Abstract
Healthcare spending has been increasing in the last few decades. This increase can be attributed to hospital readmissions, which is defined as a re-hospitalization of a patient after being discharged from a hospital within a short period of time. The correct selection of the primary medical procedure by physicians is the first step in the patient treatment process and is considered to be of the main causes for hospital readmissions. In this paper, we propose a recommender system that can accurately predict the primary medical procedure for a new admitted patient, given his or her set of diagnoses. The core of the recommender system relies on identifying other existing patients that are considered similar to the new patient. That said, we propose three approaches to predict the primary procedure. The results show the ability of our proposed system to identify the primary procedure. It can be later used to build a graph which shows all possible paths that a patient may undertake during the course of treatment.
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Acknowledgment
This work was supported by SAS Institute under UNC-Charlotte Internal Grant No. 15-0645.
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Almardini, M., Hajja, A., Raś, Z.W., Clover, L., Olaleye, D. (2017). Predicting the Primary Medical Procedure Through Clustering of Patients’ Diagnoses. In: Appice, A., Ceci, M., Loglisci, C., Masciari, E., Raś, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2016. Lecture Notes in Computer Science(), vol 10312. Springer, Cham. https://doi.org/10.1007/978-3-319-61461-8_8
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DOI: https://doi.org/10.1007/978-3-319-61461-8_8
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