHI 2020 ; Conference date: 14-08-2020 Through 16-08-2020",

year = "2020",

month = aug,

day = "14",

doi = "10.1145/3418094.3418134",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "37--44",

booktitle = "Proceedings of the 4th International Conference on Medical and Health Informatics, ICMHI 2020",

}

Nakhat, K, Khalique, F & Khan, SA 2020, Disease predictive modeling for healthcare management system. in Proceedings of the 4th International Conference on Medical and Health Informatics, ICMHI 2020. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 37-44, 4th International Conference on Medical and Health Informatics, ICMHI 2020, Virtual, Online, Japan, 14/08/20. https://doi.org/10.1145/3418094.3418134

Disease predictive modeling for healthcare management system. / Nakhat, Khulood; Khalique, Fatima; Khan, Shoab Ahmed.
Proceedings of the 4th International Conference on Medical and Health Informatics, ICMHI 2020. Association for Computing Machinery, 2020. p. 37-44 (ACM International Conference Proceeding Series).

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

TY - GEN

T1 - Disease predictive modeling for healthcare management system

AU - Nakhat, Khulood

AU - Khalique, Fatima

AU - Khan, Shoab Ahmed

PY - 2020/8/14

Y1 - 2020/8/14

N2 - This study attempts to perform predictive analytics for decision makers in healthcare management systems using surveillance data from multiple sources for formulating intervention programs based on the results. With the availability of big data in health from multiple sources including electronic health records, it is possible to integrate data and perform near real-time predictive analysis for incoming streams of disease incidences. We use a temporal predictive Auto-Regressive Integrated Moving Averaging model (ARIMA) in combination with a minimum size moving window to forecast the disease incidences over a data collection and integration framework. We applied our model for predictive analysis of Hepatitis C incidences in Vehari District of Punjab province in Pakistan. Model performance is evaluated based on Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The model is capable of finding trends of any disease to aid timely decision making in the healthcare management context.

AB - This study attempts to perform predictive analytics for decision makers in healthcare management systems using surveillance data from multiple sources for formulating intervention programs based on the results. With the availability of big data in health from multiple sources including electronic health records, it is possible to integrate data and perform near real-time predictive analysis for incoming streams of disease incidences. We use a temporal predictive Auto-Regressive Integrated Moving Averaging model (ARIMA) in combination with a minimum size moving window to forecast the disease incidences over a data collection and integration framework. We applied our model for predictive analysis of Hepatitis C incidences in Vehari District of Punjab province in Pakistan. Model performance is evaluated based on Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The model is capable of finding trends of any disease to aid timely decision making in the healthcare management context.

KW - ARIMA

KW - Forecast

KW - Predictive analysis

KW - Public health management

KW - Stochastic modeling

KW - Time series

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DO - 10.1145/3418094.3418134

M3 - Conference contribution

AN - SCOPUS:85094919672

T3 - ACM International Conference Proceeding Series

SP - 37

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BT - Proceedings of the 4th International Conference on Medical and Health Informatics, ICMHI 2020

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Y2 - 14 August 2020 through 16 August 2020

ER -

Disease predictive modeling for healthcare management system

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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