Evidence map›Paper›PMID 40558277›Full record

ArticleCurrent oncology (Toronto, Ont.)2025

Using Machine Learning Approaches on Dynamic Patient-Reported Outcomes to Cluster Cancer Treatment-Related Symptoms.

Nora Asper, Hans Friedrich Witschel, Louise von Stockar, Emanuele Laurenzi, Hans Christian Kolberg, Marcus Vetter, Sven Roth, Gerd Kullak-Ublick, Andreas Trojan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Current oncology (Toronto, Ont.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Nora AsperCenter for Dental Medicine and Faculty of Medicine, University of Zurich, 8032 Zurich, Switzerland.ORCID 0009-0003-7060-3333
Hans Friedrich WitschelSchool of Business, FHNW, University of Applied Sciences and Arts Northwestern Switzerland, 4600 Olten, Switzerland.ORCID 0000-0002-8608-9039
Louise von StockarMobile Health AG, 8008 Zurich, Switzerland.
Emanuele LaurenziSchool of Business, FHNW, University of Applied Sciences and Arts Northwestern Switzerland, 4600 Olten, Switzerland.
Hans Christian KolbergMarienhospital Bottrop GmbH, 46236 Bottrop, Germany.ORCID 0000-0003-0221-3272
Marcus VetterCantonal Hospital Baselland, 4410 Liestal, Switzerland.ORCID 0009-0004-1003-1078
Sven RothCenter for Dental Medicine and Faculty of Medicine, University of Zurich, 8032 Zurich, Switzerland.ORCID 0009-0001-6063-7447
Gerd Kullak-UblickDepartment of Clinical Pharmacology and Toxicology, University Hospital Zurich, University of Zurich, 8006 Zurich, Switzerland.ORCID 0000-0002-0757-4408
Andreas TrojanDepartment of Clinical Pharmacology and Toxicology, University Hospital Zurich, University of Zurich, 8006 Zurich, Switzerland.

Funding

Foundation Swiss Tumor Institute Zürich STI_REV2024
6 · The paper itself

Abstract

In patients undergoing systemic treatment for cancer, symptom tracking via electronic patient-reported outcomes (ePROs) has been used to optimize communication and monitoring, and facilitate the early detection of adverse effects and to compare the side effects of similar drugs. We aimed to examine whether the patterns in electronic patient-reported outcomes, without any additional clinician data input, are predictive of the underlying cancer type and reflect tumor- and treatment-associated symptom clusters (SCs). The data were derived from a total of 226 patients who self-reported on the presence and severity (according to the Common Terminology Criteria for Adverse Events (CTCAEs)) of more than 90 available symptoms via the medidux

Indexed as

Machine LearningNeoplasmsPatient Reported Outcome MeasuresAgedFemaleHumansMaleMiddle Agedadherencecancerdecision supporteHealthelectronic patient-reported outcome (ePRO)machine learningreal world evidencesymptom cluster

Identifiers

PMID40558277
PMCPMC12191751

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.