Open AccessATLAS: an automated association test using probabilistically linked health records with application to genetic studies
Author(s) -
Harrison G. Zhang,
Boris P. Hejblum,
Griffin M Weber,
Nathan Palmer,
Susanne Churchill,
Peter Szolovits,
Shawn N. Murphy,
Katherine P. Liao,
Isaac S. Kohane,
Tianxi Cai
Publication year - 2021
Publication title -
journal of the american medical informatics association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.614
H-Index - 150
eISSN - 1527-974X
pISSN - 1067-5027
DOI - 10.1093/jamia/ocab187
Subject(s) - data mining , computer science , association test , inference , probabilistic logic , statistical power , resampling , linkage (software) , atlas (anatomy) , record linkage , statistical hypothesis testing , imputation (statistics) , statistics , missing data , artificial intelligence , machine learning , mathematics , medicine , population , biochemistry , chemistry , environmental health , anatomy , genotype , single nucleotide polymorphism , gene
Large amounts of health data are becoming available for biomedical research. Synthesizing information across databases may capture more comprehensive pictures of patient health and enable novel research studies. When no gold standard mappings between patient records are available, researchers may probabilistically link records from separate databases and analyze the linked data. However, previous linked data inference methods are constrained to certain linkage settings and exhibit low power. Here, we present ATLAS, an automated, flexible, and robust association testing algorithm for probabilistically linked data.