Analysis of healthcare coverage: A data mining approach

Delen, Dursun; Fuller, Christie M.; McCann, C. Douglas; Ray, Deepa

doi:10.1016/j.eswa.2007.10.041

Cited by 48 publications

(17 citation statements)

References 26 publications

(48 reference statements)

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“…Much research in health services considers access to care, either as a primary study topic or as a factor for which to control. 8,[37][38][39][40][41][42][43] These existing studies often rely on measures of access that are too simple to account for systemwide trade-offs, or measures computed at high geographic aggregation levels. This paper demonstrates that these limitations can result in misleading policies and interventions and that high geographic resolution estimates are needed to understand the nuances in health care access where countylevel estimates wash out important differences.…”

Section: Discussionmentioning

confidence: 99%

Small-Area Estimation of Spatial Access to Care and Its Implications for Policy

et al. 2015

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Local or small-area estimates to capture emerging trends across large geographic regions are critical in identifying and addressing community-level health interventions. However, they are often unavailable due to lack of analytic capabilities in compiling and integrating extensive datasets and complementing them with the knowledge about variations in state-level health policies. This study introduces a modeling approach for small-area estimation of spatial access to pediatric primary care that is data "rich" and mathematically rigorous, integrating data and health policy in a systematic way. We illustrate the sensitivity of the model to policy decision making across large geographic regions by performing a systematic comparison of the estimates at the census tract and county levels for Georgia and California. Our results show the proposed approach is able to overcome limitations of other existing models by capturing patient and provider preferences and by incorporating possible changes in health policies. The primary finding is systematic underestimation of spatial access, and inaccurate estimates of disparities across population and across geography at the county level with respect to those at the census tract level with implications on where to focus and which type of interventions to consider.

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Section: Discussionmentioning

confidence: 99%

Small-Area Estimation of Spatial Access to Care and Its Implications for Policy

et al. 2015

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“…Artificial neural networks (ANNs) are commonly known as biologically inspired, highly sophisticated analytical techniques, capable of capturing highly complex non-linear functions. In this study, we use a popular ANN architecture called Multi-Layer Perceptron (MLP) with back-propagation (a supervised learning algorithm) [6]. DTs are an effective method of classifying dataset entries and can provide good decision support capabilities.…”

Section: Introductionmentioning

confidence: 99%

“…The most commonly used mathematical algorithm for splitting includes Entropy based information gain (used in ID3, C4.5, C5), Gini index (used in CART), and chisquared test (used in CHAID). Based on the favorable prediction results we have obtained from the preliminary runs, in this study we chose to use C5 algorithm as our decision tree method [6]. Event log have been used on many computer systems for recording errors occurring in hardware and software components of the systems.…”

Section: Introductionmentioning

confidence: 99%

Comparison between ANN and Decision Tree in Aerology Event Prediction

Lajevardi

Minaei-Bidgoli

2008

2008 International Conference on Advanced Computer Theory and Engineering

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Predictive systems use historical and other available data to predict an event. In this paper we tries to compare the power of Artificial Neural Network (ANN) and Decision Tree (DT) in prediction of aerology events with time series streams and events stream using combination of K-means clustering algorithm and Decision Tree C5 algorithm and ANN. We try to find the effective parameters on events occurrences. Firstly, we find the closest time series record for any events; therefore, we have gathered different parameters value when an event is occurring. Using Kmeans we add a field to dataset which determines the cluster of each record and after that we predict the events using C5 algorithm and ANN. This framework and time series model can predict future events efficiently. We gathered 1961 until 2005 data of aerology organization for Tehran Mehrabad Station. This data contains some fields such as wet bulb, relative humidity, amount of cloud, wind speed and etc. This dataset includes 17 types of events. Using this framework the closest event can be predicted. The C5 method is able to predict events with 79.55% accuracy and ANN with 72.87% accuracy. Applying K-means clustering algorithm the prediction increase to 94.59% for C5 and 92.66% accuracies for ANN. We use 10-fold cross validation to evaluate our prediction rate. This framework is the first estimation in the area of event prediction for a huge dataset of aerology and can be extended in many different datasets in any other environments.

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“…This step is repeated at each leaf node until the complete tree is constructed. The objective of the splitting algorithm is to find a variable-threshold pair that maximizes the homogeneity (order) of the resulting two or more subgroups of samples (Delen, Fuller, McCann, & Ray, 2009).…”

Section: Decision Trees (J48)mentioning

confidence: 99%

Data mining applied to the cognitive rehabilitation of patients with acquired brain injury

Marcano-Cedeño

Chausa

García

et al. 2013

Expert Systems with Applications

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Acquired brain injury (ABI) is one of the leading causes of death and disability in the world and is associated with high health care costs as a result of the acute treatment and long term rehabilitation involved. Different algorithms and methods have been proposed to predict the effectiveness of rehabilitation programs. In general, research has focused on predicting the overall improvement of patients with ABI. The purpose of this study is the novel application of data mining (DM) techniques to predict the outcomes of cognitive rehabilitation in patients with ABI. We generate three predictive models that allow us to obtain new knowledge to evaluate and improve the effectiveness of the cognitive rehabilitation process. Decision tree (DT), multilayer perceptron (MLP) and general regression neural network (GRNN) have been used to construct the prediction models. 10-fold cross validation was carried out in order to test the algorithms, using the Institut Guttmann Neurorehabilitation Hospital (IG) patients database. Performance of the models was tested through specificity, sensitivity and accuracy analysis and confusion matrix analysis. The experimental results obtained by DT are clearly superior with a prediction average accuracy of 90.38%, while MLP and GRRN obtained a 78.7% and 75.96%, respectively. This study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients.

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Analysis of healthcare coverage: A data mining approach

Cited by 48 publications

References 26 publications

Small-Area Estimation of Spatial Access to Care and Its Implications for Policy

Small-Area Estimation of Spatial Access to Care and Its Implications for Policy

Comparison between ANN and Decision Tree in Aerology Event Prediction

Data mining applied to the cognitive rehabilitation of patients with acquired brain injury

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