Applying unsupervised machine learning clustering techniques to hospital soundscapes

Abstract

Noise in hospitals can be problematic for both patients and staff and is consistently rated poorly on national patient satisfaction surveys. Previous studies have linked negative outcomes of hospital noise to numerous patient and staff challenges, such as reduced sleep and disrupted communication. Existing articles and guidelines commonly use equivalent sound pressure level as a primary noise metric. Additional insights into typical sound levels experienced by occupants can be found through more detailed statistical analyses of sound, such as by applying unsupervised machine learning clustering techniques. In this talk, clustering techniques will be explored in an effort to provide a more detailed analysis of the soundscape and various patterns of room activity. Noise data collected in three adult, inpatient hospital units will be analyzed using clustering techniques and compared against patient satisfaction scores. This more thorough, statistical characterization of the hospital soundscape can lead to better understanding of patterns of noise conditions and resultant occupant perceptions.