Diagnosis related group grouping study of senile cataract patients based on E-CHAID algorithm

Luo, Aijing; Chang, Wei-Fu; Xin, Zirui; Hao, Ling; Li, Junjie; Dai, Pingping; Deng, Xuantong; Zhang, Lei; Shaogang, LI

doi:10.18240/ijo.2018.02.21

Cited by 14 publications

(12 citation statements)

References 9 publications

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“…Studies have often focused on the effects of PCA on total astigmatism32–34 and Toric IOL previously 5,35. However, little is known about the influencing factors of PCA, especially the relationship between PCA and HOAs and Q value of cornea.…”

Section: Discussionmentioning

confidence: 99%

<p>Distribution and related factors of corneal regularity and posterior corneal astigmatism in cataract patients</p>

Zhang

Yin

et al. 2019

OPTH

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Purpose To study the distribution of posterior corneal astigmatism (PCA) and its influencing factors in Chinese patients before cataract surgery. Patients and methods A retrospective study was conducted in the First Affiliated Hospital of Soochow University, Suzhou, China. In all, this study enrolled 121 eligible eyes of 121 cataract patients. The astigmatism, aberrations and Q value of anterior, posterior and total cornea and anterior segment parameters (ACD, CCT, WTW, ATA) were measured by the Sirius System, and AL was measured by Lenstar LS 900. Cataract was diagnosed using slit-lamp examination. Results The mean age of patients was 67.44±10.66 years old. Mean PCA was 0.31±0.17 (range 0.05–1.09) D and 85.9% eyes had PCA values <0.5 D. With-the-rule (WTR) astigmatism predominated the anterior cornea astigmatism (ACA) (48.8%) and total corneal astigmatism (TCA) (61.2%), while against-the-rule (ATR) astigmatism predominated posterior (86.0%). Significant positive correlation was found between the astigmatic power vector (APV) of ACA and PCA (Pearson correlation=0.318, P <0.001); TCA and PCA (Pearson correlation=0.204, P =0.025); keratometric astigmatism and PCA (Pearson correlation=0.356, P <0.001); this study also found a positive correlation between primary spherical aberration (Z4°) of the total cornea and PCA (Pearson correlation=0.266, P =0.003); primary spherical aberration (Z4°) of the corneal front surface and PCA (Pearson correlation=0.260, P =0.004); total corneal aberrations (Total cornea root mean square [RMS]) and PCA (Pearson correlation=0.327, P <0.001); total corneal higher-order aberrations (Total HOA RMS) (Pearson correlation=0.232, P =0.011); total corneal lower-order aberrations (Total LOA RMS) (Pearson correlation=0.250, P =0.006). A positive linear correlation between Q value of corneal front surface and PCA, either 6 mm pupil diameter (Pearson correlation=0.264, P =0.003) or 8 mm pupil diameter (Pearson correlation=0.184, P =0.043) was found in this study. Conclusion Corneal aberration, Q value (front surface specifically) was essential that we need to take into consideration when we conduct PCA and intraocular lens measurement in clinics.

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Section: Discussionmentioning

confidence: 99%

<p>Distribution and related factors of corneal regularity and posterior corneal astigmatism in cataract patients</p>

Zhang

Yin

et al. 2019

OPTH

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“…Under the guidance of relevant reports (25)(26)(27)(28)(29), we included the following variables from the medical records: input variables included sex, age, marital status, medical payment method, length of stay, the number of other diagnoses, major procedures, and the number of procedures, and the target variable was hospitalization expenses (Table 1). The main procedure classification was adopted from the third volume of the American International Classification of Diseases Clinical Revision ICD-9-CM-3 (2011).…”

Section: Source Of Datamentioning

confidence: 99%

Retrospective Study on the Influencing Factors and Prediction of Hospitalization Expenses for Chronic Renal Failure in China Based on Random Forest and LASSO Regression

Dai

Chang

Xin

et al. 2021

Front. Public Health

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Aim: With the improvement in people's living standards, the incidence of chronic renal failure (CRF) is increasing annually. The increase in the number of patients with CRF has significantly increased pressure on China's medical budget. Predicting hospitalization expenses for CRF can provide guidance for effective allocation and control of medical costs. The purpose of this study was to use the random forest (RF) method and least absolute shrinkage and selection operator (LASSO) regression to predict personal hospitalization expenses of hospitalized patients with CRF and to evaluate related influencing factors.Methods: The data set was collected from the first page of data of the medical records of three tertiary first-class hospitals for the whole year of 2016. Factors influencing hospitalization expenses for CRF were analyzed. Random forest and least absolute shrinkage and selection operator regression models were used to establish a prediction model for the hospitalization expenses of patients with CRF, and comparisons and evaluations were carried out.Results: For CRF inpatients, statistically significant differences in hospitalization expenses were found for major procedures, medical payment method, hospitalization frequency, length of stay, number of other diagnoses, and number of procedures. The R2 of LASSO regression model and RF regression model are 0.6992 and 0.7946, respectively. The mean absolute error (MAE) and root mean square error (RMSE) of the LASSO regression model were 0.0268 and 0.043, respectively, and the MAE and RMSE of the RF prediction model were 0.0171 and 0.0355, respectively. In the RF model, and the weight of length of stay was the highest (0.730).Conclusions: The hospitalization expenses of patients with CRF are most affected by length of stay. The RF prediction model is superior to the LASSO regression model and can be used to predict the hospitalization expenses of patients with CRF. Health administration departments may consider formulating accurate individualized hospitalization expense reimbursement mechanisms accordingly.

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“…At present, research on DRG mainly focuses on building statistical models to improve DRG rules [1][2] , using DRG data for medical care quality evaluation [3][4][5] , performance appraisal [6] , disease cost measurements [7] , medical behavior research [8][9] , and hospital healthcare [10] . However, there are few studies on compiling the uni ed DRG scheme into executable programs for establishing DRG simulation grouping.…”

Section: Introductionmentioning

confidence: 99%

“…It is an important tool for measuring medical quality, evaluating cost-effectiveness, and con rming medical insurance payments. To deepen the reform of medical insurance payments, the Chinese National Healthcare Security Administration promoted DRG Payment national pilot work in 30 cities in 2019 and enabled actual DRG payments in 2021.At present, research on DRG mainly focuses on building statistical models to improve DRG rules [1][2] , using DRG data for medical care quality evaluation [3][4][5] , performance appraisal [6] , disease cost measurements [7] , medical behavior research [8][9] , and hospital healthcare [10] . However, there are few studies on compiling the uni ed DRG scheme into executable programs for establishing DRG simulation grouping.…”

mentioning

confidence: 99%

A China Healthcare Security Diagnosis Related Groups Script Development Based On R Language

Zhu

Fan

et al. 2022

Preprint

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Background: To develop a set of R scripts that could efficiently and accurately identify the home page information of medical records and perform China Healthcare Security Diagnosis Related Groups (CHS-DRG) simulating grouping.Methods: Based on the CHS-DRG grouping rules, we abstracted the DRG grouping process into a standard algorithm and compiled the R script Z-DRG. The DRG simulating groupings by Z-DRG were compared with the DRG results from the regional CHS-DRG integrated service platform to evaluate the accuracy.Results: The Z-DRG includes one function module (zdrgfun. Rc), one operation module (zdrgpro. R) and one database form (zdrgcodes.RData). The function module set 7 algorithm steps and 8 custom functions. The functions were set for multiple diagnoses, multiple operations, joint diagnosis and operation. Only (17.85±0.11) milliseconds were taken for CHS-DRG simulating grouping of one case. Compared with the regional CHS-DRG results, the accuracy rate was 99.10%. The difference in the number of other diagnoses is the main reason that affected the accuracy.Conclusions: Z-DRG is easy to operate. The CHS-DRG simulating groupings were efficient and accurate. The simulation results could be effectively applied for medical institutions to carry out CHS-DRG grouping prediction and improve the implementation effect of CHS-DRG payment work.

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Diagnosis related group grouping study of senile cataract patients based on E-CHAID algorithm

Abstract: The grouping of senile cataract cases based on E-CHAID algorithm is reasonable. And the criterion of HECP based on DRG can provide a feasible way of management in the fair use and supervision of medical insurance fund.

Cited by 14 publications

References 9 publications

<p>Distribution and related factors of corneal regularity and posterior corneal astigmatism in cataract patients</p>

<p>Distribution and related factors of corneal regularity and posterior corneal astigmatism in cataract patients</p>

Retrospective Study on the Influencing Factors and Prediction of Hospitalization Expenses for Chronic Renal Failure in China Based on Random Forest and LASSO Regression

A China Healthcare Security Diagnosis Related Groups Script Development Based On R Language

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