We present here the Probabilistic Unit Life Status Estimation (PULSE) methodology and software architecture. The PULSE approach leverages Temporal Belief Networks (TBNs) to model the subject's fundamental physiological dynamics (e.g. shock response) over time, to perform life status estimation in a robust manner that accounts for sensor hardware malfunctions. We developed a limited-scope prototype using our in-house TBN engine to estimate clinical status, which served as a framework to demonstrate the PULSE approach for two separate but related clinical assessment tasks. In the first task, the PULSE prototype provides a life status assessment of a simulated unit, allowing for the principled introduction of noise to demonstrate the system 's ability to detect sensor failure. In the second task, the PULSE prototype provides a clinical assessment of a unit 's degree of acclimatization, using pre-recorded data from studies of soldiers at altitude.