Automating Systematic Reviews of Environmental Health Literature with Machine Learning

利用机器学习自动系统评价环境健康文献

• 批准号: 10378843
• 负责人: Eitan Agai
• 金额: $ 25.6万
• 依托单位: IDOC SOFTWARE, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2023-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10378843
• 关键词: Age; Air Pollution; Algorithms; Artificial Intelligence; Categories; Code; Collaborations; Color; Communication; Computer software; Country; Databases; Decision Making; Development; Eligibility Determination; Environmental Exposure; Environmental Health; Ethnic Origin; Exposure to; Funding; Gender; Generations; Goals; Gold; Hazard Identification; Health; Healthcare; Human; Human Milk; Infant; Intervention; Investments; Language; Life; Literature; Machine Learning; Maps; Measures; Medical; Mothers; Natural Language Processing; Older Population; Ontology; Outcome; Particulate; Pharmaceutical Preparations; Phase; Policies; Policy Maker; Population; Population Study; Procedures; Production; Public Health; Race; Research Personnel; Resources; Risk Assessment; Savings; Scanning; Small Business Innovation Research Grant; Speed; Subcategory; Taxonomy; Techniques; Testing; Text; Time; Toxin; Training; Trees; Woman; algorithm training; alpha Toxin; artificial intelligence algorithm; base; cost; crowdsourcing; evidence base; falls; human old age (65+); inclusion criteria; men; people of color; phrases; screening; study population; systematic review; tool; user friendly software; user-friendly; web site

Project Summary IDOC Software proposes the development of Artificial Intelligence (AI) algorithms, together with user-friendly software, for facilitating the efficient production of Systematic Reviews (SRs) in the field of Environmental Health (EH). SR is the “Gold Standard” for assessing evidence to be used for decision making in a variety of health contexts, including health care, public health and environmental health. SRs synthesize evidence from studies that meet eligibility criteria based on the decision being made (such as hazard identification or risk assessment). All relevant studies need to be considered in an SR, meaning that all of the potentially related articles must be evaluated one by one. For example, if the SR question relates only to 40-65 year old women, then studies containing men or containing women outside this age range must be excluded from the final set of articles used to draw a conclusion. The time (and expense) involved in screening potentially thousands of citations is substantial, often taking a team of screeners months to complete. This severely limits the numbers of SRs that can be conducted and threatens timely decisions by policy makers. AI has tremendous potential to accelerate the conduct of SRs by automatically recognizing words that relate to eligibility criteria, however there are significant challenges. In the field of EH the same study populations, exposures, and health outcomes can be described with many different combinations of words and phrases. It is difficult for AI algorithms to generalize language in the way needed to overcome the complexity inherent in these scientific communications. IDOC Software has developed algorithms capable of deducing connections between words and phrases. These learned connections are formed around a EH framework, or ontology, known as PECO: Population, Exposure, Comparator, and Outcome. The software maps key words and phrases in an article onto these categories and then highlights these terms in the article text via color-coding. A screener then need not read an entire article to determine if it meets the eligibility criteria. Instead, the screener scans the “P” colored words to determine if the population studied meets the “P” inclusion criteria. Then the “E” colored words can be evaluated, and so on. This accelerates the rate at which a screener can evaluate articles manyfold. The challenge for the AI algorithms is to then find all the PECO words and phrases and accurately categorize them. High accuracy requires taking into account causal and other relationships between the words and phrases. Advances in machine learning and natural language processing achieved in Phase I on article titles and abstracts, and then on the full text of articles in Phase II, will result in more efficient conduct of SRs, reducing costs and time, and thereby furthering the goal of making timely evidence-informed decisions and policy to protect public health from unsafe environmental exposures.

项目摘要 IDOC软件提出了人工智能（AI）算法的开发，以及用户友好的 软件，以促进环境领域的系统审评（SR）的有效生产 健康（EH）。SR是评估用于各种决策的证据的“黄金标准”， 卫生方面，包括保健、公共卫生和环境卫生。 工作人员代表根据正在作出的决定，综合来自符合资格标准的研究的证据（如 危害识别或风险评估）。所有相关研究都需要在SR中考虑，这意味着所有 必须逐一评估潜在相关文章。例如，如果SR问题仅涉及 40-65岁的女性，则包含该年龄范围以外的男性或女性的研究必须 从用于得出结论的最终一组文章中排除。筛选所需的时间（和费用） 潜在的数千次引用是相当可观的，通常需要一个筛选团队数月才能完成。这 严重限制了可以进行的工作人员代表的数量，并威胁到决策者的及时决策。 人工智能具有巨大的潜力，可以通过自动识别与以下内容相关的单词来加速SR的行为 然而，在资格标准方面存在重大挑战。在EH领域，相同的研究人群， 暴露和健康结果可以用许多不同的单词和短语组合来描述。它 人工智能算法很难以克服语言固有的复杂性所需的方式来概括语言， 这些科学交流。 IDOC软件开发了能够推断单词和短语之间联系的算法。 这些学习到的联系是围绕EH框架或本体形成的，称为PECO：人口， 暴露、对照药物和结局。该软件将文章中的关键词和短语映射到这些 分类，然后通过颜色编码在文章文本中突出显示这些术语。然后，筛选者不需要阅读 以确定其是否符合资格标准。相反，筛选器扫描“P”颜色的单词， 确定研究人群是否符合“P”入选标准。那么“E”色的单词就可以 这加快了筛选器可以多次评估文章的速度。 人工智能算法面临的挑战是找到所有的PECO单词和短语并准确分类 他们高准确性要求考虑到单词之间的因果关系和其他关系， 短语第一阶段在文章标题方面取得的机器学习和自然语言处理方面的进展 和摘要，然后是第二阶段的文章全文，将导致更有效地进行工作人员代表， 减少成本和时间，从而进一步实现及时做出循证决策的目标， 保护公众健康免受不安全环境影响的政策。