Extracting Adverse Drug Events from Text Using Human Advice

Odom, Phillip; Bangera, Vishal; Khot, Tushar; Page, David; Natarajan, Sriraam

doi:10.1007/978-3-319-19551-3_26

Cited by 14 publications

(10 citation statements)

References 16 publications

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“…Information extraction is the process of automatically deriving high-quality structured information from text. A range of applications has been described in the medical application area, for example for extracting adverse drug events from text [3] or for symptom extraction from texts on rare diseases [4]. However, clinical information extraction from patient records is still under-represented and underdeveloped.…”

Section: Information Extractionmentioning

confidence: 99%

“…It is one of the most popular enterprise search engines. 3 Solr runs as a standalone full-text search server and uses the Lucene Java search library at its core for full-text indexing and faceted search. We chose the Solr system mainly because of some interesting features to faceted search, namely a proprietary query language that supports structured and textual search (cf.…”

Section: System Architecturementioning

confidence: 99%

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An architecture of open-source tools to combine textual information extraction, faceted search and information visualisation

Sonntag

Profitlich

2019

Artificial Intelligence in Medicine

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This article presents our steps to integrate complex and partly unstructured medical data into a clinical research database with subsequent decision support. Our main application is an integrated faceted search tool, accompanied by the visualisation of results of automatic information extraction from textual documents. We describe the details of our technical architecture (open-source tools), to be replicated at other universities, research institutes, or hospitals. Our exemplary use cases are nephrology and mammography. The software was first developed in the nephrology domain and then adapted to the mammography use case. We report on these case studies, illustrating how the application can be used by a clinician and which questions can be answered. We show that our architecture and the employed software modules are suitable for both areas of application with a limited amount of adaptations. For example, in nephrology we try to answer questions about the temporal characteristics of event sequences to gain significant insight from the data for cohort selection. We present a versatile time-line tool that enables the user to explore relations between a multitude of diagnosis and laboratory values.

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Section: Information Extractionmentioning

confidence: 99%

Section: System Architecturementioning

confidence: 99%

An architecture of open-source tools to combine textual information extraction, faceted search and information visualisation

Sonntag

Profitlich

2019

Artificial Intelligence in Medicine

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“…In terms of the resources forin silico DDI extraction or prediction, they commonly consist of textual and structural data, in which textual data can refer to the literatures, electronic health records (EHRs) or comments in social media, while chemical, molecular and pharmacological properties are included in structural data . However, the former is more likely to be used for detecting ADEs from the reported literatures, EHRs or comments in social media . Feature vector‐based and similarity‐based approaches are frequently used in DDI prediction.…”

Section: What Is Known and Objectivesmentioning

confidence: 99%

“…A kernel-based SVM classifier can be defined using formula (4), in which α i is the coefficient of i, and K (x, x i ) is the kernel function. According to the Lagrangian expression, parameter α i is trained using formula (5). Similarly, the testing matrix was constructed using the similarity between testing drug pairs and training drug pairs in the training matrix.…”

Section: Support Vector Machinementioning

confidence: 99%

Similarity-based machine learning support vector machine predictor of drug-drug interactions with improved accuracies

et al. 2018

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Summary What is known and objective Drug‐drug interactions (DDI) are frequent causes of adverse clinical drug reactions. Efforts have been directed at the early stage to achieve accurate identification of DDI for drug safety assessments, including the development of in silico predictive methods. In particular, similarity‐based in silico methods have been developed to assess DDI with good accuracies, and machine learning methods have been employed to further extend the predictive range of similarity‐based approaches. However, the performance of a developed machine learning method is lower than expectations partly because of the use of less diverse DDI training data sets and a less optimal set of similarity measures. Method In this work, we developed a machine learning model using support vector machines (SVMs) based on the literature‐reported established set of similarity measures and comprehensive training data sets. The established similarity measures include the 2D molecular structure similarity, 3D pharmacophoric similarity, interaction profile fingerprint (IPF) similarity, target similarity and adverse drug effect (ADE) similarity, which were extracted from well‐known databases, such as DrugBank and Side Effect Resource (SIDER). A pairwise kernel was constructed for the known and possible drug pairs based on the five established similarity measures and then used as the input vector of the SVM. Result The 10‐fold cross‐validation studies showed a predictive performance of AUROC >0.97, which is significantly improved compared with the AUROC of 0.67 of an analogously developed machine learning model. Our study suggested that a similarity‐based SVM prediction is highly useful for identifying DDI. Conclusion in silico methods based on multifarious drug similarities have been suggested to be feasible for DDI prediction in various studies. In this way, our pairwise kernel SVM model had better accuracies than some previous works, which can be used as a pharmacovigilance tool to detect potential DDI.

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“…In our prior work, we have performed ADE discovery and subgroup discovery from electronic health records (EHR) [4] and text-mining of medical journal abstracts [5]. These approaches address the problem of post-marketing surveillance , that is, they seek to exploit the new information available after a drug has been approved and has been prescribed to larger, more diverse populations.…”

Section: Introductionmentioning

confidence: 99%

Drug‐Drug Interaction Discovery: Kernel Learning from Heterogeneous Similarities

et al. 2018

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We develop a pipeline to mine complex drug interactions by combining different similarities and interaction types (molecular, structural, phenotypic, genomic etc). Our goal is to learn an optimal kernel from these heterogeneous similarities in a supervised manner. We formulate an extensible framework that can easily integrate new interaction types into a rich model. The core of our pipeline features a novel kernel-learning approach that tunes the weights of the heterogeneous similarities, and fuses them into a Similarity-based Kernel for Identifying Drug-Drug interactions and Discovery, or SKID3. Experimental evaluation on the DrugBank database shows that SKID3 effectively combines similarities generated from chemical reaction pathways (which generally improve precision) and molecular and structural fingerprints (which generally improve recall) into a single kernel that gets the best of both worlds, and consequently demonstrates the best performance.

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Extracting Adverse Drug Events from Text Using Human Advice

Cited by 14 publications

References 16 publications

An architecture of open-source tools to combine textual information extraction, faceted search and information visualisation

An architecture of open-source tools to combine textual information extraction, faceted search and information visualisation

Similarity-based machine learning support vector machine predictor of drug-drug interactions with improved accuracies

Drug‐Drug Interaction Discovery: Kernel Learning from Heterogeneous Similarities

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