Evidence map›Paper›PMID 39430478›Full record

ArticleHeliyon2024

Knowledge discovery of patients reviews on breast cancer drugs: Segmentation of side effects using machine learning techniques.

Mehrbakhsh Nilashi, Hossein Ahmadi, Rabab Ali Abumalloh, Mesfer Alrizq, Abdullah Alghamdi, Sultan Alyami

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2024. 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

6 authors.

Mehrbakhsh NilashiUCSI Graduate Business School, UCSI University, 56000, Cheras, Kuala Lumpur, Malaysia.
Hossein AhmadiCentre for Health Technology, Faculty of Health, University of Plymouth, Plymouth, PL4 8AA, UK.
Rabab Ali AbumallohDepartment of Computer Science and Engineering, Qatar University, Doha, 2713, Qatar.
Mesfer AlrizqInformation Systems Dept., College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia.
Abdullah AlghamdiInformation Systems Dept., College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia.
Sultan AlyamiComputer Science Dept., College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer stands as the most frequently diagnosed life-threatening cancer among women worldwide. Understanding patients' drug experiences is essential to improving treatment strategies and outcomes. In this research, we conduct knowledge discovery on breast cancer drugs using patients' reviews. A new machine learning approach is developed by employing clustering, text mining and regression techniques. We first use Latent Dirichlet Allocation (LDA) technique to discover the main aspects of patients' experiences from the patients' reviews on breast cancer drugs. We also use Expectation-Maximization (EM) algorithm to segment the data based on patients' overall satisfaction. We then use the Forward Entry Regression technique to find the relationship between aspects of patients' experiences and drug's effectiveness in each segment. The textual reviews analysis on breast cancer drugs found 8 main side effects: Musculoskeletal Effects, Menopausal Effects, Dermatological Effects, Metabolic Effects, Gastrointestinal Effects, Neurological and Cognitive Effects, Respiratory Effects and Cardiovascular. The results are provided and discussed. The findings of this study are expected to offer valuable insights and practical guidance for prospective patients, aiding them in making informed decisions regarding breast cancer drug consumption.

Indexed as

Breast cancerDrugsKnowledge discoveryMachine learningOnline reviewsPublic healthText mining

Identifiers

PMID39430478
PMCPMC11489357

What Socratic holds

Textmetadata
LicenceCC BY-NC
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.