ArticleJournal of machine learning research : JMLR2016
Structure-Leveraged Methods in Breast Cancer Risk Prediction.
Article in Journal of machine learning research : JMLR, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Prediction of Breast Cancer using Machine Learning Approaches.Journal of biomedical physics & engineering · 2022Article
- Research and Application of Artificial Intelligence Based on Electronic Health Records of Patients With Cancer: Systematic Review.JMIR medical informatics · 2022Review
- Lateral elbow tendinopathy and artificial intelligence: Binary and multilabel findings detection using machine learning algorithms.Frontiers in medicine · 2022Article
- Architectural Distortion-Based Digital Mammograms Classification Using Depth Wise Convolutional Neural Network.Biology · 2021Article
- Quantifying predictive capability of electronic health records for the most harmful breast cancer.Proceedings of SPIE--the International Society for Optical Engineering · 2018Article
- Improving breast cancer risk prediction by using demographic risk factors, abnormality features on mammograms and genetic variants.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Utility of Genetic Testing in Addition to Mammography for Determining Risk of Breast Cancer Depends on Patient Age.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 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
8 authors.
Funding
Abstract
Predicting breast cancer risk has long been a goal of medical research in the pursuit of precision medicine. The goal of this study is to develop novel penalized methods to improve breast cancer risk prediction by leveraging structure information in electronic health records. We conducted a retrospective case-control study, garnering 49 mammography descriptors and 77 high-frequency/low-penetrance single-nucleotide polymorphisms (SNPs) from an existing personalized medicine data repository. Structured mammography reports and breast imaging features have long been part of a standard electronic health record (EHR), and genetic markers likely will be in the near future. Lasso and its variants are widely used approaches to integrated learning and feature selection, and our methodological contribution is to incorporate the dependence structure among the features into these approaches. More specifically, we propose a new methodology by combining group penalty and [Formula: see text] (1 ≤
Indexed as
Identifiers
28559747PMC5446896What 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.