Automatic Classification of Hospital Settings through Artificial Intelligence

Abstract

Modern hospitals have to meet requirements from national and international institutions in order to ensure hygiene, quality and organisational standards. Moreover, a hospital must be flexible and adaptable to new delivery models for healthcare services. Various hospital monitoring tools have been developed over the years, which allow for a detailed picture of the effectiveness and efficiency of the hospital itself. Many of these systems are based on database management systems (DBMSs), building information modelling (BIM) or geographic information systems (GISs). This work presents an automatic recognition system for hospital settings that integrates these tools. Three alternative proposals were analysed in terms of the construction of the system: the first was based on the use of general models that are present on the cloud for the classification of images; the second consisted of the creation of a customised model and referred to the Clarifai Custom Model service; the third used an object recognition software that was developed by Facebook AI Research combined with a random forest classifier. The obtained results were promising. The customised model almost always classified the photos according to the correct intended use, resulting in a high percentage of confidence of up to 96%. Classification using the third tool was excellent when considering a limited number of hospital settings, with a peak accuracy of higher than 99% and an area under the ROC curve (AUC) of one for specific classes. As expected, increasing the number of room typologies to be discerned negatively affected performance.