In the realm of cancer treatment, summarizing adverse drug events (ADEs) reported by patients using prescribed drugs is crucial for enhancing pharmacovigilance practices and improving drug-related decision-making. While the volume and complexity of pharmacovigilance data have increased, existing research in this field has predominantly focused on general diseases rather than specifically addressing cancer. This work introduces the task of grouped summarization of adverse drug events reported by multiple patients using the same drug for cancer treatment. To address the challenge of limited resources in cancer pharmacovigilance, we present the MultiLabeled Cancer Adverse Drug Reaction and Summarization (MCADRS) dataset. This dataset includes pharmacovigilance posts detailing patient concerns regarding drug efficacy and adverse effects, along with extracted labels for drug names, adverse drug events, severity, and adversity of reactions, as well as summaries of ADEs for each drug. Additionally, we propose the Grouping and Abstractive Summarization of Cancer Adverse Drug events (GASCADE) framework, a novel pipeline that combines the information extraction capabilities of Large Language Models (LLMs) with the summarization power of the encoder-decoder T5 model. Our work is the first to apply alignment techniques, including advanced algorithms like Direct Preference Optimization, to encoder-decoder models using synthetic datasets for summarization tasks. Through extensive experiments, we demonstrate the superior performance of GASCADE across various metrics, validated through both automated assessments and human evaluations. This multitasking approach enhances drug-related decision-making and fosters a deeper understanding of patient concerns, paving the way for advancements in personalized and responsive cancer care. The code and dataset used in this work are publicly available.

Objective. Active adverse event surveillance monitors Adverse Drug Events (ADE) from different data sources, such as electronic health records, medical literature, social media and search engine logs. Over years, many datasets are created, and shared tasks are organised to facilitate active adverse event surveillance. However, most-if not all-datasets or shared tasks focus on extracting ADEs from a particular type of text. Domain generalisation-the ability of a machine learning model to perform well on new, unseen domains (text types)-is under-explored. Given the rapid advancements in natural language processing, one unanswered question is how far we are from having a single ADE extraction model that are effective on various types of text, such as scientific literature and social media posts}. Methods. We contribute to answering this question by building a multi-domain benchmark for adverse drug event extraction, which we named MultiADE. The new benchmark comprises several existing datasets sampled from different text types and our newly created dataset-CADECv2, which is an extension of CADEC (Karimi, et al., 2015), covering online posts regarding more diverse drugs than CADEC. Our new dataset is carefully annotated by human annotators following detailed annotation guidelines. Conclusion. Our benchmark results show that the generalisation of the trained models is far from perfect, making it infeasible to be deployed to process different types of text. In addition, although intermediate transfer learning is a promising approach to utilising existing resources, further investigation is needed on methods of domain adaptation, particularly cost-effective methods to select useful training instances.

The mining of adverse drug events (ADEs) is pivotal in pharmacovigilance, enhancing patient safety by identifying potential risks associated with medications, facilitating early detection of adverse events, and guiding regulatory decision-making. Traditional ADE detection methods are reliable but slow, not easily adaptable to large-scale operations, and offer limited information. With the exponential increase in data sources like social media content, biomedical literature, and Electronic Medical Records (EMR), extracting relevant ADE-related information from these unstructured texts is imperative. Previous ADE mining studies have focused on text-based methodologies, overlooking visual cues, limiting contextual comprehension, and hindering accurate interpretation. To address this gap, we present a MultiModal Adverse Drug Event (MMADE) detection dataset, merging ADE-related textual information with visual aids. Additionally, we introduce a framework that leverages the capabilities of LLMs and VLMs for ADE detection by generating detailed descriptions of medical images depicting ADEs, aiding healthcare professionals in visually identifying adverse events. Using our MMADE dataset, we showcase the significance of integrating visual cues from images to enhance overall performance. This approach holds promise for patient safety, ADE awareness, and healthcare accessibility, paving the way for further exploration in personalized healthcare.

Timely and accurate extraction of Adverse Drug Events (ADE) from biomedical literature is paramount for public safety, but involves slow and costly manual labor. We set out to improve drug safety monitoring (pharmacovigilance, PV) through the use of Natural Language Processing (NLP). We introduce BioDEX, a large-scale resource for Biomedical adverse Drug Event Extraction, rooted in the historical output of drug safety reporting in the U.S. BioDEX consists of 65k abstracts and 19k full-text biomedical papers with 256k associated document-level safety reports created by medical experts. The core features of these reports include the reported weight, age, and biological sex of a patient, a set of drugs taken by the patient, the drug dosages, the reactions experienced, and whether the reaction was life threatening. In this work, we consider the task of predicting the core information of the report given its originating paper. We estimate human performance to be 72.0% F1, whereas our best model achieves 62.3% F1, indicating significant headroom on this task. We also begin to explore ways in which these models could help professional PV reviewers. Our code and data are available: https://github.com/KarelDO/BioDEX.

Objective: To develop a natural language processing system that solves both clinical concept extraction and relation extraction in a unified prompt-based machine reading comprehension (MRC) architecture with good generalizability for cross-institution applications. Methods: We formulate both clinical concept extraction and relation extraction using a unified prompt-based MRC architecture and explore state-of-the-art transformer models. We compare our MRC models with existing deep learning models for concept extraction and end-to-end relation extraction using two benchmark datasets developed by the 2018 National NLP Clinical Challenges (n2c2) challenge (medications and adverse drug events) and the 2022 n2c2 challenge (relations of social determinants of health [SDoH]). We also evaluate the transfer learning ability of the proposed MRC models in a cross-institution setting. We perform error analyses and examine how different prompting strategies affect the performance of MRC models. Results and Conclusion: The proposed MRC models achieve state-of-the-art performance for clinical concept and relation extraction on the two benchmark datasets, outperforming previous non-MRC transformer models. GatorTron-MRC achieves the best strict and lenient F1-scores for concept extraction, outperforming previous deep learning models on the two datasets by 1%~3% and 0.7%~1.3%, respectively. For end-to-end relation extraction, GatorTron-MRC and BERT-MIMIC-MRC achieve the best F1-scores, outperforming previous deep learning models by 0.9%~2.4% and 10%-11%, respectively. For cross-institution evaluation, GatorTron-MRC outperforms traditional GatorTron by 6.4% and 16% for the two datasets, respectively. The proposed method is better at handling nested/overlapped concepts, extracting relations, and has good portability for cross-institute applications.

Adverse drug events (ADEs), injuries related to drug-related medical interventions, are considered some of the most prevalent types of health-care-related harm. Given that these events are costly and often morbid, artificial intelligence (AI) is considered a promising tool in helping researchers and clinicians understand preventable and novel ADEs, as well as a patient's likelihood of having ADEs before receiving prescription medications. Researchers at Brigham and Women's Hospital conducted a scoping review of 78 articles to identify the key use cases in which AI could be harnessed to prevent or mitigate the effects of ADEs. The review's authors describe the use of AI to reduce the frequency of ADEs as an emerging area of study, and identify several use cases in which AI could contribute to reducing or preventing ADEs. Furthermore, genetic information is thought to be critical in improving the performance of AI algorithms. With the prevalence of genotyping, researchers are confident that this type of data can become more accessible over time, and can ultimately be used to improve AI algorithm functioning and patient health.

Tabula Rasa HealthCare today launched MedWise on its PrescribeWellness platform for pharmacists to cumulatively compare how different medications interact with each other. The company, which focuses on improving medication safety, developed this service to allow pharmacists to see how medications will work together on a larger scale than one to one. By doing this, Tabula Rasa says adverse drug events can be reduced. Included with the software is a MedWise Risk Score calculation and MedWise Decision Support. The decision support tool will change the risk score in real time so pharmacists can assess complex medication regimens' safety. Adverse drug events (ADE) cause approximately 1.3 million emergency department visits each year, according to the CDC.

Adverse drug events (ADEs) are "injuries resulting from medical intervention related to a drug" (Nebeker, Barach, and Samore 2004), and are distinct from medication errors (inappropriate prescription, dispensing, usage etc.) as they are caused by drugs at normal dosages. According to the National Center for Health Statistics (NCHS 2014), 48.9% of Americans took at least one prescription drug in the last 30 days, 23.1% took at least three, and 11.9% took at least

Parkland Hospital, a Dallas, TX-based hospital and health system has saved over $17 million dollars by reducing their hospital re-admissions and eliminating adverse drug events using a customized artificial intelligence (AI)-driven predictive model. Developed in partnership with affiliate Park Center for Clinical Innovation (PCCI) over the past 2 years, Parkland has been able to prevent more than 2,000 adverse drug events (ADEs) for hospitalized patients. PCCI combines extensive clinical expertise with advanced analytics and artificial intelligence to enable the delivery of patient-centric precision medicine at the point of care. The program, Patients at Risk for Adverse Drug Events (PARADE), is a partnership between PCCI and Parkland Health & Hospital System. PARADE screens all adult patients at the point of hospitalization and flags high-risk individuals who can benefit from pharmacist intervention.

As a rule, the more medications an individual patient takes, the greater the risk of suffering a negative side effect. Thus, there is a delicate balance between the clinical benefit of the medications, and the risk to harm as an aggregate, necessitating continuous evaluation of each medication, making sure harm does not outweigh the benefit. Yet, the hunger for medications in the U.S. is growing with no end in sight. In a Consumer Reports study conducted in 2017, while the US population had only increased by 21 percent over the past two decades, there is a shocking 85 percent increase in the number of filled prescriptions, with more than half of the US population on a prescription medication. A typical American is prescribed up to four medications on average, not including over-the-counter drugs, creating complex medication scenarios and increasing the likelihood of an ADEs.