Abstract

BackgroundGenealogical 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.ObjectiveAiming 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.MethodsBuilt 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.ResultsOur 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.ConclusionsIn 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.

Highlights

  • Anthropologists often use oral interviews, historical records, genetic analysis, and other means to obtain genealogical information and draw family trees

  • We developed and evaluated a new method of name entity recognition (NER) for extracting family members’ names and relation classifications (RCs) for classing the pairs of names among family members mentioned in online obituaries [9]

  • We presented a new corpus with a predefined family relationship map and augmented training data and proposed a multitask deep neural system to construct and assemble genealogical knowledge graphs (GKGs)

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Summary

Introduction

Anthropologists often use oral interviews, historical records, genetic analysis, and other means to obtain genealogical information and draw family trees. Constructing family trees for patients becomes an urgent need to unlock the full potential of EHR data in understanding disease and trait heritability, evaluating individuals’ health risks, and exploring environmental effects on human health Genealogical information, such as that found in family trees, is imperative for biomedical research such as disease heritability and risk prediction. 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. 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

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