[2E1-GS-10] AI application

[2E1-GS-10-03] Validation of biosignal data for early prediction of delirium

〇Teruya Yamamoto^1,2, Hiroto Sano³, Wataru Matsuzawa³, Jun Ogata², Hidenori Sakanashi^1,2, Hirokazu Nosato² (1. University of Tsukuba, 2. National Institute of Advanced Industrial Science and Technology, 3. NIHON KOHDEN CORPORATION)

Keywords:Delirium

Delirium is a type of cognitive dysfunction. Patients suffering from delirium require appropriate treatment, including early
detection and careful medication, due to increased medical costs and prolonged hospitalization. However, since the diagnosis
of delirium is generally costly in terms of manpower and time, the establishment of early automated diagnostic techniques is
desirable. Therefore, this study aims to realize a multimodal early prediction model of delirium using height and weight, heart
rate, blood glucose level, and externally captured video images of the patient and so on. In this paper, we followed the model
proposed in a previous study, appropriately cleansed patient information obtained from the public dataset MIMIC-III, and
validated it to evaluate the method proposed in the previous study. The results confirm the suitability of emergency medicine
data for use in multimodal delirium early prediction models.