Empirical phenotyping of joint patient-care data supports hypothesis-driven investigation of mechanical ventilation consequences.

ABSTRACT: Analyzing patient data under current mechanical ventilation (MV) management processes is essential to understand MV consequences over time and to hypothesize improvements to care. However, progress is complicated by the complexity of lung-ventilator system (LVS) interactions, patient-care and patient-ventilator heterogeneity, and a lack of classification schemes for observable behavior. Ventilator waveform data originate from patient-ventilator interactions within the LVS while care processes manage both patients and ventilator settings. This study develops a computational pipeline to segment joint waveform and care settings timeseries data into phenotypes of the data generating process. The modular framework supports many methodological choices for representing waveform data and unsupervi

SUBMITTER: Stroh JN 

PROVIDER: S-EPMC12627839 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature