Using semantic predications to uncover drug–drug interactions in clinical data

Zhang, Rui; Cairelli, Michael J.; Fiszman, Marcelo; Rosemblat, Graciela; Kilicoglu, Halil; Rindflesch, Thomas C.; Pakhomov, Serguei; Melton, Genevieve B.

doi:10.1016/j.jbi.2014.01.004

Cited by 47 publications

(43 citation statements)

References 40 publications

(49 reference statements)

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“…NER has also been utilized in the medical field to assess interactions between drugs [29,30], the action and side effects of drugs [31,32], diagnostic classifications [33], and in the search and classification of biomedical entities [34].…”

Section: Related Workmentioning

confidence: 99%

Towards Effective Entity Extraction of Scientific Documents using Discriminative Linguistic Features

2019

KSII TIIS

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Named entity recognition (NER) is an important technique for improving the performance of data mining and big data analytics. In previous studies, NER systems have been employed to identify named-entities using statistical methods based on prior information or linguistic features; however, such methods are limited in that they are unable to recognize unregistered or unlearned objects. In this paper, a method is proposed to extract objects, such as technologies, theories, or person names, by analyzing the collocation relationship between certain words that simultaneously appear around specific words in the abstracts of academic journals. The method is executed as follows. First, the data is preprocessed using data cleaning and sentence detection to separate the text into single sentences. Then, part-of-speech (POS) tagging is applied to the individual sentences. After this, the appearance and collocation information of the other POS tags is analyzed, excluding the entity candidates, such as nouns. Finally, an entity recognition model is created based on analyzing and classifying the information in the sentences.

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Section: Related Workmentioning

confidence: 99%

Towards Effective Entity Extraction of Scientific Documents using Discriminative Linguistic Features

2019

KSII TIIS

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“…It is also large in that it has 14.3 million unique edges 1 connecting over 3 million nodes. It has already been used for literature based discovery and analysis of clinical documents [6, 7, 19, 42, 43]. …”

Section: Semantic Patterns Over Knowledge Graphsmentioning

confidence: 99%

Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations

Bakal

Talari

Kakani

et al. 2018

Journal of Biomedical Informatics

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Background Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying different causal relations between biomedical entities is also critical to understand biomedical processes. Generally, natural language processing (NLP) and machine learning are used to predict specific relations between any given pair of entities using the distant supervision approach. Objective To build high accuracy supervised predictive models to predict previously unknown treatment and causative relations between biomedical entities based only on semantic graph pattern features extracted from biomedical knowledge graphs. Methods We used 7,000 treats and 2,918 causes hand-curated relations from the UMLS Metathesaurus to train and test our models. Our graph pattern features are extracted from simple paths connecting biomedical entities in the SemMedDB graph (based on the well-known SemMedDB database made available by the U.S. National Library of Medicine). Using these graph patterns connecting biomedical entities as features of logistic regression and decision tree models, we computed mean performance measures (precision, recall, F-score) over 100 distinct 80%–20% train-test splits of the datasets. For all experiments, we used a positive:negative class imbalance of 1:10 in the test set to model relatively more realistic scenarios. Results Our models predict treats and causes relations with high F-scores of 99% and 90% respectively. Logistic regression model coefficients also help us identify highly discriminative patterns that have an intuitive interpretation. We are also able to predict some new plausible relations based on false positives that our models scored highly based on our collaborations with two physician co-authors. Finally, our decision tree models are able to retrieve over 50% of treatment relations from a recently created external dataset. Conclusions We employed semantic graph patterns connecting pairs of candidate entities in a knowledge graph as features to predict treatment/causative relations between them. We provide what we believe is the first evidence in direct prediction of biomedical relations based on graph features. Our work complements lexical pattern based approaches in that the graph patterns can be used as additional features for weakly supervised relation prediction.

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“…In its latest release (as of December 31th, 2016), it contains about 89 million relations extracted from more than 26 million abstracts. It has been used for a variety of tasks, such as clinical decision support (Jonnalagadda et al, 2013), uncovering potential drug interactions in clinical data (Zhang et al, 2014), supporting gene regulatory network construction (Chen et al, 2014), and medical question answering (Hristovski et al, 2015). It also forms the back-end for the Semantic MEDLINE application , which integrates semantic relations with automatic abstractive summarization (Fiszman et al, 2004), and visualization, to enable the user navigate biomedical literature through concepts and their relations.…”

Section: Literature-scale Relation Extractionmentioning

confidence: 99%

Biomedical Text Mining for Research Rigor and Integrity: Tasks, Challenges, Directions

Kilicoglu

2017

Preprint

Self Cite

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An estimated quarter of a trillion US dollars is invested in the biomedical research enterprise annually. There is growing alarm that a significant portion of this investment is wasted, due to problems in reproducibility of research findings and in the rigor and integrity of research conduct and reporting. Recent years have seen a flurry of activities focusing on standardization and guideline development to enhance the reproducibility and rigor of biomedical research. Research activity is primarily communicated via textual artifacts, ranging from grant applications to journal publications. These artifacts can be both the source and the end result of practices leading to research waste. For example, an article may describe a poorly designed experiment, or the authors may reach conclusions not supported by the evidence presented. In this article, we pose the question of whether biomedical text mining techniques can assist the stakeholders in the biomedical research enterprise in doing their part towards enhancing research integrity and rigor. In particular, we identify four key areas in which text mining techniques can make a significant contribution: plagiarism/fraud detection, ensuring adherence to reporting guidelines, managing information overload, and accurate citation/enhanced bibliometrics. We review the existing methods and tools for specific tasks, if they exist, or discuss relevant research that can provide guidance for future work. With the exponential increase in biomedical research output and the ability of text mining approaches to perform automatic tasks at large scale, we propose that such approaches can add checks and balances that promote responsible research practices and can provide significant benefits for the biomedical research enterprise.

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Using semantic predications to uncover drug–drug interactions in clinical data

Cited by 47 publications

References 40 publications

Towards Effective Entity Extraction of Scientific Documents using Discriminative Linguistic Features

Towards Effective Entity Extraction of Scientific Documents using Discriminative Linguistic Features

Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations

Biomedical Text Mining for Research Rigor and Integrity: Tasks, Challenges, Directions

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