Evidence map›Paper›PMID 40234958›Full record

ArticleEuropean journal of medical research2025

Prediction of postoperative intensive care unit admission with artificial intelligence models in non-small cell lung carcinoma.

Gizem Özçıbık Işık, Burcu Kılıç, Ezel Erşen, Mehmet Kamil Kaynak, Akif Turna, Onur Sefa Özçıbık, Tülay Yıldırım, Hasan Volkan Kara

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European journal of medical research, 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

8 authors.

Gizem Özçıbık IşıkDepartment of Thoracic Surgery, Istanbul University Cerrahpasa-Cerrahpasa Medical School, Yeşilköy Cerrahpaşa Tıp Fakültesi Prof. Dr. Murat Dilmener Hastanesi, Yeşilköy, Yeşilköy Caddesi, Bakırköy, Istanbul, Türkiye.
Burcu KılıçDepartment of Thoracic Surgery, Istanbul University Cerrahpasa-Cerrahpasa Medical School, Yeşilköy Cerrahpaşa Tıp Fakültesi Prof. Dr. Murat Dilmener Hastanesi, Yeşilköy, Yeşilköy Caddesi, Bakırköy, Istanbul, Türkiye.
Ezel ErşenDepartment of Thoracic Surgery, Istanbul University Cerrahpasa-Cerrahpasa Medical School, Yeşilköy Cerrahpaşa Tıp Fakültesi Prof. Dr. Murat Dilmener Hastanesi, Yeşilköy, Yeşilköy Caddesi, Bakırköy, Istanbul, Türkiye.
Mehmet Kamil KaynakDepartment of Thoracic Surgery, Istanbul University Cerrahpasa-Cerrahpasa Medical School, Yeşilköy Cerrahpaşa Tıp Fakültesi Prof. Dr. Murat Dilmener Hastanesi, Yeşilköy, Yeşilköy Caddesi, Bakırköy, Istanbul, Türkiye.
Akif TurnaDepartment of Thoracic Surgery, Istanbul University Cerrahpasa-Cerrahpasa Medical School, Yeşilköy Cerrahpaşa Tıp Fakültesi Prof. Dr. Murat Dilmener Hastanesi, Yeşilköy, Yeşilköy Caddesi, Bakırköy, Istanbul, Türkiye.
Onur Sefa ÖzçıbıkDepartment of Computer Engineering, Bogazici University Bogazici University, Istanbul, Türkiye.
Tülay YıldırımDepartment of Electronics and Communications, Yildiz Technical University, Istanbul, Türkiye.
Hasan Volkan KaraDepartment of Thoracic Surgery, Istanbul University Cerrahpasa-Cerrahpasa Medical School, Yeşilköy Cerrahpaşa Tıp Fakültesi Prof. Dr. Murat Dilmener Hastanesi, Yeşilköy, Yeşilköy Caddesi, Bakırköy, Istanbul, Türkiye. ozcibikisikgizem@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThere is no standard practice for intensive care admission after non-small cell lung cancer surgery. In this study, we aimed to determine the need for intensive care admission after non-small cell lung cancer surgery with deep learning models.

methodsThe data of 953 patients who were operated for non-small cell lung cancer between January 2001 and 2023 was analyzed. Clinical, laboratory, respiratory, tumor's radiological and surgical features were included as input data in the study. The outcome data was intensive care unit admission. Deep learning was performed with the Fully Connected Neural Network algorithm and k-fold cross validation method.

resultsThe training accuracy value was 92.0%, the training F1 1 score of the algorithm was 86.7%, the training F1 0 value was 94.2%, and the training F1 average score was 90.5%. The test sensitivity value of the algorithm was 67.7%, the test positive predictive value was 84.0%, and the test accuracy value was 85.3%. Test F1 1 score was 75.0%, test F1 0 score was 89.5%, and test F1 average score was 82.3%. The AUC in the ROC curve created for the success analysis of the algorithm's test data was 0.83.

conclusionsUsing our method deep learning models predicted the need for intensive care unit admission with high success and confidence values. The use of artificial intelligence algorithms for the necessity of intensive care hospitalization will ensure that postoperative processes are carried out safely using objective decision mechanisms.

Indexed as

Artificial IntelligenceCarcinoma, Non-Small-Cell LungIntensive Care UnitsLung NeoplasmsPatient AdmissionPostoperative CareAgedAlgorithmsDeep LearningFemaleHumansMaleMiddle AgedNeural Networks, ComputerRetrospective StudiesArtificial IntelligenceIntensive care unitNon-small cell lung cancer

Identifiers

PMID40234958
PMCPMC12001610

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.