scholarly journals Early Detection of Adverse Drug Reactions in Social Health Networks: A Natural Language Processing Pipeline for Signal Detection

10.2196/11264 ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. e11264 ◽  
Author(s):  
Azadeh Nikfarjam ◽  
Julia D Ransohoff ◽  
Alison Callahan ◽  
Erik Jones ◽  
Brian Loew ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Early Detection ◽  
Natural Language ◽  
Signal Detection ◽  
Adverse Drug Reactions ◽  
Language Processing ◽  
Social Health ◽  
Drug Reactions ◽  
Health Networks ◽  
Processing Pipeline

scholarly journals Early Detection of Adverse Drug Reactions in Social Health Networks: A Natural Language Processing Pipeline for Signal Detection (Preprint)

2018 ◽  
Author(s):  
Azadeh Nikfarjam ◽  
Julia D Ransohoff ◽  
Alison Callahan ◽  
Erik Jones ◽  
Brian Loew ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Adverse Drug Reactions ◽  
Language Processing ◽  
Signal Generation ◽  
Social Health ◽  
Drug Reactions ◽  
Health Networks ◽  
Health Concerns ◽  
Patient Reports

BACKGROUND Adverse drug reactions (ADRs) occur in nearly all patients on chemotherapy, causing morbidity and therapy disruptions. Detection of such ADRs is limited in clinical trials, which are underpowered to detect rare events. Early recognition of ADRs in the postmarketing phase could substantially reduce morbidity and decrease societal costs. Internet community health forums provide a mechanism for individuals to discuss real-time health concerns and can enable computational detection of ADRs. OBJECTIVE The goal of this study is to identify cutaneous ADR signals in social health networks and compare the frequency and timing of these ADRs to clinical reports in the literature. METHODS We present a natural language processing-based, ADR signal-generation pipeline based on patient posts on Internet social health networks. We identified user posts from the Inspire health forums related to two chemotherapy classes: erlotinib, an epidermal growth factor receptor inhibitor, and nivolumab and pembrolizumab, immune checkpoint inhibitors. We extracted mentions of ADRs from unstructured content of patient posts. We then performed population-level association analyses and time-to-detection analyses. RESULTS Our system detected cutaneous ADRs from patient reports with high precision (0.90) and at frequencies comparable to those documented in the literature but an average of 7 months ahead of their literature reporting. Known ADRs were associated with higher proportional reporting ratios compared to negative controls, demonstrating the robustness of our analyses. Our named entity recognition system achieved a 0.738 microaveraged F-measure in detecting ADR entities, not limited to cutaneous ADRs, in health forum posts. Additionally, we discovered the novel ADR of hypohidrosis reported by 23 patients in erlotinib-related posts; this ADR was absent from 15 years of literature on this medication and we recently reported the finding in a clinical oncology journal. CONCLUSIONS Several hundred million patients report health concerns in social health networks, yet this information is markedly underutilized for pharmacosurveillance. We demonstrated the ability of a natural language processing-based signal-generation pipeline to accurately detect patient reports of ADRs months in advance of literature reporting and the robustness of statistical analyses to validate system detections. Our findings suggest the important contributions that social health network data can play in contributing to more comprehensive and timely pharmacovigilance.

scholarly journals Automatic Extraction of Adverse Drug Reactions from Summary of Product Characteristics

2021 ◽  
Vol 11 (6) ◽  
pp. 2663
Author(s):  
Zhengru Shen ◽  
Marco Spruit
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Adverse Drug Reactions ◽  
Language Processing ◽  
European Medicines Agency ◽  
Drug Reactions ◽  
Clinical Practices ◽  
Product Characteristics ◽  
Textual Information ◽  
Summary Of Product Characteristics

The summary of product characteristics from the European Medicines Agency is a reference document on medicines in the EU. It contains textual information for clinical experts on how to safely use medicines, including adverse drug reactions. Using natural language processing (NLP) techniques to automatically extract adverse drug reactions from such unstructured textual information helps clinical experts to effectively and efficiently use them in daily practices. Such techniques have been developed for Structured Product Labels from the Food and Drug Administration (FDA), but there is no research focusing on extracting from the Summary of Product Characteristics. In this work, we built a natural language processing pipeline that automatically scrapes the summary of product characteristics online and then extracts adverse drug reactions from them. Besides, we have made the method and its output publicly available so that it can be reused and further evaluated in clinical practices. In total, we extracted 32,797 common adverse drug reactions for 647 common medicines scraped from the Electronic Medicines Compendium. A manual review of 37 commonly used medicines has indicated a good performance, with a recall and precision of 0.99 and 0.934, respectively.

scholarly journals Developing A Deep Learning Natural Language Processing Algorithm For Automated Reporting Of Adverse Drug Reactions

2021 ◽  
Author(s):  
Christopher McMaster ◽  
Julia Chan ◽  
David FL Liew ◽  
Elizabeth Su ◽  
Albert G Frauman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Adverse Events ◽  
Natural Language ◽  
Adverse Drug Reactions ◽  
Language Processing ◽  
Processing Algorithm ◽  
Drug Reactions ◽  
Discharge Summaries ◽  
Natural Language Processing Algorithm

The detection of adverse drug reactions (ADRs) is critical to our understanding of the safety and risk-benefit profile of medications. With an incidence that has not changed over the last 30 years, ADRs are a significant source of patient morbidity, responsible for 5-10% of acute care hospital admissions worldwide. Spontaneous reporting of ADRs has long been the standard method of reporting, however this approach is known to have high rates of under-reporting, a problem that limits pharmacovigilance efforts. Automated ADR reporting presents an alternative pathway to increase reporting rates, although this may be limited by over-reporting of other drug-related adverse events. We developed a deep learning natural language processing algorithm to identify ADRs in discharge summaries at a single academic hospital centre. Our model was developed in two stages: first, a pre-trained model (DeBERTa) was further pre-trained on 150,000 unlabelled discharge summaries; secondly, this model was fine-tuned to detect ADR mentions in a corpus of 861 annotated discharge summaries. To ensure that our algorithm could differentiate ADRs from other drug-related adverse events, the annotated corpus was enriched for both validated ADR reports and confounding drug-related adverse events using. The final model demonstrated good performance with a ROC-AUC of 0.934 (95% CI 0.931 - 0.955) for the task of identifying discharge summaries containing ADR mentions.

Triage and diagnosis of COVID-19 from medical social media (Preprint)

2021 ◽  
Author(s):  
Abul Hasan ◽  
Mark Levene ◽  
David Weston ◽  
Renate Fromson ◽  
Nicolas Koslover ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Learning Models ◽  
Rule Based ◽  
Additional Information ◽  
Processing Pipeline ◽  
Machine Learning Models

BACKGROUND The COVID-19 pandemic has created a pressing need for integrating information from disparate sources, in order to assist decision makers. Social media is important in this respect, however, to make sense of the textual information it provides and be able to automate the processing of large amounts of data, natural language processing methods are needed. Social media posts are often noisy, yet they may provide valuable insights regarding the severity and prevalence of the disease in the population. In particular, machine learning techniques for triage and diagnosis could allow for a better understanding of what social media may offer in this respect. OBJECTIVE This study aims to develop an end-to-end natural language processing pipeline for triage and diagnosis of COVID-19 from patient-authored social media posts, in order to provide researchers and other interested parties with additional information on the symptoms, severity and prevalence of the disease. METHODS The text processing pipeline first extracts COVID-19 symptoms and related concepts such as severity, duration, negations, and body parts from patients’ posts using conditional random fields. An unsupervised rule-based algorithm is then applied to establish relations between concepts in the next step of the pipeline. The extracted concepts and relations are subsequently used to construct two different vector representations of each post. These vectors are applied separately to build support vector machine learning models to triage patients into three categories and diagnose them for COVID-19. RESULTS We report that Macro- and Micro-averaged F_{1\ }scores in the range of 71-96% and 61-87%, respectively, for the triage and diagnosis of COVID-19, when the models are trained on human labelled data. Our experimental results indicate that similar performance can be achieved when the models are trained using predicted labels from concept extraction and rule-based classifiers, thus yielding end-to-end machine learning. Also, we highlight important features uncovered by our diagnostic machine learning models and compare them with the most frequent symptoms revealed in another COVID-19 dataset. In particular, we found that the most important features are not always the most frequent ones. CONCLUSIONS Our preliminary results show that it is possible to automatically triage and diagnose patients for COVID-19 from natural language narratives using a machine learning pipeline, in order to provide additional information on the severity and prevalence of the disease through the eyes of social media.

scholarly journals A natural language processing pipeline for pairing measurements uniquely across free-text CT reports

2015 ◽  
Vol 53 ◽  
pp. 36-48 ◽  
Author(s):  
Merlijn Sevenster ◽  
Jeffrey Bozeman ◽  
Andrea Cowhy ◽  
William Trost
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Free Text ◽  
Processing Pipeline

scholarly journals Automating curation using a natural language processing pipeline

2008 ◽  
Vol 9 (Suppl 2) ◽  
pp. S10 ◽  
Author(s):  
Beatrice Alex ◽  
Claire Grover ◽  
Barry Haddow ◽  
Mijail Kabadjov ◽  
Ewan Klein ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Processing Pipeline

scholarly journals Automated pancreatic cyst screening using natural language processing: a new tool in the early detection of pancreatic cancer

HPB ◽  
2015 ◽  
Vol 17 (5) ◽  
pp. 447-453 ◽  
Author(s):  
Alexandra M. Roch ◽  
Saeed Mehrabi ◽  
Anand Krishnan ◽  
Heidi E. Schmidt ◽  
Joseph Kesterson ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Natural Language Processing ◽  
Early Detection ◽  
Natural Language ◽  
Language Processing ◽  
Pancreatic Cyst
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