Evidence map›Paper›PMID 37711878›Full record

ArticleProceedings of machine learning research2023

Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat.

Shantanu Ghosh, Ke Yu, Forough Arabshahi, Kayhan Batmanghelich

Abstract read
In one paragraph

Article in Proceedings of machine learning research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. IProceedings of machine learning research · 2025
    Article
  2. Article
  3. Distilling BlackBox to Interpretable Models for Efficient Transfer Learning.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023
    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

4 authors.

Shantanu GhoshDepartment of Electrical and Computer Engineering, Boston University, MA, USA.
Ke YuIntelligent Systems Program, University of Pittsburgh, PA, USA.
Forough ArabshahiMetaAI, MenloPark, CA, USA.
Kayhan BatmanghelichDepartment of Electrical and Computer Engineering, Boston University, MA, USA.

Funding

Integrating Multi-Modal Data and Biomechanics in COPD: Toward Robust and Interpretable Biomarkers for Disease Subtyping and ProgressionR01HL141813 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Kayhan Batmanghelich · 2018 to 2026
$4.2M
NHLBI NIH HHS R01 HL141813
6 · The paper itself

Abstract

ML model design either starts with an interpretable model or a Blackbox and explains it post hoc. Blackbox models are flexible but difficult to explain, while interpretable models are inherently explainable. Yet, interpretable models require extensive ML knowledge and tend to be less flexible and underperforming than their Blackbox variants. This paper aims to blur the distinction between a post hoc explanation of a Blackbox and constructing interpretable models. Beginning with a Blackbox, we iteratively

Identifiers

PMID37711878
PMCPMC10500943

What OpenQuestion holds

Textmetadata
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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.