Sameen Desai

Adverse Effects Image Fingerprinting using CNN Deep Learning as a Means for Individual Case Safety Report Classification.

Abstract: Objective We investigated the use of image recognition using convolutional neural networks (CNNs) to classify individual case safety reports (ICSRs) as serious or non-serious, demonstrating that it is possible to precede the well-established challenge of data extraction for prediction model development.

Method Using deep learning CNN, we extract data from images to predict case-level seriousness from incoming ICSR source documents.

Results: Our results demonstrate success in training a CNN to classify ICSRs using only image representations of source documents as input with an accuracy of 86% when compared against the ground truth.

Conclusion: Image recognition applied to PV data will allow us to precede the challenge of multiple document formats, transcription and extraction of data entities from source documents, allowing us to classify source documents upon intake.

This methodological breakthrough is significant as it increases the speed at which models may be trained to understand domain concepts, within PV and beyond, using image representations of documents. The authors believe this research may be applicable across industries which are not yet digitally ready enough to undertake a large-scale AI/ML initiative.

Sameen is the Senior Director of Pharmacovigilance Innovation at Bristol-Myers Squibb. He holds a Bachelor’s in Chemical Engineering, Master’s of Business Administration, and Master’s of Information Systems. Sameen is interested in exploring the application of AI/ML in the pharmacovigilance space with the goal of lessening the burden on our PV professionals and increasing positive outcomes for our patients globally.

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