the importance of machine learning in building public

The Importance of Machine Learning

in Building Public Health Intelligence

Dr. Kemal N. SiregarSenior Researcher, Health Informatics Research Cluster

Senior Lecturer, Faculty of Public Health Universitas IndonesiaE-mail address: [email protected]

2

We are drowning in information but starved for knowledge

John Naisbitt in his book Megatrends, 1984:

Structure of presentationCurrent Health Information System in IndonesiaIntroduction01

How strategic is the roles of Public Health Intelligence?Roles of Public Health Intelligence02

As to what extent the potential use of machine learning for public health?Potential Use of Machine Learning03

How does the Machine Learning process work?Machine Learning Process04

Experience of FPHUI using Machine Learning in PH Research

05

Conclusions06

Introduction:Current Health Information System (HIS)

in Indonesia

Current HIS in Indonesia

There have been developments in HIS components, but it has not been significant to support national health strategic decision making.

PROGRESS

Progress and Challenges

Limited resources for HIS, low data management capabilities, inadequate data quality and unintegrated data from various sources, no one data warehouse at the national level.

CHALLENGES

Source: Ministry of Health RI, 2017

HIS conditions in Indonesia

Current HIS in Indonesia

Is not ready for use in big data analytics

New research agenda: Potential use of machine

learning for public health

Whereas big data analytics including machine learning,

are needed to build PHI.

How strategic is the roles of Public Health Intelligence (PHI)?

Focus on both systems and processes

Draws on a multiplicity of information sources and types

Acts as a link between data analysis and action

Intelligence is generated through a synthesis of information

Spans the full intelligence ‘cycle’ or ‘continuum’

Many PHI definitions. However, the common features across all definitions are:

How strategic is the roles of Public Health Intelligence (PHI)?PHI in Indonesia must refer to the existing laws and health realities in IndonesiaPHI is to intelligence à Something very strategic

The data obtained must be able to be processed not only into information but further into knowledge and wisdom before being used for decision making.

§ Large-scale or national public health decisions, including to protect

people from infectious diseases that are very dangerous that could

weaken community resilience, for example related to bioterrorism (GHS).

§ Ensuring that every new-born baby is in good health

As to what extent the potentials of machine

learning for public health?

IR 4.0

Benefits of Big Data Analytics for Public Health1) Innovative prevention and health promotion, 2) Health decision making for a better future, 3) Efficiency in health costs

Example Use of Machine Learning in Public HealthAnalysing Facebook statuses and Twitter updates to monitor public attitudes toward online pharmacy, antibiotics, and other health issues. (Isah et al., 2014)

Using health record data to infer mortality, readmissions, length of stay and diagnoses from patients’ health records. (Rajkomar et al., 2018)

Identifying rodent species that can harbour undiscovered zoonotic pathogens using their biological, ecological, and geographical traits. (Han et al., 2015)

Analyzing the content of a tweet to identify whether its author is sick or healthy, and using its location metadata to identify potential disease hotspots. (Sadilek et al., 2012)

How does the Machine Learning process work?

Source: Predictive modeling, UPX academy (Siying Li, Kara Lily, 2018)

As to which extent the Faculty of Public Health University of Indonesia studied the roles of machine learning to be further developed for public health in Indonesia?

Establishment of Health Informatics Research Cluster (HIRC), end of 2017

1

A number of research articles produced during 2018-20192

A review is conducted: • 10 articles selected purposively which were

preliminary studies before using machine learning• 10 articles selected that had used the machine

learning method

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Overview of the Use of Machine Learning in Public Health Researches at FPH UI, 2018-2019

ParameterPreliminary Studies before Using

Machine Learning in Health Informatics Researches

Use of Machine Learning in Health Informatics Researches

Main Study Results Before Using Machine Learning

As to What Extent the Use of Machine Learning Method

• Searching for factors that need to be asked to determine someone is risky or not

• Prototyping android-based Personal Health Record

• Utilization of sentiment analysis to assess public opinion

• Use decision tree to determine whether someone is at risk or not

• Selection of accuracy test between Naïve Bayesian, Support Vector Machine, Neural Network

• Integration of machine learning into the applications (android- or web-based)

Source: Siregar, Kemal N. et al (2019) New Research Agenda: Potential Use of Machine Learning

Study at the stage of developing idea through literature study although at stage TRL 1, is very important before utilizing machine learning



Parameter

Preliminary Studies before Using Machine Learning in

Health Informatics Researches


Status of Research Some have been published in national or international journals

Some have been published in national or international journals

Each stage of study in the use of machine learning starting from the initial idea stage, modeling, until getting adequate accuracy for a prediction, deserve to be published both at national and international level.


ParameterPreliminary Studies before Using Machine Learning in

Health Informatics Researches


Other Information, Including Support for Research and TRL

Several types of research, namely post graduate research programs, doctoral research programs, where some have received funding from UI.

Several types of research, namely post graduate research programs, doctoral research programs, where some have received funding from UI. Technology Readiness Level from 2 to 6.


The prototype of the use of machine learning that was developed is still at an early stage and no one has tested it at the user level (TRL 7 and above)

Conclusions

• Based on the experience of health informatics research at FPH UI in the last 2

years, machine learning methods are very useful for recognizing patterns

(positive or negative) and making predictions of outcomes with high accuracy.

• This research experience

can be the basis for the

future research agenda in

public health field, so that

the machine learning

could be used further in

various management

decision-making,

programs and service

delivery care.

• It could further be used

for national level Public

Health Intelligence,

which is high level

decision making and

very strategic

Acknowledgment

• Appreciation to the Dean and leadership of FPH UI for supporting and launching the establishment of the Health Informatics Research Cluster (HIRC) in early 2018.

• Thank you to the Research Fellows of HIRC who were actively involved in various health informatics research activities during 2018-2019.

• Thank you very much goes to Miss Retnowatiand Miss Berly who were very helpful in preparing this presentation.

Key references

• London Health Observatory. (2006). Mapping health intelligence and its dissemination in London: Summary findings. Retrieved August 7, 2019, from http://www.lho.org.uk/viewResource.aspx?id=10898.

• Fu, P., Luck, J., & Protti, D. (2009). Information systems in support of public health in high-income countries. In R. Detels, R. Beaglehole, M. Lansang, & M. Gulliford (Eds.), Oxford textbook of public health (pp. 395–426). Oxford, England: Oxford University Press.

• Hendropriyono. (2013). Filsafat Intelijen. Jakarta: PT. Kompas Media Nusantara. ISBN 978-979-709-710-3

• Siregar Kemal N, et al. (2019). New Research Agenda: Potentials of Machine Learning for Public Health. Manuscript for The 4th Int’l Symposium on Health Research & 14th Nat’l Congress of IPHA

• Kementerian Kesehatan Republik Indonesia. (2017). Penilaian Sistem Informasi Kesehatandi Indonesia Tahun 2007, 2012, dan 2016 Pusat Data dan Informasi KementerianKesehatan Republik Indonesia.

• Siying Li, Kara Lily. (2018). “Certified fresh” lessons from machine learning. Retrieved from https://www.mawer.com/the-art-of-boring/blog/certified-fresh-lessons-from-machine-learning

http://www.lho.org.uk/viewResource.aspx?id=10898

Thank you

the importance of machine learning in building public

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