Treatment for people living with multiple chronic conditions (MCC) currently accounts for an estimated 66 percent of the Nation's health care costs and will continue to grow. This mounting challenge has become a major public health issue since MCC is linked to suboptimal health outcomes and rising health care costs. However, it now known how MCC emerge among individuals or in the general population. Traditional epidemiological approaches have led to important findings of disease links and comorbidity associations. However, they are limited in their ability to characterize how patients acquire new chronic conditions and predict/personalize the emergence of MCC for individual patients. Objectives: This study will develop approaches that can be used to identify the most likely combinations of comorbidity within a population. These approaches can be tailored to examine MCC patterns in specific sub- populations or at the level of the individual patient. We will also study the effect of a large set of risk factors on MCC combinations emergence. Furthermore, we will use data mining approaches to predict and monitor the development of MCC combinations in at the population and individual levels. Hypotheses: We hypothesize that the emergence and progression of comorbidities in MCC patients form patterns that can be predicted and which are associated with prior medical conditions, demographic, and socio-economic characteristics. We further hypothesize that these methods can predict the timing and emergence of new chronic diseases more accurately by personalizing the records for individual patients.
Aims and methodology:
in Aim1, we will characterize how MCC emerge and progress in distinct patterns and how these patterns transition between different combinations of diseases over time. We will then identify and group major MCC transitions using the Markov clustering (MCL) algorithm, which is a novel, efficient graphical approach to handle big data.
In Aim 2, we will identify which risk factors are associated with MCC emergence using a machine learning approach that can handle the complex heterogeneity of comorbidity patterns. Risk factors include age, sex, race/ethnicity, education, economic status, marital status, and prior medical conditions.
In Aim 3, we will use a similarity learning approach to develop models that can predict if MCC will emerge in individual patients or among populations and we will be able to use these models to monitor MCC emergence over time. Conclusion: Our findings will provide a foundation for future research that will evaluate specific treatment patterns associated with progression in MCC patterns and ultimately identify optimal time points of intervention for those with, or at risk for multiple chronic conditions. These findings will also provide information that can be used at the community level to manage healthcare resources to improve continuity and accessibility of care.
This research proposal aims to better understand the emergence of multiple chronic conditions by identifying and predicting emerging diseases in young adults and discovering the patient characteristics associated with comorbidity emergence and progression.
| Alaeddini, Adel; Jaramillo, Carlos A; Faruqui, Syed H A et al. (2017) Mining Major Transitions of Chronic Conditions in Patients with Multiple Chronic Conditions. Methods Inf Med 56:391-400 |
| Alaeddini, Adel; Hong, Seung Hee (2017) A Multi-way Multi-task Learning Approach for Multinomial Logistic Regression*. An Application in Joint Prediction of Appointment Miss-opportunities across Multiple Clinics. Methods Inf Med 56:294-307 |
