Evidence map›Paper›PMID 30568493›Full record

ArticleCancer management and research2018

Development of a prediction model for pancreatic cancer in patients with type 2 diabetes using logistic regression and artificial neural network models.

Meng Hsuen Hsieh, Li-Min Sun, Cheng-Li Lin, Meng-Ju Hsieh, Chung-Y Hsu, Chia-Hung Kao

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer management and research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 1 pooled it
2.0field-weighted citation impact, top 12% of its field
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

35 citing papers in PubMed, 1 synthesis or guideline pooled it, 69 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Review
  5. Review
  6. Article
  7. Review
  8. Article
  9. Review
  10. Article
  11. Observational
  12. Review
  13. Article
  14. Review
  15. Review
  16. Article
  17. Review
  18. Article
  19. Review
  20. Review
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 at 4 institutions in 3 countries.

Meng Hsuen HsiehDepartment of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.
Li-Min SunDepartment of Radiation Oncology, Zuoying Branch of Kaohsiung Armed Forces General Hospital, Kaohsiung, Taiwan, Republic of China.
Cheng-Li LinManagement Office for Health Data, China Medical University Hospital, Taichung, Taiwan, Republic of China.
Meng-Ju HsiehDepartment of Medicine, Poznan University of Medical Sciences, Poznan, Poland.
Chung-Y HsuGraduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan, Republic of China, d10040@mail.cmuh.org.tw.
Chia-Hung KaoGraduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan, Republic of China, d10040@mail.cmuh.org.tw.
China Medical University · TWPoznan University of Medical Sciences · PLAsia University · TWKaohsiung Armed Forces General Hospital · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesPatients with type 2 diabetes (T2DM) are suggested to have a higher risk of developing pancreatic cancer. We used two models to predict pancreatic cancer risk among patients with T2DM.

methodsThe original data used for this investigation were retrieved from the National Health Insurance Research Database of Taiwan. The prediction models included the available possible risk factors for pancreatic cancer. The data were split into training and test sets: 97.5% of the data were used as the training set and 2.5% of the data were used as the test set. Logistic regression (LR) and artificial neural network (ANN) models were implemented using Python (Version 3.7.0). The

resultsThe metrics used in this study indicated that the LR model more accurately predicted pancreatic cancer than the ANN model. For the LR model, the area under the ROC curve in the prediction of pancreatic cancer was 0.727, indicating a good fit.

conclusionUsing this LR model, our results suggested that we could appropriately predict pancreatic cancer risk in patients with T2DM in Taiwan.

Indexed as

artificial neural networklogistic regressionpancreatic cancertype 2 diabetes

Identifiers

PMID30568493
PMCPMC6267763
OpenAlexW2902936037

What OpenQuestion 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 OpenQuestion graph.