Evidence map›Paper›PMID 41118016›Full record

ArticleJA clinical reports2025

Prediction of financial deficits of postoperative patients in the intensive care unit using machine learning.

Saori Ikumi, Takuya Shiga, Eichi Takaya, Shinya Sonobe, Yu Kaiho, Yukiko Ito, Masanori Yamauchi

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Article in JA clinical reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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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

7 authors.

Saori IkumiDepartment of Anesthesiology and Perioperative Medicine, Tohoku University Graduate School of Medicine, 1-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan.
Takuya ShigaDepartment of Anesthesiology and Perioperative Medicine, Tohoku University Graduate School of Medicine, 1-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan. takuya.shiga.b5@tohoku.ac.jp.ORCID http://orcid.org/0000-0001-7303-3653
Eichi TakayaAI Lab, Tohoku University Hospital, 1-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan.
Shinya SonobeAI Lab, Tohoku University Hospital, 1-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan.
Yu KaihoDepartment of Anesthesiology and Perioperative Medicine, Tohoku University Graduate School of Medicine, 1-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan.
Yukiko ItoGraduate School of Business and Commerce, Keio University, 2-14-45, Mita, Minato-ku, Tokyo, 108-8345, Japan.
Masanori YamauchiDepartment of Anesthesiology and Perioperative Medicine, Tohoku University Graduate School of Medicine, 1-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan.

Funding

JSPS KAKENHI JP19K18311JSPS KAKENHI JP24K23481
6 · The paper itself

Abstract

backgroundOperational loss, defined as unanticipated financial deficits in intensive care unit (ICU) management, is challenging to predict yet critical for hospital sustainability. This study aimed to evaluate whether machine-learning models can predict financial loss events in postoperative ICU patients.

methodsWe conducted a retrospective analysis of postoperative patients admitted to the ICU at Tohoku University Hospital between April 2017 and March 2021. A total of 22 clinical and administrative variables collected within 24 h of ICU admission were used to develop machine-learning models. The outcome was defined as financial loss events, determined by a negative contribution margin below the break-even threshold of - 909 USD. The dataset was randomly split into training (70%) and test (30%) sets. Predictive performance was assessed using the area under the receiver operating characteristic curve (AUC) and accuracy.

resultsAmong 6743 postoperative ICU patients, 425 (6.3%) experienced financial loss events. The random forest classifier demonstrated high predictive performance, with an AUC of 0.859 and accuracy of 0.785.

conclusionsMachine-learning models may accurately predict financial loss events in postoperative ICU patients, potentially supporting efficient resource allocation and hospital financial planning.

Indexed as

Contribution marginFinancial deficitInsurance claims dataIntensive care unitMachine learningPostoperative patients

Identifiers

PMID41118016
PMCPMC12540221

What Socratic holds

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Registered trials

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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.