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Clinical Problem and Individual Components
How Long Should Cardiac Disease Patients Stay in Hospitals?
In the quest to find the duration the cardiac patients should stay in hospitals, the study analyzes various components generated from the available data. The research will examine the number of patients suffering from cardiac arrest who were admitted to a specific hospital for five years. Only data relating to CAD will be used in the study to offer a reliable result. The data will be analyzed using the relevant methods to answer the research question.
The data will be extracted from patient records in the hospital’s database. Notably, the hospital should be embracing an electronic health record system for easy extraction of data. It will be useful for the database to be understood before extracting the data to avoid unnecessary errors (Ahmad, Qamar & Rizvi, 2015). The extracted data will be transferred to Microsoft structured query language (MS-SQL) database for storage. It is from this database that a new set of data set for the length of stay of CAD will be extracted. It will be then organized into two types, including numerical features and categorical (Rojas et al., 2016). The data values will further be categorized to derive new fields from the presented data. The new components or fields include pulse rate, chest pain, high-density lipoprotein, cholesterol, systolic blood pressure, diastolic blood pressure, smoking, low-density lipoprotein, and triglyceride. The change of the categorical attributes will most definitely make it easy for analysis and obtaining of good results.
The research question’s attributes will then be analyzed using data mining techniques with the aim of predicting the length of stay. The three fundamental methods, including classification analysis, regression analysis, and prediction will be combined to answer the research question. The methods will enable hospitals to predict the duration and budget according to avoid unnecessary wastage of funds.
References
Ahmad, P., Qamar, S., & Rizvi, S. Q. A. (2015). Techniques of data mining in healthcare: a review. International Journal of Computer Applications, 120(15).
Rojas, E., Munoz-Gama, J., Sepúlveda, M., & Capurro, D. (2016). Process mining in healthcare: A literature review. Journal of biomedical informatics, 61, 224-236.
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