Impact of HbA1c Measurement on Hospital Readmission Rates: Analysis of 70,000 Clinical Database Patient Records

Strack, Beata; DeShazo, Jonathan P.; Gennings, Chris; Olmo, Juan Luis; Ventura, Sebastián; Cios, Krzysztof J.; Clore, John N.

doi:10.1155/2014/781670

Cited by 237 publications

(146 citation statements)

References 17 publications

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“…(1) Diabetic data source [20]: the dataset represents 10 years (1999-2008) of clinical care at 130 US hospitals and integrated delivery networks. It includes over 50 features representing patient and hospital outcomes.…”

Section: A Data Sources and Configurationsmentioning

confidence: 99%

Splitting Large Medical Data Sets Based on Normal Distribution in Cloud Environment

Zhang

Zhao

Pang

et al. 2020

IEEE Trans. Cloud Comput.

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The surge of medical and e-commerce applications has generated tremendous amount of data, which brings people to a so-called "Big Data" era. Different from traditional large data sets, the term "Big Data" not only means the large size of data volume but also indicates the high velocity of data generation. However, current data mining and analytical techniques are facing the challenge of dealing with large volume data in a short period of time. This paper explores the efficiency of utilizing the Normal Distribution (ND) method for splitting and processing large volume medical data in cloud environment, which can provide representative information in the split data sets. The ND-based new model consists of two stages. The first stage adopts the ND method for large data sets splitting and processing, which can reduce the volume of data sets. The second stage implements the ND-based model in a cloud computing infrastructure for allocating the split data sets. The experimental results show substantial efficiency gains of the proposed method over the conventional methods without splitting data into small partitions. The ND-based method can generate representative data sets, which can offer efficient solution for large data processing. The split data sets can be processed in parallel in Cloud computing environment.

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Section: A Data Sources and Configurationsmentioning

confidence: 99%

Splitting Large Medical Data Sets Based on Normal Distribution in Cloud Environment

Zhang

Zhao

Pang

et al. 2020

IEEE Trans. Cloud Comput.

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“…Diabetes Our first benchmark is a dataset of 4 different ethnic groups of diabetes patients [13]. The original dataset consists of 47 attributes and 101 766 instances.…”

Section: Results On Non-synthetic Shiftsmentioning

confidence: 99%

Versatile Decision Trees for Learning Over Multiple Contexts

Alotaibi

Prudêncio

Kull

et al. 2015

Machine Learning and Knowledge Discovery in Databases

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Abstract. Discriminative models for classification assume that training and deployment data are drawn from the same distribution. The performance of these models can vary significantly when they are learned and deployed in different contexts with different data distributions. In the literature, this phenomenon is called dataset shift. In this paper, we address several important issues in the dataset shift problem. First, how can we automatically detect that there is a significant difference between training and deployment data to take action or adjust the model appropriately? Secondly, different shifts can occur in real applications (e.g., linear and non-linear), which require the use of diverse solutions. Thirdly, how should we combine the original model of the training data with other models to achieve better performance? This work offers two main contributions towards these issues. We propose a Versatile Model that is rich enough to handle different kinds of shift without making strong assumptions such as linearity, and furthermore does not require labelled data to identify the data shift at deployment. Empirical results on both synthetic shift and real datasets shift show strong performance gains by achieved the proposed model.

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“…Through an extensive review of related studies [11][12][13][14]22] and the help of domain experts, only attributes that are potentially associated with the diabetic condition or management influencing early readmissions were retained. Figure 3 lists the features which were used in building predictive models (italic features are derived features).…”

Section: Define Predictive Featuresmentioning

confidence: 99%

“…Numerous previous studies have analyzed the risk factors that predict readmission rates of patients with diabetes [9][10][11][12][13][14][15][16]. Jiang [9] studied demographic and socioeconomic factors which influence readmission rates.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predictive risk modelling for early hospital readmission of patients with diabetes in India

Duggal

Shukla²,

Chandra³

et al. 2016

Int J Diabetes Dev Ctries

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Hospital readmission is an important contributor to total medical expenditure and is an emerging indicator of quality of care. The goal of this study is to analyze key factors using machine learning methods and patients' medical records of a reputed Indian hospital which impact the all-purpose readmission of a patient with diabetes and compare different classification models that predict readmission and evaluate the best model. This study classified the patients into two different risk groups of readmission (Yes or No) within 30 days of discharge based on patients' characteristics using 2-year clinical and administrative data. It proposed an architecture of this prediction model and identified various risk factors using text mining techniques. Also, groups of consistently occurring factors that inference readmission rates were revealed by associative rule mining. It then evaluated the classification accuracy using five different data mining classifiers and conducted cost analysis. Out of total 9381 records, 1211 (12.9 %) encounters were found as readmissions. This study found that risk factors like hospital department where readmission happens, history of recent prior hospitalization and length of stay are strong predictors of readmission. Random forest was found to be the optimal classifier for this task using the evaluation metric area under precision-recall curve (0.296). From the cost analysis, it is observed that a cost of INR 15.92 million can be saved for 9381 instances of diabetic patient encounters. This work, the first such study done from Indian Healthcare perspective, built a model to predict the risk of readmission within 30 days of discharge for diabetes. This study concludes that the model could be incorporated in healthcare institutions to witness its effectiveness. Cost analysis shows huge savings which is significant for any healthcare system especially in developing countries like India.

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Impact of HbA1c Measurement on Hospital Readmission Rates: Analysis of 70,000 Clinical Database Patient Records

Cited by 237 publications

References 17 publications

Splitting Large Medical Data Sets Based on Normal Distribution in Cloud Environment

Splitting Large Medical Data Sets Based on Normal Distribution in Cloud Environment

Versatile Decision Trees for Learning Over Multiple Contexts

Predictive risk modelling for early hospital readmission of patients with diabetes in India

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