ArticleScientific data2019
Probabilistic record linkage of de-identified research datasets with discrepancies using diagnosis codes.
Article in Scientific data, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- ATLAS: an automated association test using probabilistically linked health records with application to genetic studies.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- Quantifying the effects of pseudonymisation on epidemiological research reliability: a tailored evaluation using a clinical data warehouse.BMC medical informatics and decision making · 2026Article
- Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins.Journal of medical Internet research · 2024Article
- [Re-identification potential of structured health data].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2024Review
- Strategies to Address Current Challenges in Real-World Evidence Generation in Japan.Drugs - real world outcomes · 2023Review
- Artificial intelligence in clinical and translational science: Successes, challenges and opportunities.Clinical and translational science · 2022Review
- A Novel Approach to Generate a Virtual Population of Human Coronary Arteries forIEEE open journal of engineering in medicine and biology · 2021Article
- Record linkage of population-based cohort data from minors with national register data: a scoping review and comparative legal analysis of four European countries.Open research Europe · 2021Article
- Fundamental privacy rights in a pandemic state.PloS one · 2021Article
- Linkage of Hospital Records and Death Certificates by a Search Engine and Machine Learning.JAMIA open · 2021Article
- Review of Clinical Research Informatics.Yearbook of medical informatics · 2020Review
- Administrative records-based criterion measures.Military psychology : the official journal of the Division of Military Psychology, American Psychological AssociationArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
We develop an algorithm for probabilistic linkage of de-identified research datasets at the patient level, when only diagnosis codes with discrepancies and no personal health identifiers such as name or date of birth are available. It relies on Bayesian modelling of binarized diagnosis codes, and provides a posterior probability of matching for each patient pair, while considering all the data at once. Both in our simulation study (using an administrative claims dataset for data generation) and in two real use-cases linking patient electronic health records from a large tertiary care network, our method exhibits good performance and compares favourably to the standard baseline Fellegi-Sunter algorithm. We propose a scalable, fast and efficient open-source implementation in the ludic R package available on CRAN, which also includes the anonymized diagnosis code data from our real use-case. This work suggests it is possible to link de-identified research databases stripped of any personal health identifiers using only diagnosis codes, provided sufficient information is shared between the data sources.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.