  Clinical decision support systems (CDSS) will play an in-creasing role in
improving the quality of medical care for critically ill patients. However, due
to limitations in current informatics infrastructure, CDSS do not always have
com-plete information on state of supporting physiologic monitor-ing devices,
which can limit the input data available to CDSS. This is especially true in
the use case of mechanical ventilation (MV), where current CDSS have no
knowledge of critical ventilation settings, such as ventilation mode. To enable
MV CDSS to make accurate recommendations related to ventilator mode, we
developed a highly performant ma-chine learning model that is able to perform
per-breath clas-sification of 5 of the most widely used ventilation modes in
the USA with an average F1-score of 97.52%. We also show how our approach makes
methodologic improvements over previous work and that it is highly robust to
missing data caused by software/sensor error.
