Evidence map›Paper›PMID 41695204›Full record

Article... International Conference on Learning Representations2022

Gradient Importance Learning for Incomplete Observations.

Qitong Gao, Dong Wang, Joshua D Amason, Siyang Yuan, Chenyang Tao, Ricardo Henao, Majda Hadziahmetovic, Lawrence Carin, Miroslav Pajic

Abstract read
PubMed Full text at PMC
In one paragraph

Article in ... International Conference on Learning Representations, 2022. 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

9 authors.

Qitong GaoDuke University, USA.
Dong WangDuke University, USA.
Joshua D AmasonDuke University, USA.
Siyang YuanDuke University, USA.
Chenyang TaoDuke University, USA.
Ricardo HenaoDuke University, USA.
Majda HadziahmetovicDuke University, USA.
Lawrence CarinDuke University, USA.
Miroslav PajicDuke University, USA.

Funding

Predictive Analytics in Hemodialysis: Enabling Precision Care for Patient with ESKDR01DK123062 · NIDDK · DUKE UNIVERSITY · PI GOLDSTEIN, BENJAMIN ALAN · 2020 to 2023
$2.6M
Data HarmonizationR61NS120246 · NINDS · DUKE UNIVERSITY · PI PENCINA, MICHAEL J · 2020 to 2022
$2.0M
NIDDK NIH HHS R01 DK123062NINDS NIH HHS R61 NS120246
6 · The paper itself

Abstract

Though recent works have developed methods that can generate estimates (or imputations) of the missing entries in a dataset to facilitate downstream analysis, most depend on assumptions that may not align with real-world applications and could suffer from poor performance in subsequent tasks such as classification. This is particularly true if the data have large missingness rates or a small sample size. More importantly, the imputation error could be propagated into the prediction step that follows, which may constrain the capabilities of the prediction model. In this work, we introduce the gradient importance learning (GIL) method to train multilayer perceptrons (MLPs) and long short-term memories (LSTMs) to

Identifiers

PMID41695204
PMCPMC12905674

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.