A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics
Estimating patient's clinical state from multiple concurrent physiological streams plays an important role in determining if a therapeutic intervention is necessary and for triaging patients in the hospital. In this paper we construct a non-parametric learning algorithm to estimate the clinical state of a patient. The algorithm addresses several known challenges with clinical state estimation such as eliminating the bias introduced by therapeutic intervention censoring, increasing the timeliness of state estimation while ensuring a sufficient accuracy, and the ability to detect anomalous clinical states. These benefits are obtained by combining the tools of non-parametric Bayesian inference, permutation testing, and generalizations of the empirical Bernstein inequality. The algorithm is validated using real-world data from a cancer ward in a large academic hospital.
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Thank you for the submission. There are a few points of clinical clarity.  (1) The use of the term clinical state lacks specificity in the scope of this paper. The more targeted goal that is in line with other models in this area is to predict clinical deterioration (a shift in patient condition that requires a higher level of care). The goal of "clinical state" is less about understanding what it is but how shifts in the state drive an intervention (i.e. ICU transfer) (2) The authors should clarify the use case they describe. Though care practices may vary, in general, patients of these types receive treatment on an outpatient basis (i.e. they would come to the hospital daily to receive their treatment and then go home). Please confirm with your data providers whether or not this is the case. If so, I would modify the x axis of Figure 2a and 2b to be days (rather than hours). Hours seems to imply real time telemetry like data or an inpatient stay where vitals are checked routinely throughout the day and night. The authors do refer to admission time from 4 days to 85 days in some cases, but I'm not sure why these patients were admitted to begin with. It seems that there is some other inciting event that put them in the hospital (aside from just getting chemotherapy treatment). (3) If the authors confirm that the data is outpatient data, then a note should be made that the comparison to MEWS and Rothman index are somewhat apples to oranges in that both are designed for inpatient settings. However the comparison is fine as they are both aggregate scores of clinical conditions. (4) The authors hint at the models doing prediction with data 12 hours before an ICU admission or discharge. Is this in is a cross-validation framework? It seems like the same dataset is used to train and test the models so it is unclear what the model is missing. (5) Did the patient actually go to the ICU or are these estimates for what the labeling clinician thought as a set of vitals that actually required an ICU admission (i.e. the physicians were not given any clinical details and simply given the 17 temporal physiologic streams)? (6) line 252, I think you are referring to the wrong figure. (7) I'm a bit confused as to the defined priors. In the context of this study, the outcome is known (ICU vs discharge). In a practical application, there will be long runs when the next 12 or 24 hours will not result in any change. It would seem the prior distribution of the ICU conversion would be much smaller than indicated by the test data.