Automating Systematic Reviews of Environmental Health Literature with Machine Learning

项目摘要 IDOC Software建议开发人工智能(AI)算法，并提供用户友好 促进环境领域系统评审(SRS)有效产生的软件 健康(EH)。SR是评估可用于各种决策的证据的“黄金标准” 卫生背景，包括卫生保健、公共卫生和环境卫生。 SRS根据所做的决定合成符合资格标准的研究的证据(例如 危险识别或风险评估)。所有相关研究都需要在SR中考虑，这意味着所有 可能相关的文章必须逐一进行评估。例如，如果SR问题仅与 40-65岁的女性，那么包含男性或包含该年龄范围以外的女性的研究必须是 排除在用于得出结论的最后一组文章之外。筛查所涉及的时间(和费用) 数以千计的潜在引用是巨大的，通常需要一个筛选团队几个月的时间才能完成。这 严重限制了可以进行的SRS的数量，并威胁到政策制定者的及时决策。 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SRS的进行 然而，在资格标准方面存在重大挑战。在EH领域，同样的研究人群， 暴露和健康结果可以用许多不同的词语和短语组合来描述。它 对于AI算法来说，很难以克服语言固有的复杂性的方式概括语言 这些科学交流。 IDOC软件已经开发出能够推断单词和短语之间联系的算法。 这些习得的联系是围绕EH框架或本体形成的，称为PECO：Popular， 曝光率、比较器和结果。该软件将一篇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。这样，筛选器就不需要阅读 整篇文章，以确定它是否符合资格标准。取而代之的是，屏幕扫描“P”颜色的单词以 确定所研究的人口是否符合“P”纳入标准。那么“E”颜色的单词就可以 评估，以此类推。这加快了筛选者对文章进行评估的速度。 人工智能算法面临的挑战是找到所有的PECO单词和短语并准确地分类 他们。高精确度需要考虑单词和 短语。文章标题第一阶段在机器学习和自然语言处理方面取得的进展 和摘要，然后是第二阶段的文章全文，这将导致更有效地进行工作人员代表会议， 减少成本和时间，从而促进及时作出有证据的决定和 保护公众健康免受不安全环境暴露的政策。

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.