Extracting drug-drug interaction articles from MEDLINE to improve the content of drug databases.
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Abstract:
Drug-drug interaction systems exhibit low signal-to-noise ratios because of the amount of clinically insignificant or inaccurate information they contain. MEDLINE represents a respected source of peer-reviewed biomedical citations that potentially might serve as a valuable source of drug-drug interaction information, if relevant articles could be pinpointed effectively and efficiently. We evaluated the classification capability of Support Vector Machines as a method for locating articles about drug interactions. We used a corpus of "positive" and"negative" drug interaction citations to generate datasets composed of MeSH terms, CUI-tagged title and abstract text, and stemmed text words. The study showed that automated classification techniques have the potential to perform at least as well as PubMed in identifying drug-drug interaction articles.Keywords:
Drug-drug interaction
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The automatic removal of suffixes from words in English is of particular interest in the field of information retrieval. An algorithm for suffix stripping is described, which has been implemented as a short, fast program in BCPL. Although simple, it performs slightly better than a much more elaborate system with which it has been compared. It effectively works by treating complex suffixes as compounds made up of simple suffixes, and removing the simple suffixes in a number of steps. In each step the removal of the suffix is made to depend upon the form of the remaining stem, which usually involves a measure of its syllable length.
Stripping (fiber)
Suffix array
Generalized suffix tree
Compressed suffix array
SIMPLE algorithm
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LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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Multiclass classification
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Adverse drug events (ADEs) create a serious problem causing substantial harm to patients. An executable standardized knowledgebase of drug-ADE relations which is publicly available would be valuable so that it could be used for ADE detection. The literature is an important source that could be used to generate a knowledgebase of drug-ADE pairs. In this paper, we report on a method that automatically determines whether a specific adverse event (AE) is caused by a specific drug based on the content of PubMed citations. A drug-ADE classification method was initially developed to detect neutropenia based on a pre-selected set of drugs. This method was then applied to a different set of 76 drugs to determine if they caused neutropenia. For further proof of concept this method was applied to 48 drugs to determine whether they caused another AE, myocardial infarction. Results showed that AUROC was 0.93 and 0.86 respectively.
Adverse drug event
Adverse drug reaction
Executable
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Drug-drug interaction
Kernel (algebra)
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The UMLS Metathesaurus, the largest thesaurus in the biomedical domain, provides a representation of biomedical knowledge consisting of concepts classified by semantic type and both hierarchical and non-hierarchical relationships among the concepts. This knowledge has proved useful for many applications including decision support systems, management of patient records, information retrieval (IR) and data mining. Gaining effective access to the knowledge is critical to the success of these applications. This paper describes MetaMap, a program developed at the National Library of Medicine (NLM) to map biomedical text to the Metathesaurus or, equivalently, to discover Metathesaurus concepts referred to in text. MetaMap uses a knowledge intensive approach based on symbolic, natural language processing (NLP) and computational linguistic techniques. Besides being applied for both IR and data mining applications, MetaMap is one of the foundations of NLM's Indexing Initiative System which is being applied to both semi-automatic and fully automatic indexing of the biomedical literature at the library.
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Thesaurus
Controlled vocabulary
National library
Representation
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Free text fields are often used to store clinical drug data in electronic health records. The use of free text facilitates rapid data entry by the clinician. Errors in spelling, abbreviations, and jargon, however, limit the utility of these data. We designed and implemented an algorithm, using open source tools and RxNorm, to extract and normalize drug data stored in free text fields of an anesthesia electronic health record. The algorithm was developed using a training set containing drug data from 49,518 cases, and validated using a validation set containing data from 14,655 cases. Overall sensitivity and specificity for the validation set were 92.2% and 95.7% respectively. The mains sources of error were misspellings and unknown but valid drug names. These preliminary results demonstrate that free text clinical drug data can be efficiently extracted and mapped to a controlled drug nomenclature.
Text Messaging
Jargon
Spelling
Data set
Data extraction
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One of the most difficult challenges in precision medicine is determining the best treatment strategy for each patient based on personal information. Since drug response prediction in vitro is extremely expensive, time-consuming and virtually impossible, and because there are so many cell lines and drug data, computational methods are needed.MinDrug is a method for predicting anti-cancer drug response which try to identify the best subset of drugs that are the most similar to other drugs. MinDrug predicts the anti-cancer drug response on a new cell line using information from drugs in this subset and their connections to other drugs. MinDrug employs a heuristic star algorithm to identify an optimal subset of drugs and a regression technique known as Elastic-Net approaches to predict anti-cancer drug response in a new cell line. To test MinDrug, we use both statistical and biological methods to assess the selected drugs. MinDrug is also compared to four state-of-the-art approaches using various k-fold cross-validations on two large public datasets: GDSC and CCLE. MinDrug outperforms the other approaches in terms of precision, robustness and speed. Furthermore, we compare the evaluation results of all the approaches with an external dataset with a statistical distribution that is not exactly the same as the training data. The results show that MinDrug continues to outperform the other approaches.MinDrug's source code can be found at https://github.com/yassaee/MinDrug.Supplementary data are available at Bioinformatics online.
Robustness
Drug response
Elastic net regularization
Cancer cell lines
Cancer drugs
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