Adverse drug reaction extraction on electronic health records written in Spanish

148 p.This work focuses on the automatic extraction of Adverse Drug Reactions (ADRs) in Electronic HealthRecords (EHRs). That is, extracting a response to a medicine which is noxious and unintended and whichoccurs at doses normally used. From Natural Language Processing (NLP) perspective, this wasapproached as a relation extraction task in which the drug is the causative agent of a disease, sign orsymptom, that is, the adverse reaction.ADR extraction from EHRs involves major challenges. First, ADRs are rare events. That is, relationsbetween drugs and diseases found in an EHR are seldom ADRs (are often unrelated or, instead, related astreatment). This implies the inference from samples with skewed class distribution. Second, EHRs arewritten by experts often under time pressure, employing both rich medical jargon together with colloquialexpressions (not always grammatical) and it is not infrequent to find misspells and both standard andnon-standard abbreviations. All this leads to a high lexical variability.We explored several ADR detection algorithms and representations to characterize the ADR candidates.In addition, we have assessed the tolerance of the ADR detection model to external noise such as theincorrect detection of implied medical entities implied in the ADR extraction, i.e. drugs and diseases. Westtled the first steps on ADR extraction in Spanish using a corpus of real EHRs

A Sui Generis QA Approach using RoBERTa for Adverse Drug Event Identification

Extraction of adverse drug events from biomedical literature and other textual data is an important component to monitor drug-safety and this has attracted attention of many researchers in healthcare. Existing works are more pivoted around entity-relation extraction using bidirectional long short term memory networks (Bi-LSTM) which does not attain the best feature representations. In this paper, we introduce a question answering framework that exploits the robustness, masking and dynamic attention capabilities of RoBERTa by a technique of domain adaptation and attempt to overcome the aforementioned limitations. Our model outperforms the prior work by 9.53% F1-Score

Translational biomedical informatics and pharmacometrics approaches in the drug interactions research

Drug interaction is a leading cause of adverse drug events and a major obstacle for current clinical practice. Pharmacovigilance data mining, pharmacokinetic modeling, and text mining are computation and informatic tools on integrating drug interaction knowledge and generating drug interaction hypothesis. We provide a comprehensive overview of these translational biomedical informatics methodologies with related databases. We hope this review illustrates the complementary nature of these informatic approaches and facilitates the translational drug interaction research

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

Named Entity Recognition in Electronic Health Records: A Methodological Review

Objectives A substantial portion of the data contained in Electronic Health Records (EHR) is unstructured, often appearing as free text. This format restricts its potential utility in clinical decision-making. Named entity recognition (NER) methods address the challenge of extracting pertinent information from unstructured text. The aim of this study was to outline the current NER methods and trace their evolution from 2011 to 2022. Methods We conducted a methodological literature review of NER methods, with a focus on distinguishing the classification models, the types of tagging systems, and the languages employed in various corpora. Results Several methods have been documented for automatically extracting relevant information from EHRs using natural language processing techniques such as NER and relation extraction (RE). These methods can automatically extract concepts, events, attributes, and other data, as well as the relationships between them. Most NER studies conducted thus far have utilized corpora in English or Chinese. Additionally, the bidirectional encoder representation from transformers using the BIO tagging system architecture is the most frequently reported classification scheme. We discovered a limited number of papers on the implementation of NER or RE tasks in EHRs within a specific clinical domain. Conclusions EHRs play a pivotal role in gathering clinical information and could serve as the primary source for automated clinical decision support systems. However, the creation of new corpora from EHRs in specific clinical domains is essential to facilitate the swift development of NER and RE models applied to EHRs for use in clinical practice

Clinical information extraction for preterm birth risk prediction

This paper contributes to the pursuit of leveraging unstructured medical notes to structured clinical decision making. In particular, we present a pipeline for clinical information extraction from medical notes related to preterm birth, and discuss the main challenges as well as its potential for clinical practice. A large collection of medical notes, created by staff during hospitalizations of patients who were at risk of delivering preterm, was gathered and analyzed. Based on an annotated collection of notes, we trained and evaluated information extraction components to discover clinical entities such as symptoms, events, anatomical sites and procedures, as well as attributes linked to these clinical entities. In a retrospective study, we show that these are highly informative for clinical decision support models that are trained to predict whether delivery is likely to occur within specific time windows, in combination with structured information from electronic health records

Vaccine semantics : Automatic methods for recognizing, representing, and reasoning about vaccine-related information

Post-marketing management and decision-making about vaccines builds on the early detection of safety concerns and changes in public sentiment, the accurate access to established evidence, and the ability to promptly quantify effects and verify hypotheses about the vaccine benefits and risks. A variety of resources provide relevant information but they use different representations, which makes rapid evidence generation and extraction challenging. This thesis presents automatic methods for interpreting heterogeneously represented vaccine information. Part I evaluates social media messages for monitoring vaccine adverse events and public sentiment in social media messages, using automatic methods for information recognition. Parts II and III develop and evaluate automatic methods and res

Utilizing artificial intelligence in perioperative patient flow:systematic literature review

Abstract. The purpose of this thesis was to map the existing landscape of artificial intelligence (AI) applications used in secondary healthcare, with a focus on perioperative care. The goal was to find out what systems have been developed, and how capable they are at controlling perioperative patient flow. The review was guided by the following research question: How is AI currently utilized in patient flow management in the context of perioperative care? This systematic literature review examined the current evidence regarding the use of AI in perioperative patient flow. A comprehensive search was conducted in four databases, resulting in 33 articles meeting the inclusion criteria. Findings demonstrated that AI technologies, such as machine learning (ML) algorithms and predictive analytics tools, have shown somewhat promising outcomes in optimizing perioperative patient flow. Specifically, AI systems have proven effective in predicting surgical case durations, assessing risks, planning treatments, supporting diagnosis, improving bed utilization, reducing cancellations and delays, and enhancing communication and collaboration among healthcare providers. However, several challenges were identified, including the need for accurate and reliable data sources, ethical considerations, and the potential for biased algorithms. Further research is needed to validate and optimize the application of AI in perioperative patient flow. The contribution of this thesis is summarizing the current state of the characteristics of AI application in perioperative patient flow. This systematic literature review provides information about the features of perioperative patient flow and the clinical tasks of AI applications previously identified

Combining Heterogeneous Databases to Detect Adverse Drug Reactions

Adverse drug reactions (ADRs) cause a global and substantial burden accounting for considerable mortality, morbidity and extra costs. In the United States, over 770,000 ADR related injures or deaths occur each year in hospitals, which may cost up to $5.6 million each year per hospital. Unanticipated ADRs may occur after a drug has been approved due to its use or prolonged use on large, diverse populations. Therefore, the post-marketing surveillance of drugs is essential for generating more complete drug safety profiles and for providing a decision making tool to help governmental drug administration agencies take an action on the marketed drugs. Analysis of spontaneous reports of suspected ADRs has traditionally served as a valuable tool in pharmacovigilance. However, because of well-known limitations of spontaneous reports, observational healthcare data, such as electronic health records (EHRs) and administrative claims data, are starting to be used to complement the spontaneous reporting system. Synthesizing ADR evidence from multiple data sources has been conducted by human experts on an at hoc basis. However, the amount of data from both spontaneous reporting systems (SRSs) and observational healthcare databases is growing exponentially. The revolution in the ability of machines to access, process, and mine databases, making it advantageous to develop an automatic system to obtain integrated evidence by combining them. Towards this goal, this dissertation proposes a framework consisting of three components that generates signal scores based on data an EHR system and of an SRS system, and then integrates two signal scores into a composite one. The first component is a data-driven and regression- based method that aims to alleviate confounding effect and detect ADR based on EHRs. The results demonstrate that this component achieves comparable or slightly higher accuracy than those trained with experts and existing automatic methods. The second component is also a data- driven and regression-based method that aims to reduce the effect of confounding by co- medication and confounding by indication using primary suspected, secondary suspected, concomitant medications and indications on the basis of a SRS. This study demonstrates that it could accomplish comparable or slightly better accuracy than the cutting edge algorithm Gamma Poisson Shrinkage (GPS), which uses primary suspected medications only. The third component is a computational integration method that normalizes signal scores from each data source and integrates them into a composite signal score. The results achieved by the method demonstrate that the combined ADR evidence achieve better accuracy of drug-ADR detection than individual systems based on either an SRS or an EHR. Furthermore, component three is explored as a tool to assist clinical assessors in pharmacovigilance practice. The research presented in this dissertation has produced several novel insights and provided new solutions towards the challenging problem of pharmacovigilance. The method of reducing confounding effect can be generalizable to other EHR systems and the method for integrating ADR evidence can be generalizable to include other data sources. In conclusion, this dissertation develops a method to reduce confounding effect in both EHRs and SRSs, and a combined system to synthesize evidence, which could potentially unveil drug safety profiles and novel adverse events in a timely fashion