Evidence map›Paper›PMID 36124203›Full record

ArticleStatistics and computing2022

Default risk prediction and feature extraction using a penalized deep neural network.

Cunjie Lin, Nan Qiao, Wenli Zhang, Yang Li, Shuangge Ma

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Statistics and computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Cunjie LinCenter for Applied Statistics, Renmin University of China, Beijing, 100872 China.
Nan QiaoSchool of Statistics, Renmin University of China, Beijing, 100872 China.
Wenli ZhangSchool of Statistics, Renmin University of China, Beijing, 100872 China.
Yang LiCenter for Applied Statistics, Renmin University of China, Beijing, 100872 China.ORCID 0000-0002-6287-5094
Shuangge MaSchool of Statistics, Renmin University of China, Beijing, 100872 China.

Funding

Novel methods for identifying genetic interactions in cancer prognosisR01CA204120 · NCI · YALE UNIVERSITY · PI Shuangge Ma · 2016 to 2026
$3.5M
NCI NIH HHS R01 CA204120
6 · The paper itself

Abstract

Online peer-to-peer lending platforms provide loans directly from lenders to borrowers without passing through traditional financial institutions. For lenders on these platforms to avoid loss, it is crucial that they accurately assess default risk so that they can make appropriate decisions. In this study, we develop a penalized deep learning model to predict default risk based on survival data. As opposed to simply predicting whether default will occur, we focus on predicting the probability of default over time. Moreover, by adding an additional one-to-one layer in the neural network, we achieve feature selection and estimation simultaneously by incorporating an

Indexed as

Feature extractionLoan dataNeural networkRisk predictionSurvival analysis

Identifiers

PMID36124203
PMCPMC9476445

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.