Adverse drug event presentation and tracking (ADEPT): semiautomated, high throughput pharmacovigilance using real-world data

LEVERAGING UNSTRUCTURED DATA IN ELECTRONIC HEALTH RECORDS TO DETECT ADVERSE EVENTS FROM PEDIATRIC DRUG USE - A SCOPING REVIEW

Adverse drug events (ADEs) in pediatric populations pose significant public health challenges, yet research on their detection and monitoring remains limited. This scoping review evaluates the use of unstructured data from electronic health records (EHRs) to identify ADEs in children. We searched six databases, including MEDLINE, Embase and IEEE Xplore, in September 2024. From 984 records, only nine studies met our inclusion criteria, indicating a significant gap in research towards identify ADEs in children. We found that unstructured data in EHRs can indeed be of value and enhance pediatric pharmacovigilance, although its use has been so far very limited. Traditional Natural Language Processing (NLP) methods have been employed to extract ADEs, but the approaches utilized face challenges in generalizability and context interpretation. These challenges could be addressed with recent advances in transformer-based models and large language models (LLMs), unlocking the use of EHR data at scale for pediatric pharmacovigilance.

Clinical Research Informatics: a Decade-in-Review

BACKGROUND

Clinical Research Informatics (CRI) is a subspeciality of biomedical informatics that has substantially matured during the last decade. Advances in CRI have transformed the way clinical research is conducted. In recent years, there has been growing interest in CRI, as reflected by a vast and expanding scientific literature focused on the topic. The main objectives of this review are: 1) to provide an overview of the evolving definition and scope of this biomedical informatics subspecialty over the past 10 years; 2) to highlight major contributions to the field during the past decade; and 3) to provide insights about more recent CRI research trends and perspectives.

METHODS

We adopted a modified thematic review approach focused on understanding the evolution and current status of the CRI field based on literature sources identified through two complementary review processes (AMIA CRI year-in-review/IMIA Yearbook of Medical Informatics) conducted annually during the last decade.

RESULTS

More than 1,500 potentially relevant publications were considered, and 205 sources were included in the final review. The review identified key publications defining the scope of CRI and/or capturing its evolution over time as illustrated by impactful tools and methods in different categories of CRI focus. The review also revealed current topics of interest in CRI and prevailing research trends.

CONCLUSION

This scoping review provides an overview of a decade of research in CRI, highlighting major changes in the core CRI discoveries as well as increasingly impactful methods and tools that have bridged the principles-to-practice gap. Practical CRI solutions as well as examples of CRI-enabled large-scale, multi-organizational and/or multi-national research projects demonstrate the maturity of the field. Despite the progress demonstrated, some topics remain challenging, highlighting the need for ongoing CRI development and research, including the need of more rigorous evaluations of CRI solutions and further formalization and maturation of CRI services and capabilities across the research enterprise.

Real-world evidence to advance knowledge in pulmonary hypertension: Status, challenges, and opportunities. A consensus statement from the Pulmonary Vascular Research Institute's Innovative Drug Development Initiative's Real-world Evidence Working Group

This manuscript on real-world evidence (RWE) in pulmonary hypertension (PH) incorporates the broad experience of members of the Pulmonary Vascular Research Institute's Innovative Drug Development Initiative Real-World Evidence Working Group. We aim to strengthen the research community's understanding of RWE in PH to facilitate clinical research advances and ultimately improve patient care. Herein, we review real-world data (RWD) sources, discuss challenges and opportunities when using RWD sources to study PH populations, and identify resources needed to support the generation of meaningful RWE for the global PH community.

Improving drug safety with adverse event detection using natural language processing

INTRODUCTION

Pharmacovigilance (PV) involves monitoring and aggregating adverse event information from a variety of data sources, including health records, biomedical literature, spontaneous adverse event reports, product labels, and patient-generated content like social media posts, but the most pertinent details in these sources are typically available in narrative free-text formats. Natural language processing (NLP) techniques can be used to extract clinically relevant information from PV texts to inform decision-making.

AREAS COVERED

We conducted a non-systematic literature review by querying the PubMed database to examine the uses of NLP in drug safety and distilled the findings to present our expert opinion on the topic.

EXPERT OPINION

New NLP techniques and approaches continue to be applied for drug safety use cases; however, systems that are fully deployed and in use in a clinical environment remain vanishingly rare. To see high-performing NLP techniques implemented in the real setting will require long-term engagement with end users and other stakeholders and revised workflows in fully formulated business plans for the targeted use cases. Additionally, we found little to no evidence of extracted information placed into standardized data models, which should be a way to make implementations more portable and adaptable.

Artificial Intelligence in Pharmacovigilance: An Introduction to Terms, Concepts, Applications, and Limitations

The tools of artificial intelligence (AI) have enormous potential to enhance activities in pharmacovigilance. Pharmacovigilance experts need not be AI experts, but they should know enough about AI to explore the possibilities of collaboration with those who are. Modern concepts of AI date from Alan Turing's work, especially his paper on "the imitation game", in the late 1940s and early 1950s. Its scope today includes computational skills, including the formulation of mathematical proofs; visual perception, including facial recognition and virtual reality; decision making by expert systems; aspects of language, such as language processing, speech recognition, creative composition, and translation; and combinations of these, e.g. in self-driving vehicles. Machines can be programmed with the ability to learn, using neural networks that mimic cognitive actions of the human brain, leading to deep structural learning. Limitations of AI include difficulties with language, arising from the need to understand context and interpret ambiguities, which particularly affect translation, and inadequacies of databases, requiring careful preparation and curation. New techniques may cause unforeseen difficulties via unexpected malfunctioning. Relevant terms and concepts include different types of machine learning, neural networks, natural language programming, ontologies, and expert systems. Adoption of the tools of AI in pharmacovigilance has been slow. Machine learning, in conjunction with natural language processing and data mining, to study adverse drug reactions in databases such as those found in electronic health records, claims databases, and social media, has the potential to enhance the characterization of known adverse effects and reactions and detect new signals.

Key Contributions in Clinical Research Informatics

Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2020.

Method: A bibliographic search using a combination of Medical Subject Headings (MeSH) descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting between two section editors and the editorial team was organized to finally conclude on the selected four best papers.

Results: Among the 877 papers published in 2020 and returned by the search, there were four best papers selected. The first best paper describes a method for mining temporal sequences from clinical documents to infer disease trajectories and enhancing high-throughput phenotyping. The authors of the second best paper demonstrate that the generation of synthetic Electronic Health Record (EHR) data through Generative Adversarial Networks (GANs) could be substantially improved by more appropriate training and evaluation criteria. The third best paper offers an efficient advance on methods to detect adverse drug events by computer-assisting expert reviewers with annotated candidate mentions in clinical documents. The large-scale data quality assessment study reported by the fourth best paper has clinical research informatics implications, in terms of the trustworthiness of inferences made from analysing electronic health records.

Conclusions: The most significant research efforts in the CRI field are currently focusing on data science with active research in the development and evaluation of Artificial Intelligence/Machine Learning (AI/ML) algorithms based on ever more intensive use of real-world data and especially EHR real or synthetic data. A major lesson that the coronavirus disease 2019 (COVID-19) pandemic has already taught the scientific CRI community is that timely international high-quality data-sharing and collaborative data analysis is absolutely vital to inform policy decisions.