ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2018
Incorporating Knowledge-Driven Insights into a Collaborative Filtering Model to Facilitate the Differential Diagnosis of Rare Diseases.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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Who cites it
4 citing papers in PubMed.
- The Right to Ask, the Need to Answer-When Patients Meet Research: How to Cope with Time.International journal of environmental research and public health · 2023Article
- Diagnostic Process in Rare Diseases: Determinants Associated with Diagnostic Delay.International journal of environmental research and public health · 2022Article
- Recommendations for patient similarity classes: results of the AMIA 2019 workshop on defining patient similarity.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Improving rare disease classification using imperfect knowledge graph.BMC medical informatics and decision making · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Rare diseases, although individually rare, collectively affect one in ten Americans. Because of their rarity, patients with rare diseases are typically left misdiagnosed or undiagnosed, which leads to a prolonged medical journey. The diagnosis pathway of a rare disease is highly dependent on the associated clinical phenotypes, i.e., the observable characteristics, at the physical, morphologic, or biochemical level, of an individual. In our previous study, we applied a collaborative filtering model on clinical data generated at Mayo Clinic to stratify patients into subgroups of rare diseases. Information mined from clinical data, however, usually contains a certain level of noise, such as occurrences of comorbidities, which could impact the accuracy of differential diagnosis. In this study, we sought to incorporate a knowledge-driven approach into collaborative filtering to optimize results learned from clinical data. Our results demonstrated an improvement in performance over pure data-driven approaches with the potential to facilitate the differential diagnosis of rare diseases.
Indexed as
Identifiers
30815196PMC6371266What OpenQuestion holds
Registered trials
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