Machine Learning Approach to Facilitate Knowledge Synthesis at the Intersection of Liver Cancer, Epidemiology, and Health Disparities Research.

PURPOSE: Liver cancer is a global challenge, and disparities exist across multiple domains and throughout the disease continuum. However, liver cancer's global epidemiology and etiology are shifting, and the literature is rapidly evolving, presenting a challenge to the synthesis of knowledge needed to identify areas of research needs and to develop research agendas focusing on disparities. Machine learning (ML) techniques can be used to semiautomate the literature review process and improve efficiency. In this study, we detail our approach and provide practical benchmarks for the development of a ML approach to classify literature and extract data at the intersection of three fields: liver cancer, health disparities, and epidemiology. METHODS: We performed a six-phase process including: training (I), validating (II), confirming (III), and performing error analysis (IV) for a ML classifier. We then developed an extraction model (V) and applied it (VI) to the liver cancer literature identified through PubMed. We present precision, recall, F1, and accuracy metrics for the classifier and extraction models as appropriate for each phase of the process. We also provide the results for the application of our extraction model. RESULTS: With limited training data, we achieved a high degree of accuracy for both our classifier and for the extraction model for liver cancer disparities research literature performed using epidemiologic methods. The disparities concept was the most challenging to accurately classify, and concepts that appeared infrequently in our data set were the most difficult to extract. CONCLUSION: We provide a roadmap for using ML to classify and extract comprehensive information on multidisciplinary literature. Our technique can be adapted and modified for other cancers or diseases where disparities persist.

Participant use and communication of findings from exome sequencing: a mixed-methods study.

PURPOSE: This study investigated how genome sequencing results affect health behaviors, affect, and communication. METHODS: We report on 29 participants who received a sequence result in the ClinSeq study, a cohort of well-educated, postreproductive volunteers. A mixed-methods design was used to explore respondents' use, communication, and perceived utility of results. RESULTS: Most participants (72%) shared their result with at least one health-care provider, and 31% reported subsequent changes in the health care they received. Participants scored high on the Positive Experiences subscale and low on the Distress subscale of a modified version of the Multidimensional Impact of Cancer Risk Assessment. The majority (93%) shared their result with at least one family member. Participants described deriving personal utility from their results. CONCLUSION: This article is the first to describe research participants' reactions to actionable sequencing results. Our findings suggest clinical and personal benefit from receiving sequencing results, both of which may contribute to improved health for the recipients. Given the participants' largely positive or neutral affective responses and disclosure of their results to physicians and relatives, health-care providers should redirect concern from the potential for distress and attend to motivating patients to follow their medical recommendations.Genet Med 18 6, 577-583.