Natural Language Processing Combined with ICD-9-CM Codes as a Novel Method to Study the Epidemiology of Allergic Drug Reactions

Extracting adverse drug events from clinical Notes: A systematic review of approaches used

BACKGROUND

An adverse drug event (ADE) is any unfavorable effect that occurs due to the use of a drug. Extracting ADEs from unstructured clinical notes is essential to biomedical text extraction research because it helps with pharmacovigilance and patient medication studies.

OBJECTIVE

From the considerable amount of clinical narrative text, natural language processing (NLP) researchers have developed methods for extracting ADEs and their related attributes. This work presents a systematic review of current methods.

METHODOLOGY

Two biomedical databases have been searched from June 2022 until December 2023 for relevant publications regarding this review, namely the databases PubMed and Medline. Similarly, we searched the multi-disciplinary databases IEEE Xplore, Scopus, ScienceDirect, and the ACL Anthology. We adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement guidelines and recommendations for reporting systematic reviews in conducting this review. Initially, we obtained 5,537 articles from the search results from the various databases between 2015 and 2023. Based on predefined inclusion and exclusion criteria for article selection, 100 publications have undergone full-text review, of which we consider 82 for our analysis.

RESULTS

We determined the general pattern for extracting ADEs from clinical notes, with named entity recognition (NER) and relation extraction (RE) being the dual tasks considered. Researchers that tackled both NER and RE simultaneously have approached ADE extraction as a "pipeline extraction" problem (n = 22), as a "joint task extraction" problem (n = 7), and as a "multi-task learning" problem (n = 6), while others have tackled only NER (n = 27) or RE (n = 20). We further grouped the reviews based on the approaches for data extraction, namely rule-based (n = 8), machine learning (n = 11), deep learning (n = 32), comparison of two or more approaches (n = 11), hybrid (n = 12) and large language models (n = 8). The most used datasets are MADE 1.0, TAC 2017 and n2c2 2018.

CONCLUSION

Extracting ADEs is crucial, especially for pharmacovigilance studies and patient medications. This survey showcases advances in ADE extraction research, approaches, datasets, and state-of-the-art performance in them. Challenges and future research directions are highlighted. We hope this review will guide researchers in gaining background knowledge and developing more innovative ways to address the challenges.

Applying Deep Learning Model to Predict Diagnosis Code of Medical Records

The International Classification of Diseases (ICD) code is a diagnostic classification standard that is frequently used as a referencing system in healthcare and insurance. However, it takes time and effort to find and use the right diagnosis code based on a patient's medical records. In response, deep learning (DL) methods have been developed to assist physicians in the ICD coding process. Our findings propose a deep learning model that utilized clinical notes from medical records to predict ICD-10 codes. Our research used text-based medical data from the outpatient department (OPD) of a university hospital from January to December 2016. The dataset used clinical notes from five departments, and a total of 21,953 medical records were collected. Clinical notes consisted of a subjective component, objective component, assessment, plan (SOAP) notes, diagnosis code, and a drug list. The dataset was divided into two groups: 90% for training and 10% for test cases. We applied natural language processing (NLP) technique (word embedding, Word2Vector) to process the data. A deep learning-based convolutional neural network (CNN) model was created based on the information presented above. Three metrics (precision, recall, and F-score) were used to calculate the achievement of the deep learning CNN model. Clinically acceptable results were achieved through the deep learning model for five departments (precision: 0.53-0.96; recall: 0.85-0.99; and F-score: 0.65-0.98). With a precision of 0.95, a recall of 0.99, and an F-score of 0.98, the deep learning model performed the best in the department of cardiology. Our proposed CNN model significantly improved the prediction performance for an automated ICD-10 code prediction system based on prior clinical information. This CNN model could reduce the laborious task of manual coding and could assist physicians in making a better diagnosis.

Artificial Intelligence: Exploring the Future of Innovation in Allergy Immunology

PURPOSE OF REVIEW

Artificial intelligence (AI) has increasingly been used in healthcare. Given the capacity of AI to handle large data and complex relationships between variables, AI is well suited for applications in healthcare. Recently, AI has been applied to allergy research.

RECENT FINDINGS

In this article, we review how AI technologies have been utilized in basic science and clinical allergy research for asthma, atopic dermatitis, rhinology, adverse reactions to drugs and vaccines, food allergy, anaphylaxis, urticaria, and eosinophilic gastrointestinal disorders. We discuss barriers for AI adoption to improve the care of patients with atopic diseases. These studies demonstrate the utility of applying AI to the field of allergy to help investigators expand their understanding of disease pathogenesis, improve diagnostic accuracy, enable prediction for treatments and outcomes, and for drug discovery.

Ophthalmology Operation Note Encoding with Open-Source Machine Learning and Natural Language Processing

INTRODUCTION

Accurate assignment of procedural codes has important medico-legal, academic, and economic purposes for healthcare providers. Procedural coding requires accurate documentation and exhaustive manual labour to interpret complex operation notes. Ophthalmology operation notes are highly specialised making the process time-consuming and challenging to implement. This study aimed to develop natural language processing (NLP) models trained by medical professionals to assign procedural codes based on the surgical report. The automation and accuracy of these models can reduce burden on healthcare providers and generate reimbursements that reflect the operation performed.

METHODS

A retrospective analysis of ophthalmological operation notes from two metropolitan hospitals over a 12-month period was conducted. Procedural codes according to the Medicare Benefits Schedule (MBS) were applied. XGBoost, decision tree, Bidirectional Encoder Representations from Transformers (BERT) and logistic regression models were developed for classification experiments. Experiments involved both multi-label and binary classification, and the best performing model was used on the holdout test dataset.

RESULTS

There were 1,000 operation notes included in the study. Following manual review, the five most common procedures were cataract surgery (374 cases), vitrectomy (298 cases), laser therapy (149 cases), trabeculectomy (56 cases), and intravitreal injections (49 cases). Across the entire dataset, current coding was correct in 53.9% of cases. The BERT model had the highest classification accuracy (88.0%) in the multi-label classification on these five procedures. The total reimbursement achieved by the machine learning algorithm was $184,689.45 ($923.45 per case) compared with the gold standard of $214,527.50 ($1,072.64 per case).

CONCLUSION

Our study demonstrates accurate classification of ophthalmic operation notes into MBS coding categories with NLP technology. Combining human and machine-led approaches involves using NLP to screen operation notes to code procedures, with human review for further scrutiny. This technology can allow the assignment of correct MBS codes with greater accuracy. Further research and application in this area can facilitate accurate logging of unit activity, leading to reimbursements for healthcare providers. Increased accuracy of procedural coding can play an important role in training and education, study of disease epidemiology and improve research ways to optimise patient outcomes.

Allergy Electronic Health Record Documentation: A 2022 Work Group Report of the AAAAI Adverse Reactions to Drugs, Biologicals, and Latex Committee

The allergy section of the electronic health record (EHR) is ideally reviewed and updated by health care workers during routine outpatient visits, emergency room visits, inpatient hospitalizations, and surgical procedures. This EHR section has the potential to help proactively and comprehensively avoid exposures to drugs, contact irritants, foods, and other agents for which, based on an individual's medical history and/or genetics, there is increased risk for adverse outcomes with future exposures. Because clinical decisions are made and clinical decision support is triggered based on allergy details from the EHR, the allergy module needs to provide meaningful, accurate, timely, and comprehensive allergy information. Although the allergy section of the EHR must meet these requirements to guide appropriate clinical decisions and treatment plans, current EHR allergy modules have not achieved this standard. We urge EHR vendors to collaborate with allergists to optimize and modernize allergy documentation. A work group within the Adverse Reactions to Drugs, Biologicals, and Latex Committee of the American Academy of Allergy, Asthma & Immunology was formed to create recommendations for allergy documentation in the EHR. Whereas it is recognized that the term "allergy" is often used incorrectly because most adverse drug reactions (ADRs) are not true immune-mediated hypersensitivity reactions, "allergy" in this article includes allergies and hypersensitivities as well as side effects and intolerances. Our primary objective is to provide guidance for the current state of allergy documentation in the EHR. This guidance includes clarification of the definition of specific ADR types, reconciliation of confirmed ADRs, and removal of disproved or erroneous ADRs. This document includes a proposal for the creation, education, and implementation of a drug allergy labeling system that may allow for more accurate EHR documentation for improved patient safety.

The Use of Electronic Health Records to Study Drug-Induced Hypersensitivity Reactions from 2000 to 2021: A Systematic Review

Electronic health records (EHRs) have revolutionized the field of drug hypersensitivity reaction (DHR) research. In this systematic review, we assessed 140 articles from 2000-2021, classifying them under six themes: observational studies (n=61), clinical documentation (n=27), case management (n=22), clinical decision support (CDS) (n=18), case identification (n=9), and genetic studies (n=3). EHRs provide convenient access to millions of medical records, facilitating epidemiological studies of DHRs. Though the goal of CDS is to promote safe drug prescribing, allergy alerts must be designed and used in a way that supports this effort. Ultimately, accurate allergy documentation is essential for DHR prevention.

Deep-ADCA: Development and Validation of Deep Learning Model for Automated Diagnosis Code Assignment Using Clinical Notes in Electronic Medical Records

Currently, the International Classification of Diseases (ICD) codes are being used to improve clinical, financial, and administrative performance. Inaccurate ICD coding can lower the quality of care, and delay or prevent reimbursement. However, selecting the appropriate ICD code from a patient’s clinical history is time-consuming and requires expert knowledge. The rapid spread of electronic medical records (EMRs) has generated a large amount of clinical data and provides an opportunity to predict ICD codes using deep learning models. The main objective of this study was to use a deep learning-based natural language processing (NLP) model to accurately predict ICD-10 codes, which could help providers to make better clinical decisions and improve their level of service. We retrospectively collected clinical notes from five outpatient departments (OPD) from one university teaching hospital between January 2016 and December 2016. We applied NLP techniques, including global vectors, word to vectors, and embedding techniques to process the data. The dataset was split into two independent training and testing datasets consisting of 90% and 10% of the entire dataset, respectively. A convolutional neural network (CNN) model was developed, and the performance was measured using the precision, recall, and F-score. A total of 21,953 medical records were collected from 5016 patients. The performance of the CNN model for the five different departments was clinically satisfactory (Precision: 0.50~0.69 and recall: 0.78~0.91). However, the CNN model achieved the best performance for the cardiology department, with a precision of 69%, a recall of 89% and an F-score of 78%. The CNN model for predicting ICD-10 codes provides an opportunity to improve the quality of care. Implementing this model in real-world clinical settings could reduce the manual coding workload, enhance the efficiency of clinical coding, and support physicians in making better clinical decisions.

A narrative review on the validity of electronic health record-based research in epidemiology

Electronic health records (EHRs) are widely used in epidemiological research, but the validity of the results is dependent upon the assumptions made about the healthcare system, the patient, and the provider. In this review, we identify four overarching challenges in using EHR-based data for epidemiological analysis, with a particular emphasis on threats to validity. These challenges include representativeness of the EHR to a target population, the availability and interpretability of clinical and non-clinical data, and missing data at both the variable and observation levels. Each challenge reveals layers of assumptions that the epidemiologist is required to make, from the point of patient entry into the healthcare system, to the provider documenting the results of the clinical exam and follow-up of the patient longitudinally; all with the potential to bias the results of analysis of these data. Understanding the extent of as well as remediating potential biases requires a variety of methodological approaches, from traditional sensitivity analyses and validation studies, to newer techniques such as natural language processing. Beyond methods to address these challenges, it will remain crucial for epidemiologists to engage with clinicians and informaticians at their institutions to ensure data quality and accessibility by forming multidisciplinary teams around specific research projects.

The applications of eHealth technologies in the management of asthma and allergic diseases

Portable devices, such as smartphones and mobile Internet access have become ubiquitous in the last decades. The term 'eHealth' stands for electronic health. The tools included in the eHealth concept utilize phones, computers and the Internet and related applications to improve the health care industry. Implementation of eHealth technologies has been documented for the management of different chronic diseases, including asthma and allergic conditions. Clinicians and patients have gained opportunity to communicate in new ways, which could be used cost-effectively to improve disease control and quality of life of those affected. Additionally, these innovations bring new opportunities to academic researchers. For example, eHealth has allowed researchers to compile data points that were previously unavailable or difficult to access, and analyse them using novel tools, collectively described as 'big data'. The role of eHealth become more important since early 2020, due to the physical distancing rules and the restrictions on mobility that have been applied worldwide as a response to the coronavirus disease 2019 pandemic. In this review, we summarize the most recent developments in various eHealth platforms and their relevance to the speciality of allergy and immunology, from the point of view of three major stakeholders: clinicians, patients and researchers.

Validity of hospital ICD-10-GM codes to identify anaphylaxis

PURPOSE

Anaphylaxis (ANA) is an important adverse drug reaction. We examined positive predictive values (PPV) and other test characteristics of ICD-10-GM code algorithms for detecting ANA as used in a multinational safety study (PASS).

METHODS

We performed a cross-sectional study on routine data from a German academic hospital (2004-2019, age ≥ 18). Chart review was used for case verification. Potential cases were identified from the hospital administration system. The main outcome required at least one of the following: any type of specific in-hospital code (T78.2, T88.6, and T80.5) OR specific outpatient code in combination with a symptom code OR in-hospital non-specific code (T78.4, T88.7, and Y57.9) in combination with two symptom codes. PPV were calculated with 95% confidence interval. Sensitivity analyses modified type of codes, unit of analysis, verification criteria and time period. The most specific algorithm used only primary codes for ANA (numbers added in brackets).

RESULTS

Four hundred and sixteen eligible cases were evaluated, and 78 (37) potential ANA cases were identified. PPV were 62.8% (95% CI 51.1-73.5) (main) and 77.4% (58.9-90.4) (most specific). PPV from all modifications ranged from 12.9% to 80.6%. The sensitivity of the main algorithm was 66.2%, specificity 91.5%, and negative predictive value 92.6%. Corresponding figures for the most specific algorithm were 32.4%, 98.0%, and 87.0%.

CONCLUSIONS

The PPV of the main algorithm seems of acceptable validity for use in comparative safety research but will underestimate absolute risks by about a third. Restriction to primary discharge codes markedly improves PPV to the expense of reducing sensitivity.

Public Health and Epidemiology Informatics: Recent Research Trends Moving toward Public Health Data Science

Objectives : To introduce and summarize current research in the field of Public Health and Epidemiology Informatics.

Methods : PubMed searches of 2019 literature concerning public health and epidemiology informatics were conducted and the returned references were reviewed by the two section editors to select 14 candidate best papers. These papers were then peer-reviewed by external reviewers to allow the Editorial Committee a curated selection of the best papers.

Results : Among the 835 references retrieved from PubMed, two were finally selected as best papers. The first best paper leverages satellite images and deep learning to identify remote rural communities in low-income countries; the second paper describes the development of a worldwide human disease surveillance system based on near real-time news data from the GDELT project. Internet data and electronic health records are still widely used to detect and monitor disease activity. Identifying and targeting specific audiences for public health interventions is a growing subject of interest.

Conclusions : The ever-increasing amount of data available offers endless opportunities to develop methods and tools that could assist public health surveillance and intervention belonging to the growing field of public health Data Science. The transition from proofs of concept to real world applications and adoption by health authorities remains a difficult leap to make.

Challenges and opportunities for public health made possible by advances in natural language processing

Natural language processing (NLP) is a subfield of artificial intelligence devoted to understanding and generation of language. The recent advances in NLP technologies are enabling rapid analysis of vast amounts of text, thereby creating opportunities for health research and evidence-informed decision making. The analysis and data extraction from scientific literature, technical reports, health records, social media, surveys, registries and other documents can support core public health functions including the enhancement of existing surveillance systems (e.g. through faster identification of diseases and risk factors/at-risk populations), disease prevention strategies (e.g. through more efficient evaluation of the safety and effectiveness of interventions) and health promotion efforts (e.g. by providing the ability to obtain expert-level answers to any health related question). NLP is emerging as an important tool that can assist public health authorities in decreasing the burden of health inequality/inequity in the population. The purpose of this paper is to provide some notable examples of both the potential applications and challenges of NLP use in public health.