Evidence map›Paper›PMID 42855458›Full record

ReviewCommunications medicine2026

Introduction to using symbolic regression for interpretable machine learning in healthcare.

Michael Ferguson, Feifan Liu, Honghuang Lin, David D McManus, Ben S Gerber

Abstract readReview
PubMed Publisher
In one paragraph

Review in Communications medicine, 2026. 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.

Michael FergusonDepartment of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA. michael.ferguson@umassmed.edu.ORCID http://orcid.org/0009-0008-8262-0276
Feifan LiuDepartment of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA.
Honghuang LinDepartment of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA.ORCID http://orcid.org/0000-0003-3043-3942
David D McManusDepartment of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA.
Ben S GerberDepartment of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA.ORCID http://orcid.org/0000-0003-4367-6396

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The past decade has seen machine learning play an increasingly important role in medicine. However, many of the state-of-the-art models being developed and deployed suffer from a lack of transparency, limiting integration into the clinical decision-making process and raising concerns about patient safety. Here we call attention to symbolic regression, a largely overlooked machine learning technique that aims to be both accurate and interpretable by learning readable mathematical models from data. In recent years, it has undergone rapid advancements with successful application to problems across a wide range of disciplines. In this perspective, we provide an approachable introduction to symbolic regression, highlight its strengths and limitations, and explore its potential applications in medicine.

Identifiers

PMID42855458

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.