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A Roadmap to Artificial Intelligence (AI): Methods for Designing and Building AI ready Data for Women’s Health Studies
OBJECTIVES: Evaluating methods for building data frameworks for application of AI in large scale datasets for women’s health studies. METHODS: We created methods for transforming raw data to a data framework for applying machine learning (ML) and natural language processing (NLP) techniques for pred...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312839/ https://www.ncbi.nlm.nih.gov/pubmed/37398113 http://dx.doi.org/10.1101/2023.05.25.23290399 |
Sumario: | OBJECTIVES: Evaluating methods for building data frameworks for application of AI in large scale datasets for women’s health studies. METHODS: We created methods for transforming raw data to a data framework for applying machine learning (ML) and natural language processing (NLP) techniques for predicting falls and fractures. RESULTS: Prediction of falls was higher in women compared to men. Information extracted from radiology reports was converted to a matrix for applying machine learning. For fractures, by applying specialized algorithms, we extracted snippets from dual x-ray absorptiometry (DXA) scans for meaningful terms usable for predicting fracture risk. DISCUSSION: Life cycle of data from raw to analytic form includes data governance, cleaning, management, and analysis. For applying AI, data must be prepared optimally to reduce algorithmic bias. CONCLUSION: Algorithmic bias is harmful for research using AI methods. Building AI ready data frameworks that improve efficiency can be especially valuable for women’s health. |
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