SMILE: a feature-based temporal abstraction framework for event-interval sequence classification

Adverse drug event (ADE) detection in social media texts poses significant challenges due to the informal nature of the text and the limited availability of annotations. The scarcity of ADE named entity recognition (NER) datasets for social media hinders the development of robust ADE detection models for this type of corpus. In this paper, we leveraged the generative capabilities of large language models (LLMs) to create synthetic data, addressing this dataset gap. Specifically, we generated 17,000 tweets with ADE annotations and pre-trained NER models on this synthetic data. Our evaluations on an out-of-sample collection of 915 manually annotated tweets revealed that these models outperform state-of-the-art lexico-based and massively pre-trained open NER models. We also show that fine-tuning our synthetically pre-trained models on human-annotated data surpasses the current state-of-the-art in ADE detection on tweets. These findings suggest that synthetic data generated by LLMs can enhance ADE detection performance, offering a promising avenue to explore in response to the scarcity of annotated ADE datasets. The synthetic dataset is available at https://huggingface.co/datasets/anthonyyazdaniml/synthetic-ner-ade-tweets-v1.

Sequential endoscopist-driven phone calls improve the capture rate of adverse events after ERCP: a prospective study

Background:Most studies of healthcare complications identify surgery as a major contributor to the overall burden of complicated care that leads to injury or death. Indeed, surgical adverse events account for...

Although patient safety is a major problem, most health care organizations rely on spontaneous reporting, which detects only a small minority of adverse events. As a result, problems with safety have remained hidden. Chart review can detect adverse events in research settings, but it is too expensive for routine use. Information technology techniques can detect some adverse events in a timely and cost-effective way, in some cases early enough to prevent patient harm. To review methodologies of detecting adverse events using information technology, reports of studies that used these techniques to detect adverse events, and study results for specific types of adverse events. Structured review. English-language studies that reported using information technology to detect adverse events were identified using standard techniques. Only studies that contained original data were included. Adverse events, with specific focus on nosocomial infections, adverse drug events, and injurious falls. Tools such as event monitoring and natural language processing can inexpensively detect certain types of adverse events in clinical databases. These approaches already work well for some types of adverse events, including adverse drug events and nosocomial infections, and are in routine use in a few hospitals. In addition, it appears likely that these techniques will be adaptable in ways that allow detection of a broad array of adverse events, especially as more medical information becomes computerized. Computerized detection of adverse events will soon be practical on a widespread basis.

BackgroundThe "Put Prevention into Practice" campaign of the US Public Health Service (USPHS) was launched with the dissemination of the Clinician's Handbook of Preventive Services that recommended standards of clinical care for various prevention activities, including preventive clinical strategies to reduce the risk of adverse drug events. We explored whether nonprescribing clinicians such as chiropractors may contribute to advancing drug safety initiatives by identifying potential adverse drug events in their chiropractic patients, and by bringing suspected adverse drug events to the attention of the prescribing clinicians.MethodsMail survey of US chiropractors about their detection of potential adverse drug events in their chiropractic patients.ResultsOver half of responding chiropractors (62%) reported having identified a suspected adverse drug event occurring in one of their chiropractic patients. The severity of suspected drug-related events detected ranged from mild to severe.ConclusionsChiropractors or other nonprescribing clinicians may be in a position to detect potential adverse drug events in the community. These detection and reporting mechanisms should be standardized and policies related to clinical case management of suspected adverse drug events occurring in their patients should be developed.

Antiretroviral therapy (ART) is a lifelong combination therapy which has been very promising in managing HIV/AIDs but reported to have several adverse events on the recipients, affecting the desired antiretroviral therapeutic goals. Adverse drug event detection and prevention therefore become very important safety culture maintenance tools among patients living with HIV/AIDS. The designed protocol provided step by step approaches to develop and validate a trigger tool for detection of adverse drug events (ADE) among adult HIV/AIDS patients. The approaches involved include a baseline (Pre-trigger tool development) retrospective clinical record review to establish a baseline ADE detection capacity of a conventional non-trigger tool method; the Delphi panel process to develop the trigger tool in question; Face and content validity approach for validation of the developed trigger tool; Pilot test of the trigger tool using small clinical record sample; determining the effectiveness of the developed trigger tool through the retrospective clinical record review of same samples reviewed at the baseline. Finally, the protocol described the process of determining the feasibility of the developed trigger tool through the survey of the clinical record review team members.

BackgroundChronic Obstructive Pulmonary Disease is characterised by declining lung function and a greater oxidative stress burden due to reduced activity of antioxidant enzymes such as Glutathione Peroxidase 1.ObjectivesThe extent to which drugs may contribute to this compromised activity is largely unknown. An integrative drug safety model explores inhibition of Glutathione Peroxidase 1 by drugs and their association with chronic obstructive pulmonary disease adverse drug events.MethodsIn silico molecular modelling approaches were utilised to predict the interactions that drugs have within the active site of Glutathione Peroxidase 1 in both human and bovine models. Similarities of chemical features between approved drugs and the known inhibitor tiopronin were also investigated. Subsequently the Food and Drug Administration Adverse Event System was searched to uncover adverse drug event signals associated with chronic obstructive pulmonary disease.ResultsStatistical and molecular modelling analyses confirmed that the use of several registered drugs, including acetylsalicylic acid and atenolol may be associated with inhibition of Glutathione Peroxidase 1 and chronic obstructive pulmonary disease.ConclusionThe integration of molecular modelling and pharmacoepidemological data has the potential to advance drug safety science. Ongoing review of medication use and further pharmacoepidemiological and biological analyses are warranted to ensure appropriate use is recommended.Graphical

Emergency department (ED) overcrowding is a problem for the delivery of adequate and timely emergency care. To improve patient flow and the admission process, the quick prediction of a patient's need for admission is crucial. We aimed to investigate the variables associated with hospitalisation after an ED visit, with a particular focus on the variables related to medication. This prospective study was conducted from 2011 to 2018 in subacute medical ED of a French University Hospital. Specialised EDs (paediatric, gynaecologic, head and neck and psychiatric) and the outpatient unit of the ED were not included. Participation in this study was proposed to all adult patients who underwent a medication history interview with a pharmacist. Pharmacists conducted structured interviews for the completion of the medication history and the detection of adverse drug events (ADE). Relations between patient characteristics and hospitalisation were analysed using logistic regression. Among the 14511 included patients, 5972 (41.2%) were hospitalised including 69 deaths. In total, 7458 patients (51.4%) took more than 5 medications and 2846 patients (19.6%) had an ADE detected during the ED visit. In hospitalised patients, bleeding (32.2%) and metabolic disorders (16.8%) were the most observed ADE symptoms. Variables associated with increased hospital admission included 2 demographic variables (age, male gender), 4 clinical variables (renal and hepatic failures, alcohol addiction, ED visit for respiratory reason) and 6 medication-related variables (medications >5, use of blood, systemic anti-infective, metabolism and antineoplastic/immunomodulating medications and ADE). We identified variables associated with hospitalisation including drug-related variables. These results point out the importance and the relevance of collecting medication data in a subacute medical ED (study registered on ClinicalTrials.gov, NCT03442010).

Authenticity and credibility aware detection of adverse drug events from social media

GAR: Graph adversarial representation for adverse drug event detection on Twitter

Although several methods exist for Adverse Drug events (ADE) detection due to past hospitalizations, a tool that could display those ADEs to the physicians does not exist yet. This article presents the ADE Scorecards, a Web tool that enables to screen past hospitalizations extracted from Electronic Health Records (EHR), using a set of ADE detection rules, presently rules discovered by data mining. The tool enables the physicians to (1) get contextualized statistics about the ADEs that happen in their medical department, (2) see the rules that are useful in their department, i.e. the rules that could have enabled to prevent those ADEs and (3) review in detail the ADE cases, through a comprehensive interface displaying the diagnoses, procedures, lab results, administered drugs and anonymized records. The article shows a demonstration of the tool through a use case.

Pharmacovigilance improves patient safety by detecting and preventing adverse drug events. However, challenges exist that limit adverse drug event detection, resulting in many adverse drug events being underreported or inaccurately reported. One challenge includes having access to large data sets from various sources including electronic health records and wearable medical devices. Artificial intelligence, including machine learning methods, such as natural language processing and deep learning, can detect and extract information about adverse drug events, thus automating the pharmacovigilance process and improving the surveillance of known and documented adverse drug events. In addition, with the increased demand for telehealth services, for managing both acute and chronic diseases, artificial intelligence methods can play a role in detecting and preventing adverse drug events. In this review, we discuss two use cases of how artificial intelligence methods may be useful to improve the quality of pharmacovigilance and the role of artificial intelligence in telehealth practices.

Adverse reactions to drugs are a major public health care issue. Currently, the Food and Drug Administration (FDA) publishes quarterly reports that typically contain on the order of 200,000 adverse incidents. In such numerous incidents, low frequency events that are clinically highly significant often remain undetected. In this paper, we introduce a visual analytics system to solve this problem using (1) high scalable interfaces for analyzing correlations between a number of complex variables (e.g., drug and reaction); (2) enhanced statistical computations and interactive relevance filters to quickly identify significant events including those with a low frequency; and (3) a tight integration of expert knowledge for detecting and validating adverse drug events. We applied these techniques to the FDA Adverse Event Reporting System and were able to identify important adverse drug events, such as the known association of the drug Avandia with myocardial infarction and Seroquel with diabetes mellitus, as well as low frequency events such as the association of Boniva with femur fracture. In our evaluation, we found over 90% of the adverse drug events that were published in the Institute for Safe Medication Practices (ISMP) reports from 2009 to 2012. In addition, our domain expert was able to identify some previously unknown adverse drug events.

As a result of increased attention to medical errors, many institutions are contemplating increased use of information technology and clinical decision support. We conducted a retrospective analysis to estimate the frequency and cost of adverse drug events (ADEs) for inpatients at the University of Virginia. Applying published criteria for the detection of potential adverse events, we used a clinical data warehouse to identify patients and cases with potential ADEs. Again using published criteria, we then estimated the actual number of adverse drug events and preventable adverse drug events, as well as their attributable costs and excess length of stay. Our results showed a higher estimate (10.4-11.5 events per 100 admissions) for ADEs than seen in the ADE Prevention Study, highlighting the importance of considering the generalizability of published ADE studies to other settings. Our analysis demonstrates that retrospective analysis can be an efficient and powerful technique to evaluate rules and criteria used to detect ADEs and to assess their impact.

An adverse event is any abnormal clinical finding associated with the use of a therapy. Adverse events are classified by reporting an event's seriousness, expectedness, and relatedness. Monitoring patient safety is of utmost importance as more and more data becomes available. In reality, very low numbers of adverse events are reported via the official path. Chart review, voluntary reporting, computerized surveillance, and direct observation can detect adverse drug events. Medication errors are commonly seen in hospitals and need provider and system-based interventions to prevent them. The need of the hour in India is to develop and implement medication safety best practices to avoid adverse events. The utility of artificial intelligence techniques in adverse event detection remains unexplored, and their accuracy and precision need to be studied in a controlled setting. There is a need to develop predictive models to assess the likelihood of adverse reactions while testing novel pharmaceutical drugs.