Signal Fusion and Semantic Similarity Evaluation for Social Media Based Adverse Drug Event Detection

Recent advancements in pharmacovigilance tasks have shown the usage of social media as a resource to obtain real-time signals for drug surveillance. Researchers demonstrated a good potential for the detection of Adverse Drug Events (ADEs) using social media much earlier than the traditional reporting systems maintained by official regulatory authorities like the United States Food and Drug Administration (FDA). Existing automated drug surveillance systems have used various types of social media channels and search query logs for monitoring ADE signals.;In this thesis, we address two key performance issues related to automated drug surveillance systems. The first is to improve the ADE signal detection by analyzing signals from multiple social media channels, and the second is usage of semantic similarity to evaluate ADE narratives detected by drug surveillance systems. Most current approaches for detecting ADEs from social media rely on a single channel: forums or microblogs or query logs. In this study we propose a new methodology to fuse signals from different social media channels. We use graphical causal models to discover potentially hidden connections between data channels, and then use such associations to generate signals for ADEs. Further, prior work have not emphasized much on the language of healthcare consumers, which is often casual and informal in expressing health issues on social media. There is a high potential to miss the semantic similarity between ADE terms extracted from social media and terms from formal official narratives when the two sets of terms do not share exact text. Thus, we exhibit the usage of semantic similarity to enhance accuracy of detected ADEs, and evaluated similarity measurement algorithms developed over biomedical vocabularies in ADE surveillance domain. We experimented on a dataset of drugs which had FDA black box warnings with a retrospective analysis spanning years 2008 to 2015. The results show a better detection rate and an improved performance in terms of precision, recall and timeliness using our proposed methods

Adverse Drug Event Detection, Causality Inference, Patient Communication and Translational Research

Adverse drug events (ADEs) are injuries resulting from a medical intervention related to a drug. ADEs are responsible for nearly 20% of all the adverse events that occur in hospitalized patients. ADEs have been shown to increase the cost of health care and the length of stays in hospital. Therefore, detecting and preventing ADEs for pharmacovigilance is an important task that can improve the quality of health care and reduce the cost in a hospital setting. In this dissertation, we focus on the development of ADEtector, a system that identifies ADEs and medication information from electronic medical records and the FDA Adverse Event Reporting System reports. The ADEtector system employs novel natural language processing approaches for ADE detection and provides a user interface to display ADE information. The ADEtector employs machine learning techniques to automatically processes the narrative text and identify the adverse event (AE) and medication entities that appear in that narrative text. The system will analyze the entities recognized to infer the causal relation that exists between AEs and medications by automating the elements of Naranjo score using knowledge and rule based approaches. The Naranjo Adverse Drug Reaction Probability Scale is a validated tool for finding the causality of a drug induced adverse event or ADE. The scale calculates the likelihood of an adverse event related to drugs based on a list of weighted questions. The ADEtector also presents the user with evidence for ADEs by extracting figures that contain ADE related information from biomedical literature. A brief summary is generated for each of the figures that are extracted to help users better comprehend the figure. This will further enhance the user experience in understanding the ADE information better. The ADEtector also helps patients better understand the narrative text by recognizing complex medical jargon and abbreviations that appear in the text and providing definitions and explanations for them from external knowledge resources. This system could help clinicians and researchers in discovering novel ADEs and drug relations and also hypothesize new research questions within the ADE domain

Learning to detect and understand drug discontinuation events from clinical narratives

OBJECTIVE: Identifying drug discontinuation (DDC) events and understanding their reasons are important for medication management and drug safety surveillance. Structured data resources are often incomplete and lack reason information. In this article, we assessed the ability of natural language processing (NLP) systems to unlock DDC information from clinical narratives automatically. MATERIALS AND METHODS: We collected 1867 de-identified providers\u27 notes from the University of Massachusetts Medical School hospital electronic health record system. Then 2 human experts chart reviewed those clinical notes to annotate DDC events and their reasons. Using the annotated data, we developed and evaluated NLP systems to automatically identify drug discontinuations and reasons at the sentence level using a novel semantic enrichment-based vector representation (SEVR) method for enhanced feature representation. RESULTS: Our SEVR-based NLP system achieved the best performance of 0.785 (AUC-ROC) for detecting discontinuation events and 0.745 (AUC-ROC) for identifying reasons when testing this highly imbalanced data, outperforming 2 state-of-the-art non-SEVR-based models. Compared with a rule-based baseline system for discontinuation detection, our system improved the sensitivity significantly (57.75% vs 18.31%, absolute value) while retaining a high specificity of 99.25%, leading to a significant improvement in AUC-ROC by 32.83% (absolute value). CONCLUSION: Experiments have shown that a high-performance NLP system can be developed to automatically identify DDCs and their reasons from providers\u27 notes. The SEVR model effectively improved the system performance showing better generalization and robustness on unseen test data. Our work is an important step toward identifying reasons for drug discontinuation that will inform drug safety surveillance and pharmacovigilance

BioLORD-2023: Semantic Textual Representations Fusing LLM and Clinical Knowledge Graph Insights

In this study, we investigate the potential of Large Language Models to complement biomedical knowledge graphs in the training of semantic models for the biomedical and clinical domains. Drawing on the wealth of the UMLS knowledge graph and harnessing cutting-edge Large Language Models, we propose a new state-of-the-art approach for obtaining high-fidelity representations of biomedical concepts and sentences, consisting of three steps: an improved contrastive learning phase, a novel self-distillation phase, and a weight averaging phase. Through rigorous evaluations via the extensive BioLORD testing suite and diverse downstream tasks, we demonstrate consistent and substantial performance improvements over the previous state of the art (e.g. +2pts on MedSTS, +2.5pts on MedNLI-S, +6.1pts on EHR-Rel-B). Besides our new state-of-the-art biomedical model for English, we also distill and release a multilingual model compatible with 50+ languages and finetuned on 7 European languages. Many clinical pipelines can benefit from our latest models. Our new multilingual model enables a range of languages to benefit from our advancements in biomedical semantic representation learning, opening a new avenue for bioinformatics researchers around the world. As a result, we hope to see BioLORD-2023 becoming a precious tool for future biomedical applications.Comment: Preprint of upcoming journal articl