Evidence map›Paper›PMID 23304412›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2012

Logical Differential Prediction Bayes Net, improving breast cancer diagnosis for older women.

Houssam Nassif, Yirong Wu, David Page, Elizabeth Burnside

Open access · greenAbstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
0.3field-weighted citation impact, top 44% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Integrating Expert Knowledge with Data in Bayesian Networks:Expert systems with applications · 2016
    Article
  5. Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling.Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD (Conference)
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Houssam NassifUniversity of Wisconsin, Madison, USA.
Yirong Wu
David Page
Elizabeth Burnside
University of Wisconsin–Madison · USMassachusetts Institute of Technology · US

Funding

UW COMPREHENSIVE CANCER CENTER SUPPORTP30CA014520 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI AMY TRENTHAM-DIETZ · 1985 to 2026
$142.6M
NCI NIH HHS P30 CA014520
6 · The paper itself

Abstract

Overdiagnosis is a phenomenon in which screening identities cancer which may not go on to cause symptoms or death. Women over 65 who develop breast cancer bear the heaviest burden of overdiagnosis. This work introduces novel machine learning algorithms to improve diagnostic accuracy of breast cancer in aging populations. At the same time, we aim at minimizing unnecessary invasive procedures (thus decreasing false positives) and concomitantly addressing overdiagnosis. We develop a novel algorithm. Logical Differential Prediction Bayes Net (LDP-BN), that calculates the risk of breast disease based on mammography findings. LDP-BN uses Inductive Logic Programming (ILP) to learn relational rules, selects older-specific differentially predictive rules, and incorporates them into a Bayes Net, significantly improving its performance. In addition, LDP-BN offers valuable insight into the classification process, revealing novel older-specific rules that link mass presence to invasive, and calcification presence and lack of detectable mass to DCIS.

Indexed as

AlgorithmsArtificial IntelligenceAgedBayes TheoremBreast NeoplasmsCarcinoma, Intraductal, NoninfiltratingDiagnostic ErrorsFemaleHumansLogicMammography

Identifiers

PMID23304412
PMCPMC3540455
OpenAlexW10321813

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.