This document discusses remote patient monitoring and the use of artificial intelligence for sepsis detection. It provides an example of Duke University's sepsis watch system which uses an AI model to identify patients at risk of sepsis in emergency departments. The document discusses issues with explainability of deep learning models and whether human interpretation is needed. It then provides an example use case of detecting sepsis shock and considers decision trees and their limitations for modeling clinical time series data before introducing Waylay as an alternative approach.

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Remote Patient & Elderly Care
Monitoring
Veselin Pizurica, CTO
AI Session #6 Biotech & Pharma THU 5 November - Online session

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How is COVID changing the perspective?

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Remote patient monitoring

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Sepsis Watch - Duke University
https://physicians.dukehealth.org/articles/dukes-augmented-intelligence-system-helps-prevent-sepsis-ed
Following a successful pilot phase from November 2018 through June 2019, Sepsis
Watch, an augmented intelligence (AI) system developed by Duke, has now been
deployed in the emergency departments (ED) at all three Duke University Health
System hospitals to identify patients in the early stages of developing life-threatening
sepsis. The system signals an AI-computed risk of sepsis so that care teams can
quickly begin treatment.

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Explainability or not?
Dataset: Data were curated from the local quarternary academic hospital with over 1,000 beds and over
40,000 inpatient admissions per year. In total, the model development and evaluation dataset contained over
32 million data points.
Model explainability was not prioritized, because regulations promote standardized treatment of sepsis,
regardless of cause. As previously described, human experts often disagree about sepsis diagnoses and major
medical and public health organizations publicly promote distinct disease definitions.
The model generates risk scores every hour for every adult patient to detect sepsis. The model extended prior
work using recurrent neural networks (RNNs) for clinical event detection by coupling a RNN with a multi-task
gaussian processes (MGPs).
Although there are emerging methods to improve explainability of RNNs, end users cannot contemplate the
entire model and cannot reliably understand the relationships between model inputs and outputs. As such,
there was not a deliberate effort to explain the MGP-RNN model output.

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Using DL models in Waylay

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Can we blindly trust DL models?
https://www.nabla.com/blog/gpt-3/

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Do we need human interpretation?
https://aaas.confex.com/aaas/2017/webprogram/Paper19142.html
http://nautil.us/issue/40/learning/is-artificial-intelligence-permanently-inscrutable#disqus_thread

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Sepsis shock diagnosis
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4958885/
https://onlinelibrary.wiley.com/doi/full/10.1111/imj.14199

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Use case: Sepsis Shock
Sepsis is an abnormal inflammatory response to infection and is the leading cause of inpatient mortality within hospitals in
the United States (*). Possible complications include:
● heart failure
● abnormal blood clotting
● kidney failure
● respiratory failure
● stroke
● liver failure
● loss of a portion of the bowel
● loss of portions of the extremities
* Rhee, C. et al. 2017. Incidence and Trends of Sepsis in US Hospitals Using Clinical vs Claims Data, 2009-2014. JAMA. 318, 13 (Oct. 2017), 1241–1249.

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Sepsis Shock as a rule
https://www.mdcalc.com/sirs-sepsis-septic-shock-criteria

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Explainability : Decision trees

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Branching hell - the curse of dimensionality
The depth of the tree grows linearly with the number of variables, but the number of branches grows exponentially with
the number of states. Let’s consider X and Y variables
with three possible outcomes:
As the figure above shows, this leads to 18 leaf nodes (red/green dots) and overall 31 nodes for only two variables! We
have simplified the example by assuming only subset of AND Boolean functions for a given combination. For instance, we
didn't model (X is low) OR (Y is either medium or high) -> as that would be mean connecting some other branches. How
many distinct decision trees do we have with 'n' Boolean attributes? Answer = #number of Boolean functions = #number
of distinct truth tables with 2^n rows = 2^2^n
e.g.: With 6 Boolean attributes we get 18,446,744,073,709,551,616 (2^64 which is the upper limit of an unsigned long)
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https://www.waylay.io/blog/the-curse-of-dimensionality-in-decision-trees-branching-problem/

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Dealing with time and majority voting?
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This is a majority voting problem (impossible to express with decision trees)

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Is there another way? Waylay
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Demo: Sepsis shock monitoring
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Demo setup
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Veselin
Steve
Paul
Piet

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Demo
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