ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2017
Leveraging Collaborative Filtering to Accelerate Rare Disease Diagnosis.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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
19 citing papers in PubMed.
- Development, Implementation, and Preliminary Results of a Novel Social Network for Rare Disease Communities: Mixed Methods Study.JMIR human factors · 2026Article
- Information content as a health system screening tool for rare diseases.NPJ digital medicine · 2025Article
- Identification of Smith-Magenis syndrome cases through an experimental evaluation of machine learning methods.Frontiers in computational neuroscience · 2024Article
- [Faster diagnosis of rare diseases with artificial intelligence-A precept of ethics, economy and quality of life].Innere Medizin (Heidelberg, Germany) · 2023Review
- Review
- The Impact of Artificial Intelligence in the Odyssey of Rare Diseases.Biomedicines · 2023Review
- Recommended practices and ethical considerations for natural language processing-assisted observational research: A scoping review.Clinical and translational science · 2023Article
- Use, Impact, Weaknesses, and Advanced Features of Search Functions for Clinical Use in Electronic Health Records: A Scoping Review.Applied clinical informatics · 2021Article
- Why is misdiagnosis more likely among some people with rare diseases than others? Insights from a population-based cross-sectional study in China.Orphanet journal of rare diseases · 2020Article
- Leveraging the UMLS As a Data Standard for Rare Disease Data Normalization and Harmonization.Methods of information in medicine · 2020Article
- Diagnosis support systems for rare diseases: a scoping review.Orphanet journal of rare diseases · 2020Article
- Improving rare disease classification using imperfect knowledge graph.BMC medical informatics and decision making · 2019Article
- Detection of Surgical Site Infection Utilizing Automated Feature Generation in Clinical Notes.Journal of healthcare informatics research · 2019Article
- HPO2Vec+: Leveraging heterogeneous knowledge resources to enrich node embeddings for the Human Phenotype Ontology.Journal of biomedical informatics · 2019Article
- Rare disease knowledge enrichment through a data-driven approach.BMC medical informatics and decision making · 2019Article
- A clinical text classification paradigm using weak supervision and deep representation.BMC medical informatics and decision making · 2019Article
- Utilization of Electronic Medical Records and Biomedical Literature to Support the Diagnosis of Rare Diseases Using Data Fusion and Collaborative Filtering Approaches.JMIR medical informatics · 2018Article
- Article
- Incorporating Knowledge-Driven Insights into a Collaborative Filtering Model to Facilitate the Differential Diagnosis of Rare Diseases.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
In the USA, rare diseases are defined as those affecting fewer than 200,000 patients at any given time. Patients with rare diseases are frequently misdiagnosed or undiagnosed which may due to the lack of knowledge and experience of care providers. We hypothesize that patients' phenotypic information available in electronic medical records (EMR) can be leveraged to accelerate disease diagnosis based on the intuition that providers need to document associated phenotypic information to support the diagnosis decision, especially for rare diseases. In this study, we proposed a collaborative filtering system enriched with natural language processing and semantic techniques to assist rare disease diagnosis based on phenotypic characterization. Specifically, we leveraged four similarity measurements with two neighborhood algorithms on 2010-2015 Mayo Clinic unstructured large patient cohort and evaluated different approaches. Preliminary results demonstrated that the use of collaborative filtering with phenotypic information is able to stratify patients with relatively similar rare diseases.
Indexed as
Identifiers
29854225PMC5977716What 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.