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      Innovation in Pharmacovigilance: Use of Artificial Intelligence in Adverse Event Case Processing

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          Automation of pharmaceutical safety case processing represents a significant opportunity to affect the strongest cost driver for a company's overall pharmacovigilance budget. A pilot was undertaken to test the feasibility of using artificial intelligence and robotic process automation to automate processing of adverse event reports. The pilot paradigm was used to simultaneously test proposed solutions of three commercial vendors. The result confirmed the feasibility of using artificial intelligence–based technology to support extraction from adverse event source documents and evaluation of case validity. In addition, the pilot demonstrated viability of the use of safety database data fields as a surrogate for otherwise time‐consuming and costly direct annotation of source documents. Finally, the evaluation and scoring method used in the pilot was able to differentiate vendor capabilities and identify the best candidate to move into the discovery phase.

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          Most cited references 20

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          Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features

          Objective Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks, particularly for pharmacovigilance, via the use of natural language processing (NLP) techniques. However, the language in social media is highly informal, and user-expressed medical concepts are often nontechnical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and thus far, advanced machine learning-based NLP techniques have been underutilized. Our objective is to design a machine learning-based approach to extract mentions of adverse drug reactions (ADRs) from highly informal text in social media. Methods We introduce ADRMine, a machine learning-based concept extraction system that uses conditional random fields (CRFs). ADRMine utilizes a variety of features, including a novel feature for modeling words’ semantic similarities. The similarities are modeled by clustering words based on unsupervised, pretrained word representation vectors (embeddings) generated from unlabeled user posts in social media using a deep learning technique. Results ADRMine outperforms several strong baseline systems in the ADR extraction task by achieving an F-measure of 0.82. Feature analysis demonstrates that the proposed word cluster features significantly improve extraction performance. Conclusion It is possible to extract complex medical concepts, with relatively high performance, from informal, user-generated content. Our approach is particularly scalable, suitable for social media mining, as it relies on large volumes of unlabeled data, thus diminishing the need for large, annotated training data sets.
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            Learning probabilistic phenotypes from heterogeneous EHR data

            We present the Unsupervised Phenome Model (UPhenome), a probabilistic graphical model for large-scale discovery of computational models of disease, or phenotypes. We tackle this challenge through the joint modeling of a large set of diseases and a large set of clinical observations. The observations are drawn directly from heterogeneous patient record data (notes, laboratory tests, medications, and diagnosis codes), and the diseases are modeled in an unsupervised fashion. We apply UPhenome to two qualitatively different mixtures of patients and diseases: records of extremely sick patients in the intensive care unit with constant monitoring, and records of outpatients regularly followed by care providers over multiple years. We demonstrate that the UPhenome model can learn from these different care settings, without any additional adaptation. Our experiments show that (i) the learned phenotypes combine the heterogeneous data types more coherently than baseline LDA-based phenotypes; (ii) they each represent single diseases rather than a mix of diseases more often than the baseline ones; and (iii) when applied to unseen patient records, they are correlated with the patients' ground-truth disorders. Code for training, inference, and quantitative evaluation is made available to the research community.
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              Extracting drug-drug interactions from literature using a rich feature-based linear kernel approach.

              Identifying unknown drug interactions is of great benefit in the early detection of adverse drug reactions. Despite existence of several resources for drug-drug interaction (DDI) information, the wealth of such information is buried in a body of unstructured medical text which is growing exponentially. This calls for developing text mining techniques for identifying DDIs. The state-of-the-art DDI extraction methods use Support Vector Machines (SVMs) with non-linear composite kernels to explore diverse contexts in literature. While computationally less expensive, linear kernel-based systems have not achieved a comparable performance in DDI extraction tasks. In this work, we propose an efficient and scalable system using a linear kernel to identify DDI information. The proposed approach consists of two steps: identifying DDIs and assigning one of four different DDI types to the predicted drug pairs. We demonstrate that when equipped with a rich set of lexical and syntactic features, a linear SVM classifier is able to achieve a competitive performance in detecting DDIs. In addition, the one-against-one strategy proves vital for addressing an imbalance issue in DDI type classification. Applied to the DDIExtraction 2013 corpus, our system achieves an F1 score of 0.670, as compared to 0.651 and 0.609 reported by the top two participating teams in the DDIExtraction 2013 challenge, both based on non-linear kernel methods.

                Author and article information

                Clin Pharmacol Ther
                Clin. Pharmacol. Ther
                Clinical Pharmacology and Therapeutics
                John Wiley and Sons Inc. (Hoboken )
                11 December 2018
                April 2019
                11 December 2018
                : 105
                : 4 , Regulatory Science: Fostering Innovation and Accelerating Access ( doiID: 10.1002/cpt.2019.105.issue-4 )
                : 954-961
                [ 1 ] Pfizer R&D Lake Forest Illinois USA
                [ 2 ] Pfizer Business Technology Artificial Intelligence Center of Excellence La Jolla California USA
                [ 3 ] Pfizer Global Product Development Safety Solutions Kirkland Quebec, Ontario Canada
                [ 4 ] Pfizer Finance and Business Operations Peapack New Jersey USA
                [ 5 ] Pfizer R&D Collegeville Pennsylvania USA
                [ 6 ] Pfizer R&D New York New York USA
                Author notes
                [* ]Correspondence: Juergen Schmider ( juergen.schmider@ )
                © 2018 Pfizer Inc. Clinical Pharmacology & Therapeutics published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics

                This is an open access article under the terms of the License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                Page count
                Figures: 5, Tables: 1, Pages: 8, Words: 4819
                Custom metadata
                April 2019
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.6.4 mode:remove_FC converted:24.06.2019

                Pharmacology & Pharmaceutical medicine


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