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Literature DB >> 30815154

Mining Disease-Symptom Relation from Massive Biomedical Literature and Its Application in Severe Disease Diagnosis.

Eryu Xia¹, Wen Sun¹, Jing Mei¹, Enliang Xu¹, Ke Wang¹, Yong Qin¹.
1. IBM Research, Beijing, China.

Abstract

Disease-symptom relation is an important biomedical relation that can be used for clinical decision support including building medical diagnostic systems. Here we present a study on mining disease-symptom relation from massive biomedical literature and constructing biomedical knowledge graph from the relation. From 15,970,134 MEDLINE/PubMed citation records, occurrences of 8,514 disease concepts from the Human Disease Ontology and 842 symptom concepts from the Symptom Ontology and their relation were analyzed and characterized. We improve previous disease-symptom relation mining work by: (1) leveraging the hierarchy information of concepts in medical entity association discovery; and (2) including more exquisite relationship with weights between entities for knowledge graph construction. A medical diagnostic system for severe disease diagnosis was implemented based on the constructed knowledge graph and achieved the best performance compared to all other methods.

Entities: Species

Mesh：

Year: 2018 PMID： 30815154 PMCID： PMC6371303

Source DB: PubMed Journal: AMIA Annu Symp Proc ISSN： 1559-4076

11 in total

1. A simple algorithm for identifying negated findings and diseases in discharge summaries.

Authors: W W Chapman; W Bridewell; P Hanbury; G F Cooper; B G Buchanan
Journal: J Biomed Inform Date: 2001-10 Impact factor: 6.317

2. Constipation--a sign of a disease to be treated surgically, or a symptom to be deciphered as nonverbal communication?

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3. Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. II. Evaluation of diagnostic performance.

Authors: B Middleton; M A Shwe; D E Heckerman; M Henrion; E J Horvitz; H P Lehmann; G F Cooper
Journal: Methods Inf Med Date: 1991-10 Impact factor: 2.176

Mining Disease-Symptom Relation from Massive Biomedical Literature and Its Application in Severe Disease Diagnosis.

1. A simple algorithm for identifying negated findings and diseases in discharge summaries.

2. Constipation--a sign of a disease to be treated surgically, or a symptom to be deciphered as nonverbal communication?

3. Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. II. Evaluation of diagnostic performance.

4. Automated knowledge acquisition from clinical narrative reports.

5. Use of the Quick Medical Reference (QMR) program as a tool for medical education.

6. DXplain. An evolving diagnostic decision-support system.

7. Sick patients have more data: the non-random completeness of electronic health records.

8. Evaluation of symptom checkers for self diagnosis and triage: audit study.

9. Building the graph of medicine from millions of clinical narratives.

10. Learning a Health Knowledge Graph from Electronic Medical Records.

1. Knowledge-Based Biomedical Data Science.

2. A corpus-driven standardization framework for encoding clinical problems with HL7 FHIR.

3. DISNET: a framework for extracting phenotypic disease information from public sources.