Evidence map›Paper›PMID 37790880›Full record

ArticleProceedings. IEEE International Symposium on Biomedical Imaging2023

Deep Clustering Survival Machines with Interpretable Expert Distributions.

Bojian Hou, Hongming Li, Zhicheng Jiao, Zhen Zhou, Hao Zheng, Yong Fan

Abstract read
In one paragraph

Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Discovering Heterogeneous Neurodegenerative Disease Patterns From MRI Data for Improved Prediction.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2027
    Article
  2. Expert-Driven Survival Machines: Improving Stratification and Interpretability in Multiple Clinical Cohorts.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2026
    Article
  3. Article
  4. IRIS: Interpretable Risk Clustering Intelligence for Survival Analysis.Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data · 2025
    Article
  5. Multi-Modal Deep Clustering Survival Machines for Alzheimer's Disease Subtype Discovery.... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision · 2025
    Article
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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.

Bojian HouDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, USA.
Hongming LiDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, USA.
Zhicheng JiaoDepartment of Diagnostic Imaging, Warren Albert Medical School, Brown University, USA.
Zhen ZhouDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, USA.
Hao ZhengDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, USA.
Yong FanDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, USA.

Funding

Personalized Functional Network Modeling to Characterize and Predict Psychopathology in YouthR01EB022573 · NIBIB · UNIVERSITY OF PENNSYLVANIA · PI Yong Fan, Theodore Satterthwaite · 2016 to 2026
$6.0M
Fast and robust deep learning tools for analysis of neuroimaging data of Alzheimer's diseaseR01AG066650 · NIA · UNIVERSITY OF PENNSYLVANIA · PI FAN, YONG · 2021 to 2025
$3.4M
Biomedical Image Computing and Informatics ClusterS10OD023495 · OD · UNIVERSITY OF PENNSYLVANIA · PI DAVATZIKOS, CHRISTOS · 2017 to 2017
$1.9M
NIA NIH HHS R01 AG066650NIBIB NIH HHS R01 EB022573NIH HHS S10 OD023495
6 · The paper itself

Abstract

We develop deep clustering survival machines to simultaneously predict survival information and characterize data heterogeneity that is not typically modeled by conventional survival analysis methods. By modeling timing information of survival data

Indexed as

clusteringSurvival analysistime-to-event prediction

Identifiers

PMID37790880
PMCPMC10544788

What OpenQuestion holds

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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.