6533b85efe1ef96bd12bfd3a
RESEARCH PRODUCT
Predicting hospital associated disability from imbalanced data using supervised learning.
Sami ÄYrämöOlli-pekka RyynänenMirka Saarelasubject
Association rule learningmedicine.medical_treatmentvanhuksetMedicine (miscellaneous)sairaalahoitoOutcome (game theory)Task (project management)03 medical and health sciences0302 clinical medicineArtificial IntelligenceMedicineHumanstoimintarajoitteetDisabled PersonsSet (psychology)Adverse effectFinlandta316030304 developmental biologyAgedta1130303 health sciencesRehabilitationbusiness.industrySupervised learningennusteetta3142medicine.diseaseMedical researchHospitalizationmachine learningkoneoppiminenhospital associated disabilityMedical emergencySupervised Machine Learningtiedonlouhintabusiness030217 neurology & neurosurgeryrandom forestdescription
Hospitalization of elderly patients can lead to serious adverse effects on their functional capability. Identifying the underlying factors leading to such adverse effects is an active area of medical research. The purpose of the current paper is to show the potential of artificial intelligence in the form of machine learning to complement the existing medical research. This is accomplished by studying the outcome of hospitalization of elderly patients as a supervised learning task. A rich set of features characterizing the medical and social situation of elderly patients is leveraged and using confusion matrices, association rule mining, and two different classes of supervised learning algorithms, it is shown that the need for help and supervision are the most important features predicting whether these patients will return home after hospitalization. Such findings can help to improve hospitalization and rehabilitation of elderly patients. peerReviewed
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-01-01 | Artificial intelligence in medicine |