Understanding the effect of specialization on hospital performance through knowledge-guided machine learning

Lee, Jangwon; He, Q. Peter

doi:10.1016/j.compchemeng.2019.03.040

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…LR analyzes the relationship between multiple independent variables and a categorical dependent variable, and it can estimate the probability of occurrence of an event by fitting data to a logistic curve (Bian et al, 2019;Lee and He, 2019). The logistic function is helpful because it can take input with any values from negative to positive infinity, whereas the output always takes values between zero and one; hence, the model output is interpretable as a probability.…”

Section: Logistic Regressionmentioning

confidence: 99%

Prediction and design of cyclodextrin inclusion complexes formation via machine learning-based strategies

Niu

Yang

et al. 2022

Chemical Engineering Science

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Section: Logistic Regressionmentioning

confidence: 99%

Prediction and design of cyclodextrin inclusion complexes formation via machine learning-based strategies

Niu

Yang

et al. 2022

Chemical Engineering Science

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“…Domain knowledge can also help guide supervised machine learning. In a recent work [189], we examined whether the so-called focused factory theory (i.e., factories that concentrate on narrow range of services or operations produce better products at low costs) is applicable to hospital operations. Specifically, we examine whether the hospitals that are specialized in certain diseases achieve better results in terms of costs and patient outcomes using a large national healthcare cost and utilization project (HCUP) dataset.…”

Section: Knowledge-guided Supervised Learningmentioning

confidence: 99%

Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review

Wang

2020

Processes

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In the past few decades, we have witnessed tremendous advancements in biology, life sciences and healthcare. These advancements are due in no small part to the big data made available by various high-throughput technologies, the ever-advancing computing power, and the algorithmic advancements in machine learning. Specifically, big data analytics such as statistical and machine learning has become an essential tool in these rapidly developing fields. As a result, the subject has drawn increased attention and many review papers have been published in just the past few years on the subject. Different from all existing reviews, this work focuses on the application of systems, engineering principles and techniques in addressing some of the common challenges in big data analytics for biological, biomedical and healthcare applications. Specifically, this review focuses on the following three key areas in biological big data analytics where systems engineering principles and techniques have been playing important roles: the principle of parsimony in addressing overfitting, the dynamic analysis of biological data, and the role of domain knowledge in biological data analytics.

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“…Some of the existing papers, regarding government spending, are focused on the hospital performance metrics (Kumar et al, 2019;Chang et al, 2015;Lee et al, 2019) or regional statistics (Belton and Stewart, 2002;Edmonds et al, 2019;Kruger et al, 2019) which partially influenced our study. Most of the authors were focused on predicting mortality rates compared with government spending, improving healthcare benefits and patient treatment, inequality of services or other related concerns, using advanced statistic technics and neural networks.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The paper use new data sources and transforms the traditional approach focused on the hospital performance metrics (Kumar et al, 2019;Chang et al, 2015;Lee et al, 2019;Mikhaylov and Sokolinskaya, 2019;Mikhaylov, 2020) or regional statistics (Belton and Stewart, 2002;Edmonds et al, 2019;Kruger et al, 2019).…”

Section: Entrepreneurship and Sustainability Issuesmentioning

confidence: 99%

Medication market performance analysis with help of Analytic Hierarchy Processing

Trubnikov¹,

Meynkhard²,

Shvandar³

et al. 2020

JESI

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This study proposes the concept of Analytic Hierarchy Processing (AHP) on the market of active substances used in treatment of HIV and checks the control factors and criteria interconnection and implements Random Forest forecasting model. The new method must help to improve the management decision-making process in the fields of healthcare government budget planning. It has become a prime concern for understanding and comparing of publicly available information with internal market data and the consequences of companies` and government`s actions in choosing the best approach for correct construction of to reduce HIV incidence in Russia. The paper develops the forecasting model of one of the parameters, which has a substantial role in decision-making process. The medication market data in this study represents the cumulative daily concluded contracts, used in treatment of HIV in Russia, the level of HIV incidence (yearly) and federal budget on healthcare (yearly). The proposed approach have more than 82% average accuracy at predicting the sum of medication contract prices at the 3-year time period. The received figures are effective in predicting the factors` behavior in future. It can be used for improved modulation of AHP and consequently, the overall accuracy of the model structure.

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Understanding the effect of specialization on hospital performance through knowledge-guided machine learning

Cited by 4 publications

References 14 publications

Prediction and design of cyclodextrin inclusion complexes formation via machine learning-based strategies

Prediction and design of cyclodextrin inclusion complexes formation via machine learning-based strategies

Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review

Medication market performance analysis with help of Analytic Hierarchy Processing

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