Home > Published Issues > 2020 > Volume 11, No. 4, November 2020 >

Integration of Existing HIS with Deep Neural Network for Predicting Medicine Needs by Using Scheduled Job

I Putu Arya Dharmaadi and I Made Sukarsa
Department of Information Technology, Udayana University, Bali, Indonesia

Abstract—Most hospitals have used a Hospital Information System (HIS) to help doctors, nurses, and staff in giving fast and excellent services. The data recorded by the system, such as diagnoses, medicines, and billing can be processed further to produce useful information. For example, medicine transaction records during a year can be analyzed to find out how many medical materials needed for tomorrow. This information can prevent the hospitals from scarcity of medicines and other medical materials. Considering its convenience and performance, in order to learn and analyze the data, most researchers used Python libraries that are very handy to run complex mathematical calculations. Therefore, this research develop an integration system to combine existing HIS implemented in hospitals, usually written in PHP code, with the need prediction module that has applied a deep neural network model using Python libraries. Rather than allowing direct call schemes, PHP and Python are interconnected through scheduled job schemes. Finally, the integration system is able to run properly and produce accurate prediction without bother HIS implementation.
Index Terms—deep neural network, integration, python, scheduled job

Cite: I Putu Arya Dharmaadi and I Made Sukarsa, "Integration of Existing HIS with Deep Neural Network for Predicting Medicine Needs by Using Scheduled Job," Journal of Advances in Information Technology, Vol. 11, No. 4, pp. 271-276, November 2020. doi: 10.12720/jait.11.4.271-276

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.