Upload
ethan-merza-ectole
View
227
Download
0
Embed Size (px)
Citation preview
8/12/2019 Lecture13 Monitoring
1/30
HLTHINFO 730Lecture 13 Slide #1
HLTHINFO 730Healthcare Decision Support Systems
Lecture 13: Monitoring
Lecturer: Prof Jim Warren
http://images.google.co.nz/imgres?imgurl=http://www.bodytype.com/images/thyroid_icon.gif&imgrefurl=http://www.bodytype.com/bodytype/thyroid.html&h=80&w=115&sz=4&hl=en&start=16&um=1&tbnid=ENJ0pl30cFFI0M:&tbnh=61&tbnw=87&prev=/images%3Fq%3Dgears%2Bicon%26svnum%3D10%26um%3D1%26hl%3Den%26rlz%3D1T4ADBR_enNZ231NZ2348/12/2019 Lecture13 Monitoring
2/30
HLTHINFO 730Lecture 13 Slide #2
Monitoring
A few different domains Critical care monitoringreporting back to humans
who will respond quickly
Ubiquitous monitoring getting data (probably over
a long period of time) without being too obvious aboutit
Participatory monitoringpatients get a sense ofengagement by participating in the medical record
Coaching the interaction is mostly about
encouraging healthy behaviour
8/12/2019 Lecture13 Monitoring
3/30
HLTHINFO 730Lecture 13 Slide #3
Critical care systems
Classic app is ECG monitoring
See http://www.nda.ox.ac.uk/wfsa/html/u11/u1105_01.htm
P - R interval
QRS complex
duration
Q - T intervalcorrected for heart
rate (QTc) QTc =
QT/ RR interval
0.12 - 0.2 seconds
(3-5 small squares
of standard ECGpaper)
less than or equal
to 0.1 seconds (2.5
small squares)
less than or equalto 0.44 seconds
8/12/2019 Lecture13 Monitoring
4/30
HLTHINFO 730Lecture 13 Slide #4
Another view of the ECG
One
heart-
beat
Particularly want to look out for lengthening Q-T
8/12/2019 Lecture13 Monitoring
5/30
HLTHINFO 730Lecture 13 Slide #5
Amplitude, Frequency, Phase
Amplitude is displacement (adistance) in a physical
vibration and then is usually
transformed to an electric
current and is measured in
voltage
http://upload.wikimedia.org/wikipedia/commons/6/64/Phase_shift.pnghttp://en.wikipedia.org/wiki/Image:Simple_harmonic_motion.png8/12/2019 Lecture13 Monitoring
6/30
HLTHINFO 730Lecture 13 Slide #6
AM / FM
Can encode signals by changing (modulating)amplitude or frequency (or phase) of a carrier signal
8/12/2019 Lecture13 Monitoring
7/30
HLTHINFO 730Lecture 13 Slide #7
Basics of signal processing
Sampling frequency Must take samples frequently enough
The Nyquistrateistwice thefrequency ofthe highestfrequencycomponentof the signal
If theres something higher frequency, then youll getaliasingan incorrect interpretation of the signal
8/12/2019 Lecture13 Monitoring
8/30
8/12/2019 Lecture13 Monitoring
9/30
HLTHINFO 730Lecture 13 Slide #9
Signal classification
Algorithms can classify signals based on featuresof thesignal Might be straightforward (e.g., time between lowest and highest
amplitudebut keep in mind all those sampling errors!)
Signal can be mathematically transformed
Fourier transformtransforms from amplitude over time ->amplitude over frequency
We can then extract features from the transformed signal
Classifiers can then use whatever machine learningmethods Multiple regression, artificial neural networks, induced decision
trees, etc. Can classify the system (e.g., the patients heart) as being in
any of a variety of states
And you can layer symbolic reasoning (production rules) andfuzzy logic on top of the signal-feature-based classifiers
8/12/2019 Lecture13 Monitoring
10/30
HLTHINFO 730Lecture 13 Slide #10
Fourier transform results
A sine wave is the pure spike once Fourier
transformed
Square waves
and pulsesmake more
complex
patterns
Time domain Frequency domain
8/12/2019 Lecture13 Monitoring
11/30
HLTHINFO 730Lecture 13 Slide #11
Markov model
Based on the memoryless (or Markov) property(M either way!)
Your previous states say nothing; only need to think
about current state and probability/rate of progression
to other states from there
e.g., P(Bt+1| At) = 0.9
Can describe the system with a square
matrix, NxN, where N is the number of
states
Again, only accurate if the system is
memoryless with respect to those states
Can use a series of low probability
transitions to indicate that the system
has changed (and throw an alert)
8/12/2019 Lecture13 Monitoring
12/30
HLTHINFO 730Lecture 13 Slide #12
Applications
ICU (esp. PICU) monitoring
Respiration, blood glucose, etc.classify and
alert on changes
Worn heart monitors http://www.nlm.nih.gov/medlineplus/news/fullstory_64123.html
Also, worn accelerometers for falls detection
Smart homes Monitor usage patterns of lights, water,
refrigerator etc. and also track motion
8/12/2019 Lecture13 Monitoring
13/30
HLTHINFO 730Lecture 13 Slide #13
8/12/2019 Lecture13 Monitoring
14/30
HLTHINFO 730Lecture 13 Slide #14
Discussion
Have you experienced any good (or not so
good) automated monitors?
8/12/2019 Lecture13 Monitoring
15/30
HLTHINFO 730Lecture 13 Slide #15
Participatory Home Telemedcare
Home ECG, lung function, blood
oxygen saturation, glucose, weight, BP All with feedback so patient sees their
state and their progress
Can, for instance, learn to deal with anasthma attack (possibly on phone tonurse) without called ambulance
8/12/2019 Lecture13 Monitoring
16/30
HLTHINFO 730Lecture 13 Slide #16
Reminders, life coaches
STOMPtxt messaging to quite smoking chewing gum for the fingers automated friend to
txt whencraving
Plus stagedsupportive
messagesandmonitoring
Significantquit effect(Maori and
non-Maoriat 6 months
Other obvious apps are exercise coaches, drug administrationreminders and (esp. w. video phones) guides (e.g., for insulin dosingor nebulizer spacer technique)
8/12/2019 Lecture13 Monitoring
17/30
HLTHINFO 730Lecture 13 Slide #17
What is a care plan anyway?
Fundamental to monitoring or health
promotion should be the notion of the care
planfor a patient
What are our objectives (specified as goals
and target values)?
What interventions do we have in place to
achieve those objectives? How often do we monitor status?
When do we plan to re-plan?
8/12/2019 Lecture13 Monitoring
18/30
HLTHINFO 730Lecture 13 Slide #18
Care plan model
Weve created an information model for
care plans (Khambati, Warren, Grundy and Hosking)
8/12/2019 Lecture13 Monitoring
19/30
HLTHINFO 730Lecture 13 Slide #19
Model (contd.)
8/12/2019 Lecture13 Monitoring
20/30
HLTHINFO 730Lecture 13 Slide #20
Designing a care plan in the
model
8/12/2019 Lecture13 Monitoring
21/30
HLTHINFO 730Lecture 13 Slide #21
Care plan in the model (contd.)
8/12/2019 Lecture13 Monitoring
22/30
HLTHINFO 730Lecture 13 Slide #22
Automated interface
generation
Weve prototyped a
process for generating
multiple user interface
implementations for anindividual care plan
around the care plan
model
Model a care plan
using the care plan
visual language
Guideline Implementer
Instantiate the care plan
template for a patient
Provider (e.g., GP)
Care Plan Template
Care Plan Instance
Model a suitable visual-
isation for representing a
care plan on a specific
device
User Interface
Programmer
Care Plan Visual-
isation Definition
Generate an application
for a user to visualise a
care plan instance
Visualisation Generator
OpenLaszlo script
representing end-user application
Create runnable
application
OpenLaszlo Compiler
Shockwave Flash
ObjectsDHTML
E l i t f
8/12/2019 Lecture13 Monitoring
23/30
HLTHINFO 730Lecture 13 Slide #23
Example interfaces
Part of a diabetes monitoring care plan being tailored in
our care plan instantiation application
8/12/2019 Lecture13 Monitoring
24/30
HLTHINFO 730Lecture 13 Slide #24
Example interfaces
End-user Flash application compiled from OpenLaszlo
Auto-generated interfaces are still a bitbasic, but better than nothing
8/12/2019 Lecture13 Monitoring
25/30
HLTHINFO 730Lecture 13 Slide #25
Your plastic pal thats fun to be with
Healthcare robots (or healthbots) are being
considered to supplement human personnel
Particularly in low-intensity monitoring situations such
as aged care Robot is from a Czech word for to work
But many practical robots are actually more focused on being
mobile sensor platforms and computer terminals
Real work robots are possible when fixed to an automotive
assembly line, but not yet practical for dealing with people
Which doesnt mean the Japanese arent trying
8/12/2019 Lecture13 Monitoring
26/30
HLTHINFO 730Lecture 13 Slide #26
Robots that can lift and carry
Japanese
RI-MAN
(incidentally,
thats a doll
its lifting) still highly
experimental
8/12/2019 Lecture13 Monitoring
27/30
HLTHINFO 730Lecture 13 Slide #27
Tele-presence healthbot
Much more common
and further along
toward real-world use
8/12/2019 Lecture13 Monitoring
28/30
HLTHINFO 730Lecture 13 Slide #28
Robots for companionship
Gladys Moore, a resident at theNHC Healthcare assisted-livingfacility in Maryland Heights,Missouri, plays with AIBO, a
robotic dog, in this undatedhandout photo. Researchersfound that the robot dog wasabout as good as a real dog ateasing the loneliness of
nursing home residents in astudy.
8/12/2019 Lecture13 Monitoring
29/30
HLTHINFO 730Lecture 13 Slide #29
UoA Health Robotics Centre
Working with ETRI (Korean Robotics Institute) Looking at adapting an inexpensive
robot for elder care
Combination of companion-
ship and monitoringcapabilities
Strong emphasis on speechinteraction
More autonomous adjunct to
human healthcare workers, ratherthan for tele-presence
Possibly supplement othersmart home equipment
Ultrasonic sensors to avoid bumping into
things
http://techdigest.tv/iRobiQ1.jpg8/12/2019 Lecture13 Monitoring
30/30
HLTHINFO 730 Lecture 13 Slide #30
Summary
Monitoring is a major class of health IT activity
It leads to the embedding of sometimes non-trivial
artificial intelligence in devices (often with reliance
on traditional signal processing) Monitors may be overt or ubiquitous
They may engage the consumer
In fact, engaging the consumer may be the main point! Monitoring implies the knowledge engineering of
guidelines