ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2020
Developing a Search Engine for Precision Medicine.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- CellAwareGNN: Single-Cell Enhanced Knowledge Graph Foundation Model for Drug Indication Prediction.bioRxiv : the preprint server for biology · 2026Article
- Building a knowledge graph to enable precision medicine.Scientific data · 2023Article
- State-of-the-Art Evidence Retriever for Precision Medicine: Algorithm Development and Validation.JMIR medical informatics · 2022Article
- Gut microbiome, big data and machine learning to promote precision medicine for cancer.Nature reviews. Gastroenterology & hepatology · 2020Review
- Extracting Concepts for Precision Oncology from the Biomedical Literature.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational ScienceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Precision medicine focuses on developing new treatments based on an individual's genetic, environmental, and lifestyle profile. While this data-driven approach has led to significant advances, retrieving information specific to a patient's condition has proved challenging for oncologists due to the large volume of data. In this paper, we propose the PRecIsion Medicine Robust Oncology Search Engine (PRIMROSE) for cancer patients that retrieves scientific articles and clinical trials based on a patient's condition, genetic profile, age, and gender. Our search engine utilizes Elasticsearch indexes for information storage and retrieval, and we developed a knowledge graph for query expansion in order to improve recall. Additionally, we experimented with machine learning and learning-to-rank components to the search engine and compared the results of the two approaches. Finally, we developed a front-facing ReactJS website and a REST API for connecting with our search engine. The development of this front-facing website allows for easy access to our system by healthcare providers.
Identifiers
32477680PMC7233032What 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.