New Research Agenda: Potentials Use of Machine Learning for Public Health

DOI

10.2991/ahsr.k.200215.028How to use a DOI?

Keywords

public health, health informatics, machine learning, sentiment analysis, risk prediction

Abstract

In the era of Industrial Revolution 4.0, the question is to what extent this development has been utilized in the field of public health? In fact, in this era there are a number of technological developments, especially information technology, among others, Big Data Analytics and Machine Learning that can be utilized in the field of public health. Public health surveillance that traditionally uses structured data, with the development of information technology today, the surveillance can use textual data. Using Machine Learning, the surveillance would be able to assess public opinion that are negative about a public health program, for example the immunization program. Machine Learning could also assist public health researchers to develop a model to predict the occurrence of certain ill health, which can then test the model with available data, regarding the accuracy of the model in predicting the event. The objectives of this paper are, firstly to provide some of the Faculty of Public Health University of Indonesia (FPHUI) achievements in utilizing the potentials of Machine Learning. Secondly, to identify the future Health Informatics research agenda, especially in the use of Machine Learning.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Kemal N. Siregar
AU  - Tris Eryando
AU  - Martya Rahmaniati
AU  - Indang Trihandini
AU  - Retnowati
PY  - 2020
DA  - 2020/02/22
TI  - New Research Agenda: Potentials Use of Machine Learning for Public Health
BT  - Proceedings of the 4th International Symposium on Health Research (ISHR 2019)
PB  - Atlantis Press
SP  - 142
EP  - 147
SN  - 2468-5739
UR  - https://doi.org/10.2991/ahsr.k.200215.028
DO  - 10.2991/ahsr.k.200215.028
ID  - Siregar2020
ER  -