ArticleKDD : proceedings. International Conference on Knowledge Discovery & Data Mining2016
Computational Drug Repositioning Using Continuous Self-Controlled Case Series.
Article in KDD : proceedings. International Conference on Knowledge Discovery & Data Mining, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 24 citations in OpenAlex.
- Assessing the impact of social determinants of health on diabetes severity and management.JAMIA open · 2024Article
- Clinical connectivity map for drug repurposing: using laboratory results to bridge drugs and diseases.BMC medical informatics and decision making · 2021Article
- The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities.Statistics in medicine · 2020Article
- A Machine-Learning-Based Drug Repurposing Approach Using Baseline Regularization.Methods in molecular biology (Clifton, N.J.) · 2019Article
- Temporal Poisson Square Root Graphical Models.Proceedings of machine learning research · 2018Article
- Leveraging Population-Based Clinical Quantitative Phenotyping for Drug Repositioning.CPT: pharmacometrics & systems pharmacology · 2018Observational
- A Simple Text Mining Approach for Ranking Pairwise Associations in Biomedical Applications.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2017Article
- Baseline Regularization for Computational Drug Repositioning with Longitudinal Observational Data.IJCAI : proceedings of the conference · 2016Article
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 at 3 institutions in 1 country.
Funding
Abstract
Computational Drug Repositioning (CDR) is the task of discovering potential new indications for existing drugs by mining large-scale heterogeneous drug-related data sources. Leveraging the patient-level temporal ordering information between numeric physiological measurements and various drug prescriptions provided in Electronic Health Records (EHRs), we propose a Continuous Self-controlled Case Series (CSCCS) model for CDR. As an initial evaluation, we look for drugs that can control Fasting Blood Glucose (FBG) level in our experiments. Applying CSCCS to the Marshfield Clinic EHR, well-known drugs that are indicated for controlling blood glucose level are rediscovered. Furthermore, some drugs with recent literature support for the potential effect of blood glucose level control are also identified.
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.