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However, the utility of genetic information in the\u00a0clinical decision-making process has not been examined extensively from a real-world, data-driven perspective. Through mining real-world data (RWD) from clinical notes, we could extract patients\u2019 genetic information and further associate treatment decisions with genetic information.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We proposed a real-world evidence (RWE) study framework that incorporates context-based natural language processing (NLP) methods and data quality examination before final association analysis. The framework was demonstrated in a Foundation-tested women cancer cohort (N\u2009=\u2009196). Upon retrieval of patients\u2019 genetic information using NLP system, we assessed the completeness of genetic data captured in unstructured clinical notes according to a genetic data-model. We examined the distribution of different topics regarding\n                      <jats:italic>BRCA1\/2<\/jats:italic>\n                      throughout patients\u2019 treatment process, and then analyzed the association between\n                      <jats:italic>BRCA1\/2<\/jats:italic>\n                      mutation status and the discussion\/prescription of targeted therapy.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      We identified seven topics in the\u00a0clinical context of genetic mentions including: Information, Evaluation, Insurance, Order, Negative, Positive, and Variants of unknown significance. Our rule-based system achieved a precision of 0.87, recall of 0.93 and F-measure of 0.91. Our machine learning system achieved a precision of 0.901, recall of 0.899 and F-measure of 0.9 for four-topic classification and a precision of 0.833, recall of 0.823 and F-measure of 0.82 for seven-topic classification. We found in result-containing sentences, the\u00a0capture of\n                      <jats:italic>BRCA1\/2<\/jats:italic>\n                      mutation information was 75%, but detailed variant information (e.g. variant types) is largely missing. Using cleaned RWD, significant associations were found between\n                      <jats:italic>BRCA1\/2<\/jats:italic>\n                      positive mutation and targeted therapies.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>In conclusion, we demonstrated a framework to generate RWE using RWD from different clinical sources. Rule-based NLP system achieved the best performance for resolving contextual variability when extracting RWD from unstructured clinical notes. Data quality issues such as incompleteness and discrepancies exist thus manual data cleaning is needed before further analysis can be performed. Finally, we were able to use cleaned RWD to evaluate the\u00a0real-world utility of genetic information to initiate a prescription of targeted therapy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-020-01364-y","type":"journal-article","created":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T09:08:54Z","timestamp":1609924134000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Generating real-world evidence from unstructured clinical notes to examine clinical utility of genetic tests: use case in BRCAness"],"prefix":"10.1186","volume":"21","author":[{"given":"Yiqing","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Saravut J.","family":"Weroha","sequence":"additional","affiliation":[]},{"given":"Ellen L.","family":"Goode","sequence":"additional","affiliation":[]},{"given":"Hongfang","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2638-3081","authenticated-orcid":false,"given":"Chen","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,1,6]]},"reference":[{"issue":"6178","key":"1364_CR1","doi-asserted-by":"publisher","first-page":"1466","DOI":"10.1126\/science.1251827","volume":"343","author":"FJ Couch","year":"2014","unstructured":"Couch FJ, Nathanson KL, Offit K. 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