Project

Reducing Alarm Fatigue

Goal: Build an adaptive aiding system incorporating machine learning for reducing alarm fatigue in hospitals.

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Project log

Albert Boulanger
added an update
Bingyue He Alarm Fatigue Reports
 
Albert Boulanger
added a research item
Hum-Dinger aims to manage alarms by providing suggestions to medical care practitioners to adjust alarm settings on monitoring devices and other actions so that alarm fatigue is minimized. We use an adaptive aiding approach where adaption is based on machine learning. The product integrates continuous monitor data from distributed monitors and clinical data from the EHR database using the HL7 protocol. The actions taken, including dismissal of alarms, are the machine learning training labels. Gamification is used to interject performance nudges for medical care practitioners that exploits and drives stronger man-machine symbiosis.
Albert Boulanger
added an update
Nikhil Mitra's Final Project Report Dec 2016
Weston Jackson's Final Project Report May 2017 (see also research section for this project)
Zehao Dong's report May 2018
Yingying Huang's Final Report May 2018 (on using Bokeh)
 
Albert Boulanger
added a project goal
Build an adaptive aiding system incorporating machine learning for reducing alarm fatigue in hospitals.