Evidence map›Paper›PMID 33294870›Full record

ReviewPatterns (New York, N.Y.)2020

Extensions of the External Validation for Checking Learned Model Interpretability and Generalizability.

Sung Yang Ho, Kimberly Phua, Limsoon Wong, Wilson Wen Bin Goh

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Patterns (New York, N.Y.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 114 papers, 12 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
114citing papers in PubMed, 12 pooled it
–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

114 citing papers in PubMed, 12 syntheses or guidelines pooled it.

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54 more citing papers are in PubMed but not listed here.

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.

Sung Yang HoSchool of Biological Sciences, Nanyang Technological University, Singapore 637551, Singapore.
Kimberly PhuaSchool of Biological Sciences, Nanyang Technological University, Singapore 637551, Singapore.
Limsoon WongDepartment of Computer Science, National University of Singapore, Singapore 117417, Singapore.
Wilson Wen Bin GohSchool of Biological Sciences, Nanyang Technological University, Singapore 637551, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We discuss the validation of machine learning models, which is standard practice in determining model efficacy and generalizability. We argue that internal validation approaches, such as cross-validation and bootstrap, cannot guarantee the quality of a machine learning model due to potentially biased training data and the complexity of the validation procedure itself. For better evaluating the generalization ability of a learned model, we suggest leveraging on external data sources from elsewhere as validation datasets, namely external validation. Due to the lack of research attractions on external validation, especially a well-structured and comprehensive study, we discuss the necessity for external validation and propose two extensions of the external validation approach that may help reveal the true domain-relevant model from a candidate set. Moreover, we also suggest a procedure to check whether a set of validation datasets is valid and introduce statistical reference points for detecting external data problems.

Indexed as

computational biologydata sciencedescriptive statisticsexploratory data analysisscientific method

Identifiers

PMID33294870
PMCPMC7691387

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.