{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T18:23:17Z","timestamp":1790792597132,"version":"4.1.0"},"reference-count":42,"publisher":"JMIR Publications Inc.","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Med Inform"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Adverse drug events (ADEs) are unintended side effects of drugs that cause substantial clinical and economic burdens globally. Not all ADEs are discovered during clinical trials; therefore, postmarketing surveillance, called pharmacovigilance, is routinely conducted to find unknown ADEs. A wealth of information, which facilitates ADE discovery, lies in the growing body of biomedical literature. Knowledge graphs (KGs) encode information from the literature, where the vertices and the edges represent clinical concepts and their relations, respectively. The scale and unstructured form of the literature necessitates the use of natural language processing (NLP) to automatically create such KGs. Previous studies have demonstrated the utility of such literature-derived KGs in ADE prediction. Through unsupervised learning of the representations (features) of clinical concepts from the KG, which are used in machine learning models, state-of-the-art results for ADE prediction were obtained on benchmark data sets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Due to the use of NLP to infer literature-derived KGs, there is noise in the form of false positive (erroneous) and false negative (absent) nodes and edges. Previous representation learning methods do not account for such inaccuracies in the graph. NLP algorithms can quantify the confidence in their inference of extracted concepts and relations from the literature. Our hypothesis, which motivates this work, is that by using such confidence scores during representation learning, the learned embeddings would yield better features for ADE prediction models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed methods to use these confidence scores on two well-known representation learning methods\u2014DeepWalk and Translating Embeddings for Modeling Multi-relational Data (TransE)\u2014to develop their weighted versions: Weighted DeepWalk and Weighted TransE. These methods were used to learn representations from a large literature-derived KG, the Semantic MEDLINE Database, which contains more than 93 million clinical relations. They were compared with Embedding of Semantic Predications, which, to our knowledge, is the best reported representation learning method using the Semantic MEDLINE Database with state-of-the-art results for ADE prediction. Representations learned from different methods were used (separately) as features of drugs and diseases to build classification models for ADE prediction using benchmark data sets. The methods were compared rigorously over multiple cross-validation settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The weighted versions we designed were able to learn representations that yielded more accurate predictive models than the corresponding unweighted versions of both DeepWalk and TransE, as well as Embedding of Semantic Predications, in our experiments. There were performance improvements of up to 5.75% in the F1-score and 8.4% in the area under the receiver operating characteristic curve value, thus advancing the state of the art in ADE prediction from literature-derived KGs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our classification models can be used to aid pharmacovigilance teams in detecting potentially new ADEs. Our experiments demonstrate the importance of modeling inaccuracies in the inferred KGs for representation learning.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/32730","type":"journal-article","created":{"date-parts":[[2021,9,18]],"date-time":"2021-09-18T14:48:46Z","timestamp":1631976526000},"page":"e32730","source":"Crossref","is-referenced-by-count":22,"title":["Adverse Drug Event Prediction Using Noisy Literature-Derived Knowledge Graphs: Algorithm Development and Validation"],"prefix":"10.2196","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0522-9889","authenticated-orcid":false,"given":"Soham","family":"Dasgupta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5658-0724","authenticated-orcid":false,"given":"Aishwarya","family":"Jayagopal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7503-0624","authenticated-orcid":false,"given":"Abel Lim","family":"Jun Hong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7242-0966","authenticated-orcid":false,"given":"Ragunathan","family":"Mariappan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6748-6864","authenticated-orcid":false,"given":"Vaibhav","family":"Rajan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2021,10,25]]},"reference":[{"issue":"6","key":"ref1","first-page":"340","volume":"43","author":"Ventola, CL","year":"2018","journal-title":"P T"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1177\/1060028018765159"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2017.5150"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.18632\/oncotarget.16723"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2015.13766"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1126\/scitranslmed.3003377"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.tips.2013.01.006"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/s40264-012-0002-x"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1002\/psp4.59"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbx010"},{"key":"ref11","unstructured":"MEDLINE: OverviewNational Library of Medicine2021-09-24https:\/\/www.nlm.nih.gov\/medline\/medline_overview.html"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2012.07.003"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/s40264-014-0218-z"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1136\/amiajnl-2011-000699"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocy077"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/6.4.357"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-10-311"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2003.11.003"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"ref20","unstructured":"BordesAUsunierNGarcia-DuranAWestonJYakhnenkoOTranslating embeddings for modelling multi-relational dataProceedings of the 26th International Conference on Neural Information Processing Systems2013NIPS'13: 26th International Conference on Neural Information Processing SystemsDecember 5 - 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