Identifying Family Structures from Obituaries and Matching them to Patients in an Electronic Heath Record

Family data is a valuable data source in bioinformatic research. This is because family members often share common genetic and environmental exposures. Collecting this family data is traditionally very labor intensive but advances in electronic health record (EHR) data mining has proven useful when identifying pedigrees linked to longitudinal health histories. These are called e-pedigrees. Unfortunately, e-pedigrees tend to miss the oldest generations who inherently have the longest and richest health histories. A good source of family data from older generations includes obituaries, as they have a formulaic nature making them a good candidate for natural language processing that can extract relationships to the decedent. While there have been several studies on obtaining such data from obituaries, we demonstrate for the first-time approaches that tie that information to an EHR.

NLP extraction resulted in 8,166,534 family members being abstracted from 567,279 obituaries published in the state of Wisconsin. After matching decedent and family members to patients in the EHR, we identified 109,365 unique patients that were put in 34,158 pedigrees. The largest pedigree consisted of 21 individuals. Heritability of adult height was quantified (H ² = 0.51 +- .04, P=< 1.00e-07) demonstrating this data's use in genetic research. The heritability data, coupled with overlapping data in a biobank, suggested 80% - 90% of familial relationships were accurately defined. The totality of these findings demonstrate obituaries with the oldest generations can be highly informative for bioinformatic research.

Code is available on GitHub at https://github.com/jgmayer672/ObituaryNLP .