Despite all the promises of analytics, its complexity, multidimensionality, and multidisciplinary... more Despite all the promises of analytics, its complexity, multidimensionality, and multidisciplinary nature can sometimes disserve its efficacy. What can further aggravate the problem is the need to deal with human behavior and social interactions as inherent qualities of the application domain. One such area is the drug court; an alternative for traditional criminal courts that attempts to transform the traditional punitive jurisprudence to a therapeutic one. Under this new philosophy, the eligible offenders are considered as individuals in need of rehabilitative treatments and are persuaded to undergo a regimen that seeks to return them back to the community, rather than sending them to prison. This initiative, if performed properly, has proven to be effective in lowering the costs and improving the social outcomes. While many researchers have studied this initiative from the perspective of its factors, requirements, and tradeoffs, there currently is a lack of a comprehensive analytics model that can accurately predict who would (or would not) graduate from these programs. To fill this gap, and to enable better management of resources and improvement of outcomes, this study develops an analytics model to describe a large real-world sample of drug court participants; to predict who would or would not graduate from these courts; and to prescribe a set of guidelines (presented as characteristics of the offenders) that can help jurisdictions and drug court administrators to make more effective and efficient decisions.
Title: Post-Acute Care Referral in United States of America: A Multiregional Study of Factors Ass... more Title: Post-Acute Care Referral in United States of America: A Multiregional Study of Factors Associated with Referral Destination in a Cohort of Patients with Coronary Artery Bypass Graft or Valve Replacement Authors: Ineen Sultana (ineensultana@tamu.edu) Madhav Erraguntla (merraguntla@tamu.edu) Hye-Chung Kum (kum@tamu.edu) Dursun Delen (dursun.delen@okstate.edu) Mark Lawley (malawley@tamu.edu) Version: 1 Date: 14 Dec 2018 Author’s response to reviews: Response to Reviewer(s)' Comments
RFID for Better Supply-Chain Management ... Supply Chain Visibility The motivation behind supplyc... more RFID for Better Supply-Chain Management ... Supply Chain Visibility The motivation behind supplychain management is to eliminate the barriers by enabling the synchronization and sharing of valuable information among trading partners (Kouvelis, Chambers, and Wang 2006). ...
The concept of customersatisfaction and loyalty (CS&L) has attracted much attention in recent yea... more The concept of customersatisfaction and loyalty (CS&L) has attracted much attention in recent years. A key motivation for the fast growing emphasis on CS&L can be attributed to the fact that higher customersatisfaction and loyalty can lead to stronger competitive position resulting in larger market share and profitability. Using a data envelopment analysis (DEA) approach, in this study we analyzed and compared CS&L efficiency for mobilephonebrands in an emerging telecommunication market, Turkey. The constructs of European CustomerSatisfaction Index (ECSI) model are treated and used as input and output indicators of our DEA model. Drawing on the perceptual responses of 251 mobilephone users, the DEA models reveal that from the top six mobilephonebrands in Turkey, Nokia features as the most efficient brand followed by LG and Sonny Ericsson in terms of CS&L efficiency, while Motorola, Samsung and Panasonic rank as the least efficient brands
Epilepsy is one of the most common brain disorders that greatly affects patients’ quality of life... more Epilepsy is one of the most common brain disorders that greatly affects patients’ quality of life and poses serious risks to their health. While the majority of the patients positively respond to the existing anti-epilepsy drugs, others who developed the refractory type of epilepsy show resistance against drug therapy and need to undergo advance treatments such as surgery. Given that identifying such patients is not a straightforward process and requires long courses of trial and error with anti-epilepsy drugs, this study aims at predicting those at-risk patients using clinical and demographic data obtained from electronic medical records. Specifically, the study employs several predictive analytics machine-learning methods, equipped with a novel approach for data balancing, to identify drug-resistant patients using their comorbidities and demographic information along with the initial epilepsy-related diagnosis made by their physician. The promising results we obtained highlight th...
Studies on diabetes have shown that population subgroups have varying rates of medical events and... more Studies on diabetes have shown that population subgroups have varying rates of medical events and related procedures; however, existing studies have investigated either medical events or procedures, and hence, it is unknown whether disparities exist between medical events and procedures. The objective of this study is to investigate how diabetes-related medical events and procedures are different across population subgroups through a social determinants of health (SDH) perspective. Because the purpose of this manuscript is to explore whether statistically significant health disparities exist across population subgroups regarding diabetes patients' medical events and procedures, group difference test methods were employed. Diabetes patients' data were drawn from the Cerner Health Facts® data warehouse. The study revealed systematic disparities across population subgroups regarding medical events and procedures. The most significant disparities were connected with smoking stat...
End-stage renal disease (ESRD), which is primarily a consequence of diabetes mellitus, shows an e... more End-stage renal disease (ESRD), which is primarily a consequence of diabetes mellitus, shows an exemplary health disparity between African American and Caucasian patients in the United States. Because diabetic chronic kidney disease (CKD) patients of these two groups show differences in their medical problems, the markers leading to ESRD are also expected to differ. The purpose of this study was, therefore, to compare their medical complications at various levels of kidney function and to identify markers that can be used to predict ESRD. The data of type 2 diabetic patients was obtained from the 2012 Cerner database, which totaled 1,038,499 records. The data was then filtered to include only African American and Caucasian outpatients with estimated glomerular filtration rates (eGFR), leaving 4,623 records. A priori machine learning was used to discover frequently appearing medical problems within the filtered data. CKD is defined as abnormalities of kidney structure, present for &g...
Crohn's disease is among the chronic inflammatory bowel diseases that impact the gastrointest... more Crohn's disease is among the chronic inflammatory bowel diseases that impact the gastrointestinal tract. Understanding and predicting the severity of inflammation in real-time settings is critical to disease management. Extant literature has primarily focused on studies that are conducted in clinical trial settings to investigate the impact of a drug treatment on the remission status of the disease. This research proposes an analytics methodology where three different types of prediction models are developed to predict and to explain the severity of inflammation in patients diagnosed with Crohn's disease. The results show that machine-learning-based analytic methods such as gradient boosting machines can predict the inflammation severity with a very high accuracy (area under the curve = 92.82%), followed by regularized regression and logistic regression. According to the findings, a combination of baseline laboratory parameters, patient demographic characteristics, and disea...
This article describes how the metrics that are used to gauge acceptable versus inadequate care h... more This article describes how the metrics that are used to gauge acceptable versus inadequate care have spurred debates among health care administrators and scholars. Specifically, they argue that the use of readmissions as a quality-of-care metric may reduce patients' safety. Consequently, the new well-intended policies may prove ineffective, or even worse, yield disappointing results. While the discussions over the advantages and disadvantages of the new policies are based more on conjectures rather than on evidence, analytics provides a vehicle to measure the effectiveness of such overarching strategies. In this effort, the authors analyze large volumes of hospital encounters data before and after the implementation of the Patient Protection and Affordable Care Act (PPACA) to show how overlooking some aspects of a problem may lead to unexpected outcomes. The authors conclude that the feedback provided by big data analytics can be used by the government and organization policymak...
Despite all the promises of analytics, its complexity, multidimensionality, and multidisciplinary... more Despite all the promises of analytics, its complexity, multidimensionality, and multidisciplinary nature can sometimes disserve its efficacy. What can further aggravate the problem is the need to deal with human behavior and social interactions as inherent qualities of the application domain. One such area is the drug court; an alternative for traditional criminal courts that attempts to transform the traditional punitive jurisprudence to a therapeutic one. Under this new philosophy, the eligible offenders are considered as individuals in need of rehabilitative treatments and are persuaded to undergo a regimen that seeks to return them back to the community, rather than sending them to prison. This initiative, if performed properly, has proven to be effective in lowering the costs and improving the social outcomes. While many researchers have studied this initiative from the perspective of its factors, requirements, and tradeoffs, there currently is a lack of a comprehensive analytics model that can accurately predict who would (or would not) graduate from these programs. To fill this gap, and to enable better management of resources and improvement of outcomes, this study develops an analytics model to describe a large real-world sample of drug court participants; to predict who would or would not graduate from these courts; and to prescribe a set of guidelines (presented as characteristics of the offenders) that can help jurisdictions and drug court administrators to make more effective and efficient decisions.
Title: Post-Acute Care Referral in United States of America: A Multiregional Study of Factors Ass... more Title: Post-Acute Care Referral in United States of America: A Multiregional Study of Factors Associated with Referral Destination in a Cohort of Patients with Coronary Artery Bypass Graft or Valve Replacement Authors: Ineen Sultana (ineensultana@tamu.edu) Madhav Erraguntla (merraguntla@tamu.edu) Hye-Chung Kum (kum@tamu.edu) Dursun Delen (dursun.delen@okstate.edu) Mark Lawley (malawley@tamu.edu) Version: 1 Date: 14 Dec 2018 Author’s response to reviews: Response to Reviewer(s)' Comments
RFID for Better Supply-Chain Management ... Supply Chain Visibility The motivation behind supplyc... more RFID for Better Supply-Chain Management ... Supply Chain Visibility The motivation behind supplychain management is to eliminate the barriers by enabling the synchronization and sharing of valuable information among trading partners (Kouvelis, Chambers, and Wang 2006). ...
The concept of customersatisfaction and loyalty (CS&L) has attracted much attention in recent yea... more The concept of customersatisfaction and loyalty (CS&L) has attracted much attention in recent years. A key motivation for the fast growing emphasis on CS&L can be attributed to the fact that higher customersatisfaction and loyalty can lead to stronger competitive position resulting in larger market share and profitability. Using a data envelopment analysis (DEA) approach, in this study we analyzed and compared CS&L efficiency for mobilephonebrands in an emerging telecommunication market, Turkey. The constructs of European CustomerSatisfaction Index (ECSI) model are treated and used as input and output indicators of our DEA model. Drawing on the perceptual responses of 251 mobilephone users, the DEA models reveal that from the top six mobilephonebrands in Turkey, Nokia features as the most efficient brand followed by LG and Sonny Ericsson in terms of CS&L efficiency, while Motorola, Samsung and Panasonic rank as the least efficient brands
Epilepsy is one of the most common brain disorders that greatly affects patients’ quality of life... more Epilepsy is one of the most common brain disorders that greatly affects patients’ quality of life and poses serious risks to their health. While the majority of the patients positively respond to the existing anti-epilepsy drugs, others who developed the refractory type of epilepsy show resistance against drug therapy and need to undergo advance treatments such as surgery. Given that identifying such patients is not a straightforward process and requires long courses of trial and error with anti-epilepsy drugs, this study aims at predicting those at-risk patients using clinical and demographic data obtained from electronic medical records. Specifically, the study employs several predictive analytics machine-learning methods, equipped with a novel approach for data balancing, to identify drug-resistant patients using their comorbidities and demographic information along with the initial epilepsy-related diagnosis made by their physician. The promising results we obtained highlight th...
Studies on diabetes have shown that population subgroups have varying rates of medical events and... more Studies on diabetes have shown that population subgroups have varying rates of medical events and related procedures; however, existing studies have investigated either medical events or procedures, and hence, it is unknown whether disparities exist between medical events and procedures. The objective of this study is to investigate how diabetes-related medical events and procedures are different across population subgroups through a social determinants of health (SDH) perspective. Because the purpose of this manuscript is to explore whether statistically significant health disparities exist across population subgroups regarding diabetes patients' medical events and procedures, group difference test methods were employed. Diabetes patients' data were drawn from the Cerner Health Facts® data warehouse. The study revealed systematic disparities across population subgroups regarding medical events and procedures. The most significant disparities were connected with smoking stat...
End-stage renal disease (ESRD), which is primarily a consequence of diabetes mellitus, shows an e... more End-stage renal disease (ESRD), which is primarily a consequence of diabetes mellitus, shows an exemplary health disparity between African American and Caucasian patients in the United States. Because diabetic chronic kidney disease (CKD) patients of these two groups show differences in their medical problems, the markers leading to ESRD are also expected to differ. The purpose of this study was, therefore, to compare their medical complications at various levels of kidney function and to identify markers that can be used to predict ESRD. The data of type 2 diabetic patients was obtained from the 2012 Cerner database, which totaled 1,038,499 records. The data was then filtered to include only African American and Caucasian outpatients with estimated glomerular filtration rates (eGFR), leaving 4,623 records. A priori machine learning was used to discover frequently appearing medical problems within the filtered data. CKD is defined as abnormalities of kidney structure, present for &g...
Crohn's disease is among the chronic inflammatory bowel diseases that impact the gastrointest... more Crohn's disease is among the chronic inflammatory bowel diseases that impact the gastrointestinal tract. Understanding and predicting the severity of inflammation in real-time settings is critical to disease management. Extant literature has primarily focused on studies that are conducted in clinical trial settings to investigate the impact of a drug treatment on the remission status of the disease. This research proposes an analytics methodology where three different types of prediction models are developed to predict and to explain the severity of inflammation in patients diagnosed with Crohn's disease. The results show that machine-learning-based analytic methods such as gradient boosting machines can predict the inflammation severity with a very high accuracy (area under the curve = 92.82%), followed by regularized regression and logistic regression. According to the findings, a combination of baseline laboratory parameters, patient demographic characteristics, and disea...
This article describes how the metrics that are used to gauge acceptable versus inadequate care h... more This article describes how the metrics that are used to gauge acceptable versus inadequate care have spurred debates among health care administrators and scholars. Specifically, they argue that the use of readmissions as a quality-of-care metric may reduce patients' safety. Consequently, the new well-intended policies may prove ineffective, or even worse, yield disappointing results. While the discussions over the advantages and disadvantages of the new policies are based more on conjectures rather than on evidence, analytics provides a vehicle to measure the effectiveness of such overarching strategies. In this effort, the authors analyze large volumes of hospital encounters data before and after the implementation of the Patient Protection and Affordable Care Act (PPACA) to show how overlooking some aspects of a problem may lead to unexpected outcomes. The authors conclude that the feedback provided by big data analytics can be used by the government and organization policymak...
Uploads
Papers