Duksung Women's University undergraduates publish AI maternal mortality study

An undergraduate research team from Duksung Women's University has achieved a significant academic milestone by publishing their study on predicting maternal mortality rates using artificial intelligence in the international journal Mathematics. The research, conducted under the guidance of Professor Moon Ji-hoon, was part of the university's 'Professor-Learner Collaboration Project.'

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This achievement is the result of students applying knowledge learned in classes and extracurricular programs to solve actual social and industrial problems.

The study, titled "Missingness-Aware TabNet: Handling Structural Missing Data for the Interpretable Prediction of Global Maternal Mortality," addresses a critical global health issue aligned with UN Sustainable Development Goal 3.1, which aims to reduce maternal mortality. The team, comprising undergraduate students Yoo Si-yeon, Moon Young-shin, Lee Ga-eun, and Lee Yu-rim from the Data Science department, utilized AI and data science methodologies to analyze complex global health data.

A key aspect of their research involved handling missing data, which is common in global health statistics. Instead of treating these gaps as mere errors, the team interpreted them as reflections of a country's statistical reporting systems and health administration environments. This nuanced approach differentiated their work and highlighted the potential of AI in providing interpretable predictions for global health challenges.

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This achievement shows the possibility that even undergraduate students can produce world-class research results through systematic guidance and active research participation.

Maternal mortality is a crucial indicator of a nation's healthcare accessibility, safety of pregnancy and childbirth, women's health rights, and public health administration capacity. The university highlighted this achievement as a testament to how AI and data science can contribute to solving global health problems and informing data-driven policy-making. The success also underscores the effectiveness of the university's educational innovation programs in fostering research capabilities among undergraduate students.

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This achievement is an excellent case study of strengthening undergraduate AI research capabilities and creating research outcomes based on extracurricular activities.