Lecture13 Monitoring

8/12/2019 Lecture13 Monitoring

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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_enNZ231NZ234


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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


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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


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Another view of the ECG

One

heart-

beat

Particularly want to look out for lengthening Q-T


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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.png


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AM / FM

Can encode signals by changing (modulating)amplitude or frequency (or phase) of a carrier signal


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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


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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


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Fourier transform results

A sine wave is the pure spike once Fourier

transformed

Square waves

and pulsesmake more

complex

patterns

Time domain Frequency domain


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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)


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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


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Discussion

Have you experienced any good (or not so

good) automated monitors?


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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


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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)


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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?


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Care plan model

Weve created an information model for

care plans (Khambati, Warren, Grundy and Hosking)


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Model (contd.)


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Designing a care plan in the

model


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Care plan in the model (contd.)


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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


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Example interfaces

Part of a diabetes monitoring care plan being tailored in

our care plan instantiation application


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Example interfaces

End-user Flash application compiled from OpenLaszlo

Auto-generated interfaces are still a bitbasic, but better than nothing


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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


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Robots that can lift and carry

Japanese

RI-MAN

(incidentally,

thats a doll

its lifting) still highly

experimental


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Tele-presence healthbot

Much more common

and further along

toward real-world use


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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.


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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.jpg


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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

Lecture13 Monitoring

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