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

摘要- 销售给黑人女性的美发产品含有不成比例的有毒或致敏物质 成分，研究表明，从这些产品中接触化学物质可能有助于健康 黑人女性的不平等。为了说明这一点，一项对黑人女性常用的18种主流产品的研究 发现100%的产品含有人造香水，已知会导致多种疾病，包括皮肤 刺激和湿疹，而72%的产品含有对羟基苯甲酸甲酯，一种与改变 孕妇的激素水平、雌激素和成脂活动以及瘙痒风险的增加 (皮肤发痒)黑人女性。与使用头发产品有关的其他不良健康后果包括接触性皮炎， 和脱发--估计有50%的黑人女性受到影响--以及乳腺癌风险的增加。 青春期过早、生殖损害以及其他由激素引起的疾病和状况。暴露于 据报道，外源性激素和内分泌干扰物(EDCs)也可能 导致月经早、子宫肌瘤和不孕症的病例。值得注意的是，有生育能力的黑人女性 研究发现，与白人相比，老年人体内的内分泌细胞(如对羟基苯甲酸酯、邻苯二甲酸酯)水平更高 同龄的女性。据推测，在黑人女性中观察到的风险上升可能是 无论是向他们销售的产品中更高浓度的EDCs，还是更高的应用频率，但 需要更多的研究来澄清潜在的联系，特别是在黑发和皮肤基本上被排除在外的情况下 从研发到产品开发和临床研究。处理对风险进行进一步调查的必要性 与头发产品中的成分相关，安全使用的最佳实践，以及适当的知识途径 在社区内共享，HairDays提出了一种应用人工智能的多模式方法 (AI)包括机器学习(ML)和自然语言处理(NLP)在内的工具，以提供领先的 Kind头发智能平台，将提供成分透明度，提供数据驱动的头发护理 建议，并提出具有文化意识的见解，以促进更安全的做法。产品 将使用从科学期刊和头发中挖掘的结构化和非结构化数据来评估成分 关怀/科学数据库，以及产品评论，以了解以下因素之间的关系 成分、使用频率、头发轮廓和用户满意度。我们将利用这些知识收益来产生 对成分风险的见解和相应的产品建议，然后将其传播到 通过HairDays平台受影响的社区。通过成功开发预想的 平台，我们的目标是减轻头皮、头发和相关的病理(例如，潜在的乳腺癌风险)的根源 来自不匹配的产品和高风险成分。第一阶段的具体目标如下：1)发展头发 知识引擎使用NLP和AI来提取和映射产品和配料数据；以及2)评估 用户对产品选择和使用的态度和信念的参与。

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.