scholarly journals Predicting Medical Fees for Hospitalized Inpatients and the Determination of Inflection Point

2021 ◽  
Author(s):  
Yang Shao ◽  
CHIEN WEI ◽  
Ju-Kuo Lin ◽  
Willy Chou ◽  
Shih-Bin Su
Keyword(s):  
Growth Model ◽  
Exponential Growth ◽  
Item Difficulty ◽  
Visual Display ◽  
Inpatient Hospitalization ◽  
Specific Patient ◽  
Inflection Points ◽  
Medical Fees ◽  
Exponential Growth Model

Abstract Background: Taiwan’s Bureau of National Health Insurance (BNHI) implemented an inpatient DRG payment system scheduled for January 2008. Many hospital managers urgently invent initiatives to decrease the impacts of DRGs. Predicting medical fees for hospitalized inpatients every day and the corresponding inflection points (IPs) are required for investigations. The aims of this study include (1) verifying the efficacy of the exponential growth model on accumulative publications of mobile health research between 1997 and 2016 in the literature; (2) building the model of predicting medical fees for hospitalized inpatients and determining the inflection points; and (3) demonstrating visualizations of the prediction model online in use for hospital physicians.Methods: An exponential growth model was applied to determine the IP and predict the medical fees to help physicians contain the medical fees of a specific patient during hospitalization. The IP is equal to the item difficulty proven using the differential equation in calculus. An online visual display of the medically contained and predicted inpatient hospitalization was demonstrated in this study.Results: We observed (1) a model accuracy (R2 = 0.99) higher than that (R2 = 0.98) in the literature based on identical data; (2) 231 samples of medical fees for inpatients in the study module with a length of days between 6 and 20 and an IPS falling in the range between 1 and 10 (Q1=0.98, Q3=1.00); and (3) online visualization demonstration of medical fees predicted for hospital inpatients and IP determination on ogive curves.Conclusion: The exponential growth model can be applied to a clinical setting to help physicians consecutively predict medical fees for hospitalized inpatients and upgrade the level of hospital management in the future.

scholarly journals Estimating COVID-19 cases infected with the variant alpha (VOC 202012/01): an analysis of screening data in Tokyo, January-March 2021

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Hiroaki Murayama ◽  
Taishi Kayano ◽  
Hiroshi Nishiura
Keyword(s):  
Confidence Interval ◽  
Growth Model ◽  
Exponential Growth ◽  
Reproduction Number ◽  
Hypergeometric Distribution ◽  
Actual Number ◽  
Growth Data ◽  
Exponential Growth Model

Abstract Background In Japan, a part of confirmed patients’ samples have been screened for the variant of concern (VOC), including the variant alpha with N501Y mutation. The present study aimed to estimate the actual number of cases with variant alpha and reconstruct the epidemiological dynamics. Methods The number of cases with variant alpha out of all PCR confirmed cases was estimated, employing a hypergeometric distribution. An exponential growth model was fitted to the growth data of variant alpha cases over fourteen weeks in Tokyo. Results The weekly incidence with variant alpha from 18–24 January 2021 was estimated at 4.2 (95% confidence interval (CI): 0.7, 44.0) cases. The expected incidence in early May ranged from 420–1120 cases per week, and the reproduction number of variant alpha was on the order of 1.5 even under the restriction of contact from January-March, 2021, Tokyo. Conclusions The variant alpha was predicted to swiftly dominate COVID-19 cases in Tokyo, and this has actually occurred by May 2021. Devising the proposed method, any country or location can interpret the virological sampling data.

scholarly journals Estimated Effect of COVID-19 Lockdown on Skin Tumor Size and Survival: An Exponential Growth Model

2020 ◽  
Vol 111 (8) ◽  
pp. 629-638
Author(s):  
A. Tejera-Vaquerizo ◽  
J. Cañueto ◽  
A. Toll ◽  
J. Santos-Juanes ◽  
A. Jaka ◽  
...  
Keyword(s):  
Growth Model ◽  
Exponential Growth ◽  
Tumor Size ◽  
Skin Tumor ◽  
Exponential Growth Model

scholarly journals Models for Predicting Global Plastic Waste

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Yuexing Hao ◽  
Glenn Shafer
Keyword(s):  
Growth Model ◽  
Real World ◽  
Exponential Growth ◽  
Plastic Waste ◽  
The Self ◽  
Scientific Models ◽  
Adaptive Model ◽  
The Real ◽  
Exponential Growth Model ◽  
Self Adaptive

For more than half a century, plastic prod-ucts have been a part of people’s lives. When plastic waste is thrown into nature, it can cause a sequence of dangerous effects. Previous researchers esti-mated that global plastic waste in 2020 will be more than 400 million tons. To reduce plastic waste, they built scientific models to analyze the sources of plas-tic and provided solutions for regenerating these plastic wastes. However, their models are static and inaccurate, which may cause some false predictions.In this paper, we first observe the distribution of the real-world plastic waste data. Then, we build simple exponential growth model and logistics model to match these data. By testing different models on our plots, we discover that the SELF-ADAPTIVE MODEL is the best to describe and correctly predict our future plastic waste production, as this model combines the benefits of SIMPLE EXPONENTIAL GROWTH MODEL and the LOGISTIC MODEL. The self-Adaptive model has the potential to minimize the error rate and make the predictions more accurate. Based on this model, we can develop more accurate and informative solu-tions for the real-world plastic problems.

scholarly journals Predicting the COVID-19 positive cases in India with concern to Lockdown by using Mathematical and Machine Learning based Models

2020 ◽  
Author(s):  
Ajit Kumar Pasayat ◽  
Satya Narayan Pati ◽  
Aashirbad Maharana
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Regression Model ◽  
Growth Model ◽  
Linear Regression Model ◽  
Exponential Growth ◽  
Time Window ◽  
Epidemic Curve ◽  
First Case ◽  
Exponential Growth Model

In this study, we analyze the number of infected positive cases of COVID-19 outbreak with concern to lockdown in India in the time window of February 11th 2020 to Jun 30th 2020. The first case in India was reported in Kerala on January 30th 2020. To break the chain of spreading, Government announced a nationwide lockdown on March 24th 2020, which is increased two times. The Ongoing lockdown 3.0 is over on May 18th, 2020. We derived how the lockdown relaxation is going to impact on containment of the outbreak. Here the Exponential Growth Model has been used to derive the epidemic curve based on the data collected from February 11th 2020, to May 11th 2020, and the Machine Learning based Linear Regression model that gives the epidemic curve to predict the cases with the continuous flow of the lockdown. We estimate that if the lockdown is continuing with more relaxation, then the estimated infected cases reach up to 1.16 crores by June 30th 2020, and the lockdown would persist with current restriction, then the expected predicted infected cases are 5.69 lacs. The Exponential Growth Model and the Linear Regression Model are advantageous to predict the number of affected cases of COVID-19. These models can be used for forecasting in long term intervals. It shows from our result that lockdown with certain restriction has a vital role in preventing the spreading of this epidemic in this current situation.

scholarly journals Production of annual ryegrass with different doses of nitrogen fertilization in topdressing

2019 ◽  
Vol 40 (3) ◽  
pp. 1329
Author(s):  
Delvacir Rezende Bolke ◽  
Ione Maria Pereira Haygert-Velho ◽  
Luiz Carlos Timm ◽  
Dileta Regina Moro Alessio ◽  
Andréa Mittelmann ◽  
...  
Keyword(s):  
Growth Model ◽  
Exponential Growth ◽  
Lolium Multiflorum ◽  
Block Design ◽  
Control Treatment ◽  
Time Interval ◽  
Annual Ryegrass ◽  
Nitrogen Application ◽  
Exponential Growth Model ◽  
Different Doses

The objective of this study was to assess the growth of annual ryegrass (Lolium multiflorum) cv. BRS Ponteio with different doses of nitrogen applied in the pasture, thereby adjusting their growth to the exponential growth model. A randomized block design was used with five nitrogen application rates (0, 150, 250, 350, and 450 kg N ha-1) and four replicates, applied in installments. Each plot measured 9 m2. On April 15, 2014, 25 kg ha-1 of viable pure seeds of annual ryegrass were sown at a depth of 0.02 m, in 18 rows spaced at 0.17 m in each plot. Growth in the control treatment (zero nitrogen) pasture lasted 167 days with only three cuts, whereas in pastures treated with 350 and 450 kg N ha-1, growth was extended for an additional 45 days with a 333% increase in the number of cuts. The pastures were used for the same duration (188 days) in the treatments with 150 and 250 kg N ha-1, however, increased nitrogen resulted in two additional cuts and a shorter time interval between cuts. The time interval between each cut and the degree-days interacted dynamically causing distinct growth. Growth of the annual ryegrass BRS Ponteio without nitrogen application is poor and cannot be represented even by a first order linear model. The application of nitrogen topdressing, in the form of urea, decreases the time interval between cuts, increases the dry matter production per hectare, stimulates this production, and follows the exponential growth model.

Exponential growth model of Nile tilapia (Oreochromis niloticus) strains considering heteroscedastic variance

Aquaculture ◽  
2008 ◽  
Vol 274 (1) ◽  
pp. 96-100 ◽  
Author(s):  
Vander Bruno dos Santos ◽  
Eidi Yoshihara ◽  
Rilke Tadeu Fonseca de Freitas ◽  
Rafael Vilhena Reis Neto
Keyword(s):  
Growth Model ◽  
Exponential Growth ◽  
Oreochromis Niloticus ◽  
Nile Tilapia ◽  
Nile Tilapia Oreochromis Niloticus ◽  
Exponential Growth Model ◽  
Heteroscedastic Variance

NUMBER OF INSTARS OF THE PITCH PINE TIP MOTH, RHYACIONIA RIGIDANA (LEPIDOPTERA: TORTRICIDAE, OLETHREUTINAE)

1982 ◽  
Vol 114 (6) ◽  
pp. 531-534 ◽  
Author(s):  
Paul M. Gargiullo ◽  
C. W. Berisford
Keyword(s):  
Growth Model ◽  
Exponential Growth ◽  
Head Capsule ◽  
Normal Distributions ◽  
Multimodal Analysis ◽  
Pitch Pine ◽  
Exponential Growth Model

AbstractHead capsule widths were measured on 962 larvae of Rhyacionia rigidana (Fernald). Five instars were detected using multimodal analysis, and normal distributions of head widths for each instar are given. Regression using an exponential growth model was used to generate mean head widths according to Dyar's rule. These widths did not differ significantly from observed widths.

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