Evidence map›Paper›PMID 31871391›Full record

ArticleAdvances in neural information processing systems2019

Vincent S Chen, Sen Wu, Zhenzhen Weng, Alexander Ratner, Christopher Ré

Abstract read
In one paragraph

Article in Advances in neural information processing systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging.Proceedings of the ACM Conference on Health, Inference, and Learning · 2020
    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

5 authors.

Vincent S ChenStanford University.
Sen WuStanford University.
Zhenzhen WengStanford University.
Alexander RatnerStanford University.
Christopher RéStanford University.

Funding

Training CoreU54EB020405 · NIBIB · STANFORD UNIVERSITY · PI DELP, SCOTT L · 2014 to 2018
$11.6M
NIBIB NIH HHS U54 EB020405
6 · The paper itself

Abstract

In real-world machine learning applications, data subsets correspond to especially critical outcomes: vulnerable cyclist detections are safety-critical in an autonomous driving task, and "question" sentences might be important to a dialogue agent's language understanding for product purposes. While machine learning models can achieve high quality performance on coarse-grained metrics like F1-score and overall accuracy, they may underperform on critical subsets-we define these as

Identifiers

PMID31871391
PMCPMC6927210

What OpenQuestion holds

Textmetadata
LicenceTDM
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.