The application of data analytics in health care has led to discovering insightful and interesting information that can lead to advances in health care delivery [5]. The rapid advances in data science coupled with the growing amount of available data in all aspects of the health care industry make health care analytics even more efficient and beneficiary. In this study, we tried to add to the data mining literature by introducing the adjusted_support in rare item association analysis, and to the health care analytics literature by performing comorbidity and association analysis among major complications of diabetes.
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Several research studies have shown the impact of comorbidity on the management of diabetes. Poor management of comorbidity may lead to ineffective control of the disease and subsequent increases in both mortality rates and treatment costs ([105], [106]). The comparison of
comorbidity index values, showed that among racial/ethnic groups, Biracial, African Americans, Hispanics, and Native Americans had the highest number of comorbid complications. Moreover, Pacific Islanders, Middle Eastern Indians, Asians, and Caucasians had the lowest comorbidity index values, respectively. These results show a potential effective comorbidity management among Biracial, African Americans, Hispanics, and Native Americans and more attention should be paid to these races for a better disease control.
The next step in this study was taking a closer look at different diabetes complications prevalence among various demographic groups. Knowledge about the prevalence of complications among different groups of patients at more granular levels has several benefits. First, it could help policy makers to provide more effective high-level plans. For instance, if we know there is a higher rate of retinopathy among Native Americans compared to other races, it could be the indication of low level of ophthalmic care management among that race, thus necessary actions can be taken. Second, it would help the researchers to study relationships between genetic characteristics of people and different diseases. Third, it could help clinicians to provide targeted treatments and interventions for specific groups of patients. Based on the results of our study, neurological manifestations and heart disease were more prevalent among Caucasians, renal disease and hyperosmolarity were more prevalent among Asians, stroke and hyperosmolarity had the highest prevalence among African Americans, eye related diseases and hyperglycemia were more common among Native Americans, and Hispanics had the highest prevalence in ketoacidosis and gastroparesis compared to other races. By comparing diabetics in urban versus rural areas, we reached to these results: neurological manifestations, stroke, heart disease, and gastroparesis were more prevalent among urban patients; and renal manifestations, ophthalmic manifestations,
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retinopathy, peripheral circularity disorder, and hyperglycemia had a higher prevalence among diabetics in rural areas. Between different genders, females had higher rates of diagnosis with neurological manifestations, stroke, ophthalmic manifestations, retinopathy, and gastroparesis compared to males; and males were diagnosed with renal manifestations, peripheral circularity disorder, and heart disease more often compared to females.
Our methodological contribution in this research topic was addressing the rare item problem. To address this problem, we proposed a new objective metric for association rules and called it adjusted_support. By considering adjusted_support instead of support that has been used in traditional association analysis, we could capture the rare association rules from the data without over generating the useless association rules. We performed association analysis both in general diabetics’ population as well as various demographic groups for better understanding of the association patterns among complications in those demographic groups. The knowledge about the association among complications of diabetes can facilitate the diagnosing of different
complication of diabetes, it also can be a hint to study the scientific reasons behind those associations, and last but not the least it could lead to better management of diabetes and its comorbid complications.
All of the generated rules in our analysis were assessed by both objective and subjective metrics. For objective assessment, we used our proposed metric, adjusted_support beside support and lift. In addition, for subjective assessment we consulted with our medical advisors. Based on our results, skin complication was strongly associated with hyperglycemia, Peripheral circulatory disorder, heart disease, and neurological manifestations. Hyperosmolarity co-existed with ketoacidosis very often. Neurological manifestations co-existed with diabetic arthropathy and gastroparesis very frequently. Diabetics with renal manifestations were highly potential of suffering from eye related disease such as retinopathy. Finally, gastroparesis was strongly
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associated with both ketoacidosis and retinopathy. The results of the association analysis are provided in Section 3.2 in various demographic groups in more details.
Similar to any other research, we faced some limitations in this study. First, the scope of our research was limited to the complications of diabetes that are specified in ICD 9 and ICD 10. Therefore, other disease that patients may have been diagnosed with, were not considered in our study. Perhaps including those potential existing complications would lead to even more
insightful findings. Another limitation was related to the nature of EHR data. Because these types of dataset are collected for reasons other than the purpose of this research, they may lack some degree of accuracy, for instance, some of the complications of a patient may not be recorded in her visit. However, the large amount of the data that was available in our study can compensate this limitation.
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CHAPTER IV
A DATA ANALYTICS APPROACH TO BUILDING A CLINICAL DECISION SUPPORT SYSTEM FOR DIABETIC RETINOPATHY: DEVELOPING AND DEPLOYING A MODEL
ENSEMBLE
In this chapter, we explain various steps of developing the CDSS for diabetic retinopathy. We also present our proposed ensemble approach, confidence margin and assess its performance in comparison with existing ensemble methods. We expect that the CDSS we develop in this effort will be able to detect diabetic retinopathy at its early stages with a high degree of accuracy. This CDSS, which relies exclusively on lab data, not only helps overcome one of the major barriers to the early diagnosis of diabetic retinopathy, but also provides a new standard of care that will improve quality and increase compliance in healthcare without raising costs.