An early hospital readmission means that a newly discharged patient is readmitted within a small time frame (< 30 days) due to reasons directly related to the original admission. This generally runs the risk of negatively impacting both the wellbeing of the patient in question as well as the hospice care unit admitting the patient economically. Being able to use modern computational tools to predict which patients run a large risk of soon becoming admitted once more either prior to or during their discharge can help in the task of preventing these incidents altogether. During this study, 65 different machine learning models were trained on a dataset assembled using metrics from 130 American hospitals over a 10-year period. While the dataset is specialised on patients affected by diabetes, the study also presents generalized models trained on a version of the dataset free from attributes unique to patients affected by diabetes. Several of these models are trained using methods specifically designed to counter an inherent class imbalance issue present within the chosen problem domain. The study results in the presentation of several performance related metrics of the trained models, including AUC scores and an approximation of the early readmission cost per patient predicted using the different models. Lastly, the study concludes with some examples of potential alternative methods that may further evolve the performance of the models designed for this task as well as a discussion regarding the ethics of deploying such a solution in the real world.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:miun-45895 |
| Date | January 2022 |
| Creators | Temmel, Adam |
| Publisher | Mittuniversitetet, Institutionen för informationssystem och –teknologi |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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