Construction of Genealogical Knowledge Graphs From Obituaries: Multitask Neural Network Extraction System.
Background: Genealogical information, such as that found in family trees, is imperative for biomedical research such as disease heritability and risk prediction. Researchers have used policyholder and their dependent information in medical claims data and emergency contacts in electronic health reco...
| Publicado en: | Journal of Medical Internet Research Vol. 23; no. 8 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
JMIR Publications Inc.
Aug2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=152246694&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152246694 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14394456 DNC jtl: Journal of Medical Internet Research issn: 14394456 maglogo: N pubinfo: dt: Aug2021 vid: 23 iid: 8 pid: 21567 pub: JMIR Publications Inc. place: Toronto, Ontario artinfo: ui: 152246694 152246694 NLM34346903 152246694 10.2196/25670 NLM34346903 152246694 ppct: 1 formats: tig: atl: Construction of Genealogical Knowledge Graphs From Obituaries: Multitask Neural Network Extraction System. aug: au: He, Kai Yao, Lixia Zhang, JiaWei Li, Yufei Li, Chen affil: 1 School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, China sug: subj: Information Science Knowledge Human Information Retrieval Comparative Studies Multicenter Studies Evaluation Research Validation Studies Scales ab: Background: Genealogical information, such as that found in family trees, is imperative for biomedical research such as disease heritability and risk prediction. Researchers have used policyholder and their dependent information in medical claims data and emergency contacts in electronic health records (EHRs) to infer family relationships at a large scale. We have previously demonstrated that online obituaries can be a novel data source for building more complete and accurate family trees.Objective: Aiming at supplementing EHR data with family relationships for biomedical research, we built an end-to-end information extraction system using a multitask-based artificial neural network model to construct genealogical knowledge graphs (GKGs) from online obituaries. GKGs are enriched family trees with detailed information including age, gender, death and birth dates, and residence.Methods: Built on a predefined family relationship map consisting of 4 types of entities (eg, people's name, residence, birth date, and death date) and 71 types of relationships, we curated a corpus containing 1700 online obituaries from the metropolitan area of Minneapolis and St Paul in Minnesota. We also adopted data augmentation technology to generate additional synthetic data to alleviate the issue of data scarcity for rare family relationships. A multitask-based artificial neural network model was then built to simultaneously detect names, extract relationships between them, and assign attributes (eg, birth dates and death dates, residence, age, and gender) to each individual. In the end, we assemble related GKGs into larger ones by identifying people appearing in multiple obituaries.Results: Our system achieved satisfying precision (94.79%), recall (91.45%), and F-1 measures (93.09%) on 10-fold cross-validation. We also constructed 12,407 GKGs, with the largest one made up of 4 generations and 30 people.Conclusions: In this work, we discussed the meaning of GKGs for biomedical research, presented a new version of a corpus with a predefined family relationship map and augmented training data, and proposed a multitask deep neural system to construct and assemble GKGs. The results show our system can extract and demonstrate the potential of enriching EHR data for more genetic research. We share the source codes and system with the entire scientific community on GitHub without the corpus for privacy protection. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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