Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure

Abstract Objective An adverse drug event (ADE) refers to an injury resulting from medical intervention related to a drug including harm caused by drugs or from the usage of drugs. Extracting ADEs from clinical records can help physicians associate adverse events to targeted drugs. Materials and Methods We proposed a cascading architecture to recognize medical concepts including ADEs, drug names, and entities related to drugs. The architecture includes a preprocessing method and an ensemble of conditional random fields (CRFs) and neural network–based models to respectively address the challenges of surrogate string and overlapping annotation boundaries observed in the employed ADEs and medication extraction (ADME) corpus. The effectiveness of applying different pretrained and postprocessed word embeddings for the ADME task was also studied. Results The empirical results showed that both CRFs and neural network–based models provide promising solution for the ADME task. The neural network–based models particularly outperformed CRFs in concept types involving narrative descriptions. Our best run achieved an overall micro F-score of 0.919 on the employed corpus. Our results also suggested that the Global Vectors for word representation embedding in general domain provides a very strong baseline, which can be further improved by applying the principal component analysis to generate more isotropic vectors. Conclusions We have demonstrated that the proposed cascading architecture can handle the problem of overlapped annotations and further improve the overall recall and F-scores because the architecture enables the developed models to exploit more context information and forms an ensemble for creating a stronger recognizer.

Download Full-text

Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure (Preprint)

10.2196/preprints.9170 ◽

2017 ◽

Cited By ~ 1

Author(s):

Ahmad P Tafti ◽

Jonathan Badger ◽

Eric LaRose ◽

Ehsan Shirzadi ◽

Andrea Mahnke ◽

...

Keyword(s):

Machine Learning ◽

Social Media ◽

Big Data ◽

Language Processing ◽

Adverse Drug Events ◽

Learning Strategy ◽

Distributed Database ◽

Biomedical Literature ◽

Drug Event ◽

Health Related

BACKGROUND The study of adverse drug events (ADEs) is a tenured topic in medical literature. In recent years, increasing numbers of scientific articles and health-related social media posts have been generated and shared daily, albeit with very limited use for ADE study and with little known about the content with respect to ADEs. OBJECTIVE The aim of this study was to develop a big data analytics strategy that mines the content of scientific articles and health-related Web-based social media to detect and identify ADEs. METHODS We analyzed the following two data sources: (1) biomedical articles and (2) health-related social media blog posts. We developed an intelligent and scalable text mining solution on big data infrastructures composed of Apache Spark, natural language processing, and machine learning. This was combined with an Elasticsearch No-SQL distributed database to explore and visualize ADEs. RESULTS The accuracy, precision, recall, and area under receiver operating characteristic of the system were 92.7%, 93.6%, 93.0%, and 0.905, respectively, and showed better results in comparison with traditional approaches in the literature. This work not only detected and classified ADE sentences from big data biomedical literature but also scientifically visualized ADE interactions. CONCLUSIONS To the best of our knowledge, this work is the first to investigate a big data machine learning strategy for ADE discovery on massive datasets downloaded from PubMed Central and social media. This contribution illustrates possible capacities in big data biomedical text analysis using advanced computational methods with real-time update from new data published on a daily basis.

Download Full-text

Adverse Drug Event-Related Emergency Department Visits Associated With Complex Chronic Conditions

PEDIATRICS ◽

10.1542/peds.2013-3060d ◽

2014 ◽

Vol 133 (6) ◽

pp. X25-X25

Keyword(s):

Emergency Department ◽

Adverse Drug Event ◽

Chronic Conditions ◽

Emergency Department Visits ◽

Drug Event ◽

Complex Chronic Conditions

Download Full-text

A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

Healthcare ◽

10.3390/healthcare8030234 ◽

2020 ◽

Vol 8 (3) ◽

pp. 234 ◽

Cited By ~ 3

Author(s):

Hyun Yoo ◽

Soyoung Han ◽

Kyungyong Chung

Keyword(s):

Neural Network ◽

Big Data ◽

Fourier Transform ◽

Fast Fourier Transform ◽

Real Time ◽

Normal Control ◽

Input Data ◽

Deep Neural Network ◽

Pattern Mining ◽

F Measure

Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

Download Full-text

Adverse drug event-related mortality in Japan

Reactions Weekly ◽

10.1007/s40278-021-94096-4 ◽

2021 ◽

Vol 1851 (1) ◽

pp. 6-6

Keyword(s):

Adverse Drug Event ◽

Drug Event ◽

Related Mortality

Download Full-text

Using ActionADE to create information continuity to reduce re-exposures to harmful medications: study protocol for a randomized controlled trial

Trials ◽

10.1186/s13063-021-05061-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Jeffrey P. Hau ◽

Penelope M. A. Brasher ◽

Amber Cragg ◽

Serena Small ◽

Maeve Wickham ◽

...

Keyword(s):

Health Information ◽

Adverse Drug Event ◽

Primary Outcome ◽

Adverse Drug Events ◽

Primary Objective ◽

Drug Event ◽

Controlled Trials ◽

Drug Reactions ◽

Main Trial ◽

Randomized Controlled

Abstract Background Repeat exposures to culprit medications are a common cause of preventable adverse drug events. Health information technologies have the potential to reduce repeat adverse drug events by improving information continuity. However, they rarely interoperate to ensure providers can view adverse drug events documented in other systems. We designed ActionADE to enable rapid documentation of adverse drug events and communication of standardized information across health sectors by integrating with legacy systems. We will leverage ActionADE’s implementation to conduct two parallel, randomized trials: patients with adverse drug reactions in the main trial and those diagnosed with non-adherence in a secondary trial. Primary objective of the main trial is to evaluate the effects of providing information continuity about adverse drug reactions on culprit medication re-dispensations over 12 months. Primary objective of the secondary trial is to evaluate the effect of providing information continuity on adherence over 12 months. Methods We will conduct two parallel group, triple-blind randomized controlled trials in participating hospitals in British Columbia, Canada. We will enroll adults presenting to hospital with an adverse drug event to prescribed outpatient medication. Clinicians will document the adverse drug event in ActionADE. The software will use an algorithm to determine patient eligibility and allocate eligible patients to experimental or control. In the experimental arm, ActionADE will transmit information to PharmaNet, where adverse drug event information will be displayed in community pharmacies when re-dispensations are attempted. In the control arm, ActionADE will retain information in the local record. We will enroll 3600 adults with an adverse drug reaction into the main trial. The main trial’s primary outcome is re-dispensation of a culprit or same-class medication within 12 months; the secondary trial’s primary outcome will be adherence to culprit medication. Secondary outcomes include health services utilization and mortality. Discussion These studies have the potential to guide policy decisions and investments needed to drive health information technology integrations to prevent repeat adverse drug events. We present an example of how a health information technology implementation can be leveraged to conduct pragmatic randomized controlled trials. Trial registration ClinicalTrials.gov NCT04568668, NCT04574648. Registered on 1 October 2020.

Download Full-text