SemMedDB: a PubMed-scale repository of biomedical semantic predications

Kilicoglu, Halil; Shin, Dongwook; Fiszman, Marcelo; Rosemblat, Graciela; Rindflesch, Thomas C.

doi:10.1093/bioinformatics/bts591

Cited by 308 publications

(279 citation statements)

References 11 publications

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“…Besides treatment relations, identifying the cause of a condition or symptom is also of significance from a prevention perspective. Hence causative relations are also included in well known knowledge bases [15, 25] and also used in discovery applications [36, 42]. Thus in this effort our focus will be on predicting both treatment and causative relations.…”

Section: Background: Knowledge Acquisition and Approachesmentioning

confidence: 99%

“…That is, instead of looking at what a particular sentence conveys, we model the prediction problem at a global level and build models that output probability estimates of whether a pair of entities participates in a particular relation. Our models are trained with graph pattern features over the well-known knowledge graph SemMedDB [15] extracted from biomedical literature by researchers at the National Library of Medicine (NLM) using the rule based SemRep NLP tool [26]. …”

Section: Introductionmentioning

confidence: 99%

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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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Section: Background: Knowledge Acquisition and Approachesmentioning

confidence: 99%

Section: Introductionmentioning

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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“…In terms of SMDB-DP, there are three causes to the errors. First, the medical concepts in SemMedDB corpus are not precisely extracted (75% accuracy [22]), while Wiki-DP does not have this procedure and information loss. Second, through MetaMap, one disease name might be identified with several concepts.…”

Section: Further Analysis Of Diagnosis Predictionmentioning

confidence: 99%

“…However, SemMedDB has its own limitations. Kilicoglu et al [22] reported that NLM's SemRep, the tool used to build SemMedDB, only achieves 75% extraction accuracy. This means that SemMedDB itself contains many noisy extraction results.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Clinical Decision Support Systems with Public Knowledge Bases

Zhang

2017

Data and Information Management

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With vast amount of biomedical literature available online, doctors have the benefits of consulting the literature before making clinical decisions, but they are facing the daunting task of finding needles in haystacks. In this situation, it would be of great use to the doctors if an effective clinical decision support system is available to generate accurate queries and return a manageable size of highly useful articles. Existing studies showed the usefulness of patients' diagnosis information in supporting effective retrieval of relevant literature, but such diagnosis information is often missing in most cases. Furthermore, existing diagnosis prediction systems mainly focus on predicting a small range of diseases with well-formatted features, and it is still a great challenge to perform largescale automatic diagnosis predictions based on noisy medical records of the patient. In this paper, we propose automatic diagnosis prediction methods for enhancing the retrieval in a clinical decision support system, where the prediction is based on evidences automatically collected from publicly accessible online knowledge bases such as Wikipedia and Semantic MEDLINE Database (SemMedDB). The assumption is that relevant diseases and their corresponding symptoms co-occur more frequently in these knowledge bases. Our methods use Markov Random Field (MRF) model to identify diagnosis candidates in the knowledge bases, and their performance was evaluated using test collections from the Clinical Decision Support (CDS) track in TREC 2014TREC , 2015TREC , and 2016. The results show that our methods can automatically predict diagnosis with about 75% accuracy, and such predictions can significantly improve the related biomedical literatures retrieval. Our methods can generate comparable retrieval results to the state-of-the-art methods, which utilize much more complicated methods and some manually crafted medical knowledge. One possible future work is to apply these methods in collaboration with real doctors.Notes: a portion of this work was published in iConference 2017 as a poster, which won the best poster award. This paper greatly expands the research scope over that poster.

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“…A formal knowledge representation, using the Resource Description Framework (RDF) and the Web Ontology Language (OWL), can help better organize and deliver quality health information. In the biomedical domain, there has been several efforts making Semantic Web knowledge bases (KBs), such as BioNELL [3] and SemMedDB [4]. …”

Section: Introductionmentioning

confidence: 99%

OC-2-KB: A software pipeline to build an evidence-based obesity and cancer knowledge base

Lossio-Ventura

Hogan

Modave

et al. 2017

2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Obesity has been linked to several types of cancer. Access to adequate health information activates people’s participation in managing their own health, which ultimately improves their health outcomes. Nevertheless, the existing online information about the relationship between obesity and cancer is heterogeneous and poorly organized. A formal knowledge representation can help better organize and deliver quality health information. Currently, there are several efforts in the biomedical domain to convert unstructured data to structured data and store them in Semantic Web knowledge bases (KB). In this demo paper, we present, OC-2-KB (Obesity and Cancer to Knowledge Base), a system that is tailored to guide the automatic KB construction for managing obesity and cancer knowledge from free-text scientific literature (i.e., PubMed abstracts) in a systematic way. OC-2-KB has two important modules which perform the acquisition of entities and the extraction then classification of relationships among these entities. We tested the OC-2-KB system on a data set with 23 manually annotated obesity and cancer PubMed abstracts and created a preliminary KB with 765 triples. We conducted a preliminary evaluation on this sample of triples and reported our evaluation results.

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SemMedDB: a PubMed-scale repository of biomedical semantic predications

Abstract: kilicogluh@mail.nih.gov.

Cited by 308 publications

References 11 publications

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

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

Enhancing Clinical Decision Support Systems with Public Knowledge Bases

OC-2-KB: A software pipeline to build an evidence-based obesity and cancer knowledge base

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