Evidence map›Paper›PMID 40814437›Full record

ArticleIEEE transactions on knowledge and data engineering2024

Hierarchical Active Learning with Label Proportions on Data Regions.

Zhipeng Luo, Qiang Gao, Yazhou He, Hongjun Wang, Milos Hauskrecht, Tianrui Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in IEEE transactions on knowledge and data engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Zhipeng LuoSchool of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan 611756, China.
Qiang GaoSchool of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, Sichuan 611130, China.
Yazhou HeDepartment of Oncology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
Hongjun WangSchool of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan 611756, China.
Milos HauskrechtDepartment of Computer Science, The University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA.
Tianrui LiManufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province, Chengdu, Sichuan 611756, China.

Funding

Real-time detection of deviations in clinical care in ICU data streamsR01GM088224 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI CLERMONT, GILLES, COOPER, GREGORY F. · 2009 to 2017
$3.7M
NIGMS NIH HHS R01 GM088224
6 · The paper itself

Abstract

Learning classification models from real-world data often requires substantial human effort devoted to instance annotation. As the instance-based annotating process can be very time-consuming and costly, we propose a novel active learning framework that builds classification models from human-annotated

Indexed as

Active LearningLearning from Alternative Human FeedbackLearning from Label ProportionsWeakly Supervised Learning

Identifiers

PMID40814437
PMCPMC12346140

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.