ArticleRisk management and healthcare policy2024
Optimization of Diagnosis-Related Groups for 14,246 Patients with Uterine Leiomyoma in a Single Center in Western China Using a Machine Learning Model.
Article in Risk management and healthcare policy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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.
Who cites it
7 citing papers in PubMed.
- Optimizing case mix for per-diem payment of mental disorders based on E-CHAID decision tree analysis.BMC health services research · 2026Article
- Hospitalization expenses for radical prostatectomy in a tertiary hospital in China: a quantile regression and CHAID decision tree analysis.BMC health services research · 2026Article
- An adaptive and interpretable case grouping framework for healthcare payment systems.International journal for equity in health · 2026Article
- An empirical study of diagnosis-related group classification for hospitalization costs in lumbar disk herniation patients treated in Chinese medicine hospitals in China.Frontiers in public health · 2026Article
- Article
- A tree-structured multiobjective optimization framework for constructing diagnosis-related groups.NPJ digital medicine · 2025Article
- Development and Validation of a Nomogram to Predict the Risk of Special Uterine Leiomyoma Pathological Types or Leiomyosarcoma in Postmenopausal Women: A Retrospective Study.Risk management and healthcare policy · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Uterine leiomyoma (UL) is one of the most common benign tumors in women, and its incidence is gradually increasing in China. The clinical complications of UL have a negative impact on women's health, and the cost of treatment poses a significant burden on patients. Diagnosis-related groups (DRG) are internationally recognized as advanced healthcare payment management methods that can effectively reduce costs. However, there are variations in the design and grouping rules of DRG policies across different regions. Therefore, this study aims to analyze the factors influencing the hospitalization costs of patients with UL and optimize the design of DRG grouping schemes to provide insights for the development of localized DRG grouping policies. Methods: The Mann-Whitney Results: Age, occupation, number of hospitalizations, type of medical insurance, Transfer to other departments, length of stay (LOS), type of UL, admission condition, comorbidities and complications, type of primary procedure, other types of surgical procedures, and discharge method had a significant impact on hospitalization costs (P<0.05). Among them, the type of primary procedure, other types of surgical procedures, and LOS were the main factors influencing hospitalization costs. By incorporating the type of primary procedure, other types of surgical procedures, and LOS into the decision tree model, patients were divided into 11 DRG combinations. Conclusion: Hospitalization costs for UL are mainly related to the type of primary procedure, other types of surgical procedures, and LOS. The DRG case combinations of UL based on E-CHAID algorithm are scientific and reasonable.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.