Inferring ADR causality by predicting the Naranjo Score from Clinical Notes. Rawat, B. P. S., Jagannatha, A., Liu, F., & Yu, H. In AMIA Fall Symposium, pages 1041–1049, 2020.
abstract   bibtex   
Clinical judgment studies are an integral part of drug safety surveillance and pharmacovigilance frameworks. They help quantify the causal relationship between medication and its adverse drug reactions (ADRs). To conduct such studies, physicians need to review patients’ charts manually to answer Naranjo questionnaire1. In this paper, we propose a methodology to automatically infer causal relations from patients’ discharge summaries by combining the capabilities of deep learning and statistical learning models. We use Bidirectional Encoder Representations from Transformers (BERT)2 to extract relevant paragraphs for each Naranjo question and then use a statistical learning model such as logistic regression to predict the Naranjo score and the causal relation between the medication and an ADR. Our methodology achieves a macro-averaged f1-score of 0.50 and weighted f1-score of 0.63.
@inproceedings{rawat_inferring_2020,
	title = {Inferring {ADR} causality by predicting the {Naranjo} {Score} from {Clinical} {Notes}},
	abstract = {Clinical judgment studies are an integral part of drug safety surveillance and pharmacovigilance frameworks. They help quantify the causal relationship between medication and its adverse drug reactions (ADRs). To conduct such studies, physicians need to review patients’ charts manually to answer Naranjo questionnaire1. In this paper, we propose a methodology to automatically infer causal relations from patients’ discharge summaries by combining the capabilities of deep learning and statistical learning models. We use Bidirectional Encoder Representations from Transformers (BERT)2 to extract relevant paragraphs for each Naranjo question and then use a statistical learning model such as logistic regression to predict the Naranjo score and the causal relation between the medication and an ADR. Our methodology achieves a macro-averaged f1-score of 0.50 and weighted f1-score of 0.63.},
	booktitle = {{AMIA} {Fall} {Symposium}},
	author = {Rawat, Bhanu Pratap Singh and Jagannatha, Abhyuday and Liu, Feifan and Yu, Hong},
	year = {2020},
	pmcid = {PMC8075501},
	pmid = {33936480},
	pages = {1041--1049},
}

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