Abstract
Background: Every year millions of people migrate across international borders from country to country capturing the attention of governments. While this movement has led to the development of many new international policies and programs that help assist these migrants, still a lot needs to be done to plug the unmet sexual and reproductive health needs of adolescent migrants who are mostly dependent on their families financially and socially and often fall through the cracks of the system. Objective: In order to create new policies and programs, legislators and other government workers must collect extensive data about these migrants to find out more information regarding the reasons for their migration and their immediate health needs in the process of migration for key decision-making. This study explores ways of getting relevant data from the migrants and apply machine learning to derive insights from the data for the stakeholders. Methods: To solve this problem, we have created a web application that will facilitate crucial data collection. Additionally, we have mocked up a data driven recommendation system about predicting most vulnerable migrants. This could help different stakeholders involved in the sexual and reproductive health of youth migrants in clinical decision-making. Results: The study involved building a web-app and curation of a questionnaire for the migrants to build a pipeline of data that could be later used for deriving insights about the patterns in migration and its potential sexual and health risks. It has also explored different ways of disseminating information about sexual and reproductive health needs to the youth migrants. Finally, machine learning was used for predicting vulnerability of migrants based on their backgrounds. Conclusion: Data is of great essence in mitigating the risks associated with various sexual and reproductive health related issues among migrants. First, it can be used to make youth migrants aware of their sexual and reproductive health needs and rights. Second, it can be used by Machine Learning to generate useful recommendations for reducing the risks of migration.
Highlights
IntroductionIn the past few decades, people have increasingly moved across borders due to compelling political, social and economic circumstances or in search of a better future for themselves as well as their families
We have discussed different ways in which we could provide data driven recommendations to different stakeholders that play a role in Sexual and Reproductive Health Among Youth Migrants
For addressing problems such as the current one, where machine learning can only be used when there is data, we have shown that such a cold-start can be addressed by artificially curating the data using rules
Summary
In the past few decades, people have increasingly moved across borders due to compelling political, social and economic circumstances or in search of a better future for themselves as well as their families. Every year millions of people migrate across international borders from country to country capturing the attention of governments While this movement has led to the development of many new international policies and programs that help assist these migrants, still a lot needs to be done to plug the unmet sexual and reproductive health needs of adolescent migrants who are mostly dependent on their families financially and socially and often fall through the cracks of the system. Results: The study involved building a web-app and curation of a questionnaire for the migrants to build a pipeline of data that could be later used for deriving insights about the patterns in migration and its potential sexual and health risks It has explored different ways of disseminating information about sexual and reproductive health needs to the youth migrants.
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