Knowledge Engine- A computational approach to combatting dermatological health disparities in underserved communities

知识引擎 - 一种解决服务不足社区皮肤病健康差异的计算方法

• 批准号: 10697912
• 负责人: Tiffany St. Bernard
• 金额: $ 25.96万
• 依托单位: HAIRDAYS, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10697912
• 关键词: Address; Affect; Age; Algorithms; Allergic Contact Dermatitis; Allergic Reaction; Alopecia; Artificial Intelligence; Attitude; Beauty; Belief; Big Data; Black Populations; Black race; Breast Cancer Risk Factor; Bulla; Burn injury; Caring; Chemical Exposure; Chemicals; Cicatrix; Clinical Research; Communities; Conscious; Contact Dermatitis; Data; Databases; Democracy; Dermatologic; Development; Diagnosis; Disease; Eczema; Endocrine Disruptors; Exanthema; Exclusion; Exposure to; Feedback; Frequencies; Goals; Hair; Hair Dyes; Hair shaft structure; Health; Health Personnel; Healthcare; Hormones; Improve Access; Individual; Industry; Infertility; Insurance; Intelligence; Investigation; Irritant Dermatitis; Journals; Knowledge; Learning; Link; Lotion; Machine Learning; Mainstreaming; Maps; Marketing; Mediating; Menstruation; Natural Language Processing; Oils; Pain; Parabens; Pathology; Patient-Focused Outcomes; Persons; Phase; Polishes; Pregnant Women; Pruritus; Puberty; Recommendation; Regimen; Reporting; Research; Risk; Scalp structure; Science; Skin; Source; Thinness; Uterine Fibroids; Woman; black women; estrogenic; health data; health disparity; high risk; immunoreaction; insight; knowledge graph; multimodality; phthalates; poor health outcome; premature; product development; reproductive; research and development; satisfaction; side effect; skin irritation; stem; structured data; telehealth; tool; underserved community; unstructured data; working group

Abstract - Hair products marketed towards Black women contain a disproportionate amount of toxic or allergenic ingredients, and research suggests that chemical exposure from these products may contribute to health disparities in Black women. Illustrating this, a study of 18 mainstream products commonly used by Black women found that 100% of products contained artificial fragrances known to cause multiple conditions including skin irritation and eczema, while 72% of products contained methylparaben, an ingredient associated with altered hormone levels in pregnant women, estrogenic and adipogenic activity, as well as an increased risk of pruritus (itchy skin) in Black women. Other poor health outcomes linked to hair product use include contact dermatitis, and hair loss—affecting an estimated 50% of Black women—as well as elevated risks for breast cancer, premature puberty, reproductive harm, and other hormone-mediated diseases and conditions. Exposure to exogenous hormones and endocrine-disrupting chemicals (EDCs) has also been reported as potentially contributing to cases of early menstruation, uterine fibroids, and infertility. Notably, Black women of reproductive age have been found to have higher levels EDCs in their bodies (e.g., parabens, phthalates) compared to white women of the same age. It has been postulated that the elevated risks observed among Black women may owe to both higher concentrations of EDCs in products marketed to them and higher frequencies of application, but more research is needed to clarify potential links, particularly as Black hair and skin have largely been excluded from R&D for product development and clinical research. Addressing the need for further investigation into risks associated with ingredients in hair products, best practices for safer use, and appropriate avenues for knowledge sharing within the community, HairDays proposes a multi-modal approach applying artificial intelligence (AI) tools including machine learning (ML) and natural language processing (NLP) to deliver a first-in- kind hair intelligence platform that will provide ingredient transparency, offer data-driven hair care recommendations, and present culturally conscious insights to promote safer practices. Product ingredients will be assessed using structured and unstructured data mined from scientific journals and hair care/science databases, as well as product reviews to understand the relationships between factors such as ingredients, frequency of use, hair profile, and user satisfaction. We will use these knowledge gains to generate insights on ingredient risk and corresponding product recommendations, which will then be disseminated to affected communities through the HairDays platform. Through successful development of the envisioned platform, we aim to mitigate scalp, hair, and related pathologies (e.g., potential breast cancer risk) stemming from ill-matched products and high-risk ingredients. Phase I Specific Aims are as follows: 1) Develop hair knowledge engine using NLP and AI to extract and map product and ingredient data; and 2) Assess impact of user engagement on attitudes and beliefs about product selection and usage.

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