scoring systems in the intensive care unit and time series ... · scoring systems in the intensive...

Scoring Systems in the Intensive Care Unit and Time Series Preprocessing

Orhan Konak

Data Management for Digital Health

Winter 2018

Where are we?

Data Management for Digital Health, Winter 2018

Use Case Intensive Care

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DataSources

DataFormats

Oncology Nephrology Intensive CareAdditional

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BusinessProcesses

Processingand Analysis

Recap



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ICU

• Patients with life threatening illness

• Supporting failing organ systems

• Highly specialized environment

Equipment

• Organ support equipment

• Intravenous lines, feeding, suction, drains, catheter

• Constant monitoring of bodily functions

Monitoring

• Observing vital signs

• Key to improve patients survival

• Generates data like waveforms

http://telemedicinamorsch.blogspot.com/2018/11/como-colocar-eletrodos-no-paciente-para.html

Patient Data Management System (PDMS)



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■ Document vital parameters sampled by monitors

■ Demands on PDMS have increased immensely

■ PDMS are currently expected to assist clinicians at every level of

intensive care, e.g.

□ Strategic level of physician orders and prescriptions

□ Operational level

□ Administrative level

https://www.getinge.com/de/Produktkatalog/metavision-perfusion/

Does Introduction of a PDMS Improve the ICU?



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■ PDMS implementation costs range from 15k to 20 kEUR per

bed

■ Costs and revenues increased continuously over the years

■ No clear evidence for cost savings after the PDMS introduction

■ PDMS has resulted in better patient outcomes

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3847636/

https://www.imd-soft.com/

PDMSIT Infrastructure



6

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Patient Data Management System

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Scoring Systems in the ICU



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■ Scoring system as clinical decision support

■ Severity scales important to predict

□ Patient outcome,

□ Comparing quality-of-care, and

□ Stratification for clinical trials.

■ Essential part of improvement in clinical decisions and in

identifying patients with unexpected outcomes

■ Using logistic regression models

https://www.digitalhealth.net/2017/02/papworth-hospital-goes-paperless-icu-metavision/

Scoring Systems in the ICU



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■ Scoring system usually comprises of two parts

□ a score (a number assigned to disease severity) and

□ a probability model (equation giving the probability of

hospital death of the patients)

Number

Probability model

Score

P 𝑨

A =10

Types of Scoring SystemsCommonly Used Adult ICU Scoring Systems



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■ First-day scoring systems

□ Acute Physiology and Chronic Health Evaluation (APACHE)

□ Simplified Acute Physiology Score (SAPS)

□ Mortality Prediction Model (MPM)

■ Repetitive scoring systems

□ Organ System Failure (OSF)

□ Sequential Organ Failure Assessment (SOFA)

□ Organ Dysfunction and Infection System (ODIN)

□ Multiple Organ Dysfunction Score (MODS)

□ Logistic Organ Dysfunction (LOD)

http://scoringexpert.pl/2017/01/01/model-scoringowy-troche-teorii/

Severity Scores in Medical & Surgical ICUTimeline



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•APACHE

•SAPS

•APACHE II

1980-85

•SAPS II

•MPM

1986-90•APACHE III

•MODS

•MPM II

•ODIN

1990-95

•SOFA

•CIS

1996-2000•SAPS III

•APACHE IV

2000-current

Glasgow Coma Score (GCS)Let’s Take a Closer Look at One Score



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■ Neurological scale

■ Give a reliable and objective way of recording the conscious

■ Initially used to assess a person's level of consciousness after a head

injury

■ Now used by first responders, EMS, nurses, and doctors

■ Part of several ICU scoring systems, including APACHE II, SAPS II, and

SOFA https://nurse.org/articles/glasgow-coma-scale/

GCSCalculation



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Eye Opening Response

•4 Spontaneously

•3 To speech

•2 To pain

•1 No response

Verbal Response

•5 Oriented to time, person and place

•4 Confused

•3 Inappropriate words

•2 Incomprehensible sounds

•1 No response

Motor Response

•6 Obeys command

•5 Moves to localized pain

•4 Flex to withdraw from pain

•3 Abnormal flexion

•2 Abnormal extension

•1 No response

Behavior Response

Total Score

Mild 13 – 15

Moderate 9 – 12

Severe 3 – 8

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GCSCalculation



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https://codehealth.io/library/article-1/glasgow-coma-scale/

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GCSExample 1



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■ Infant, moves spontaneously towards objects and follows them, smiling and orienting

towards interesting sounds. The infant opens the eyes spontaneously.

E V M

4 5 6 15 https://www.thompsons-scotland.co.uk/serious-head-and-brain-injury/brain-injury-solicitors-scotland/brain-injury-claims-and-the-glasgow-coma-scale

GCSExample 2



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■ Adult, moves the hand away when applying pressure on the nail bed. The patient can make

words but not form sentences. The patient opens the eyes to pain, but not to speech.

E V M


GCSExample 3



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■ Adult, moves hand towards head when applying pressure above the eye socket. The patient

is disoriented but able to form sentences. The patient opens the eyes in response to speech.

E V M


Acute Physiology And Chronic Health Evaluation II (APACHE II)Calculation - Patient’s Age and 12 Routine Physiological Measurements



17http://www.scymed.com/en/smnxpw/pwfbd770.htm

AaDO2 or PaO2

(depending on FiO2)

Temperature (rectal)

Mean arterial pressure

pH arterial Heart rate

Respiratory rate

Sodium (serum)

Potassium (serum)

Creatinine

Hematocrit

White blood cell count

Glasgow Coma Scale

Comparison of ICU Scoring



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ICU Scoring System Timing of data collected

Physiological values

Other required data Total data elements required

Original reported mortality prediction performance

SAPS III Prior to and within 1 hour of ICU admission

10 Age, six chronic health variables, ICU admission diagnosis, ICU admission source, LOS prior to ICU admission, emergency surgery, infection on admission, four variables for surgery type

26 AUC = 84.8% (n=16,784)

APACHE IV First ICU day (16-32 h depending on time of admission)

17 Age, six chronic health variables, ICU admission diagnosis, ICU admission source, LOS prior to ICU admission, emergency surgery, thrombolytic therapy, Fio2, mechanical ventilation

32 AUC = 88.0% (n=52,647)

MPM III Prior to and within 1 hour of ICU admission

3 Age, three chronic health variables, five acute diagnosis variables, admission type (e.g., medical-surgical) and emergency surgery, CPR within 1 h of ICU admission, mechanical ventilation, code status

16 AUC = 82.3% (n=50,307)

https://www.researchgate.net/figure/A-comparison-of-intensive-care-unit-ICU-scoring-systems-from-47-with-permission_tbl1_273059579

Which Score to Use?



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■ APACHE, SAPS, MPM → only of historic significance

■ APACHE II →most widely used in USA

■ SAPS II → commonly used in Europe

■ APACHE III → not in public domain

■ SAPS III, APACHE IV → better design

■ MODS, LODS → uncommonly used ?

The Ideal Scoring System



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■ On the basis of easily/routinely recordable variables

■ Well calibrated

■ Applicable to all patient populations

■ Can be used in different countries

■ The ability to predict functional status or quality of life after ICU discharge

No scoring system currently incorporates all these features

Taking Off the Physician’s Glasses



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Everything’s kind of blurry!

But I'm seeing things very clearly!

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https://www.brillenecke.eu/sitemap/

Selected Data SourceElectrocardiography (ECG)



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Hospital Intensive Care Unit Monitoring

ECG Signal

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https://en.wikipedia.org/wiki/Electrocardiography

Function of ECG



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https://en.wikipedia.org/wiki/Electrocardiography

■ Process of recording the electrical activity of the heart

■ Electrodes placed over the skin

■ Electrodes detect the tiny electrical changes on the skin

■ Commonly performed to detect any cardiac problems

https://www.philips.de/healthcare/product/

Simplified ECG Signal Chain



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Amplifier FilterAnalog-to-digital

converter

https://de.banggood.com/AD8232-Measurement-Pulse-Heart-Monitoring-Hearbeat-Sensor-Module-for-Arduino-Monitor-Devices-p-1402651.html?akmClientCountry=DE&gmcCountry=DE&currency=EUR&createTmp=1&utm_source=googleshopping&utm_medium=cpc_bgcs&utm_content=zouzou&utm_campaign=pla-deg-label3-0-30-pc&ad_id=324664290179&cur_warehouse=CN

https://www.boehm-elektromedizin-gmbh.de/shop/neue-produkte/philips-intellivue-mx550/

Amplifier



25https://www.adinstruments.com/tips/data-quality

■ Electronic device that increases the power of a signal. It does this by taking energy

from a power supply and controlling the output to match the input signal shape but

with a larger amplitude

https://www.conrad.de/de/conrad-components-stereo-verstaerker-bausatz-9-vdc-12-vdc-18-vdc-20-w-2-115592.html

Electronic Filter



26https://www.allaboutcircuits.com/technical-articles/an-introduction-to-filters/

■ Circuits which perform signal processing functions

■ Remove unwanted frequency components from the signal

■ Include the low-pass filter, the high-pass filter, the band-pass filter, and

the notch filter (or the band-reject or band-stop filter)

https://de.banggood.com/MAX262-Programmable-Filter-Bandpass-Band-Resistant-All-Pass-Low-Pass-High-Pass-p-1382154.html?akmClientCountry=DE&gmcCountry=DE&currency=EUR&createTmp=1&utm_source=googleshopping&utm_medium=cpc_bgcs&utm_content=zouzou&utm_campaign=pla-deg-ele-pc&cur_warehouse=CN

Analog-to-Digital Converter (ADC)



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http://qqtrading.com.my/electrocardiogram-sensor-ecg-heart-rate-monitor-ad8232

■ Converts an analog voltage to a digital number

■ Converts the output data into a series of digital values by

approximating the signal with fixed precision

■ Detecting binary signals: is the button pressed or not? These

are digital signals

■ Converts voltage as a binary – 0 or 1

Sampling RateNyquist–Shannon Sampling Theorem



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https://www.adinstruments.com/tips/data-quality

■ The minimum rate at which digital

sampling can accurately record an analog

signal is known as the Nyquist Frequency,

which is double the highest expected

signal frequency

■ Nyquist frequency = 2 x highest expected

frequency

■ E.g. you are recording ECG in humans that

has components which can reach up to 50

Hz as their highest expected frequency, so

the minimum sampling rate should be 100

Hz

Data Acquisition



29http://www.buykorea.org/product-details/SimDAQ-KIT--Biosignal-DAQ-8-channel-24bit-ADC-Isolation-DAQ-USB-DAQ--3107900.html

■ Biological signals recorded via a data acquisition unit (DAQ)

■ Converted to a digital signal by DAQ unit

■ Resulting digital signal is sampled at regular intervals by analysis

software

■ Data stored and displayed on computer

What is a Time Series?



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■ A time series is a collection of observations made sequentially in time

15:51:00 1 25.175015:51:01 1 25.225015:51:02 2 25.250015:51:03 3 25.250015:51:04 4 25.275015:51:05 5 25.325015:51:06 6 25.350015:51:07 7 25.350015:51:08 8 25.350015:51:09 9 25.3500

…

…

15:52:40 100 24.750015:52:41 101 24.755015:52:42 102 24.760015:52:43 103 24.770015:52:44 104 24.760015:52:45 105 24.5500

http://didawiki.cli.di.unipi.it/lib/exe/fetch.php/dm/time_series_2017.pdf

Time

Time series ID

Value

Time Series?

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Time Series are Ubiquitous!



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■ Everything which is on the monitoring

screen (ECG, BP, RR, …)

■ Angela Merkel’s popularity rating

■ The weather in Berlin

■ German Stock Index DAX

People measure things …

… and things change over timehtt

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Time Series



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■ 1 hour of ECG data: 1 GB

■ Typical weblog: 5 GB per week

■ Space Shuttle database: 200 GB and growing

■ Since most of the data lives on disk (or tape), we need

a representation of the data we can efficiently

manipulate

Why is working with time series so difficult?

How do we work with very large databases?

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■ Time-series data applications are proliferating

■ As a result time-series databases are in fashion

■ Most adopt a NoSQL model

■ Developers preferred NoSQL to relational databases

for time-series data by over 2:1

■ Reason for adopting NoSQL time-series databases

comes down to scale

Time Series Database



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https://www.percona.com/blog/2017/02/10/percona-blog-poll-database-engine-using-store-time-series-data/

https://misfra.me/2016/04/09/tsdb-list/

Time Series



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■ Differing data formats

■ Differing sampling rates

■ Noise, missing values, etc.

Why is working with time series so difficult?

Miscellaneous data handling problems

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Time SeriesLet’s Compare ECG Signals



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I’m comparing the curves and

try to find similarities, respectively

abnormalities.

What are you doing there?

Let me show you how to do it.

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Euclidean Distance MetricComparing to Time Series



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■ Let’s assume we want to compare two time series

About 80% of published work in data mining uses

Euclidean distance

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Preprocessing the Data Before Distance Calculations



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■ 4 most common distortions

□ Offset Translation

□ Amplitude Scaling

□ Linear Trends

□ Noise

Euclidean distance is very sensitive to some “distortions”

in the data. For most problems

these distortions are not meaningful → should remove

them

If we naively try to measure the distance between two

“raw” time series, we may get very unintuitive results

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Preprocessing the DataOffset Translation



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Preprocessing the DataAmplitude Scaling



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■ Zero-mean

■ Unit-variance

■ Widely used for normalization in many machine learning algorithms


Preprocessing the DataOffset Translation



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■ Removing linear trend:

□ Fit the best fitting straight line to the

time series, then

□ subtract that line from the time

■ Remove linear trend

■ Removed offset translation

■ Removed amplitude scaling


Preprocessing the DataNoise



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The intuition behind removing noise is

…

Average each data points value with

its neighborshttp://didawiki.cli.di.unipi.it/lib/exe/fetch.php/dm/time_series_2017.pdf

Software Filter



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Low Pass Filter High Pass Filter

https://www.adinstruments.com/tips/data-quality

■ Fourier showed that any periodic signal s(t) can be written as a sum of sine waves with

various amplitudes, frequencies and phases

■ For example, the Fourier expansion of a square wave can be written as

Discrete Fourier Transform



43http://mriquestions.com/fourier-transform-ft.html




44

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Fourier series in 1822




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■ Important signal processing tool

■ Used to decompose a signal into its sine and cosine

components

■ Output of the transformation represents the signal in the

Fourier or frequency domain

■ Apply mathematical operations to eliminate certain

frequency domains very easily

■ Applying the inverse Fourier transform to recover the

original time signal

https://slideplayer.com/slide/4173668/

Summary of Preprocessing



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The “raw” time series may have distortions which we

should remove before clustering, classification

etc.

Of course, sometimes the distortions are the most interesting thing about the data, the above is only a general

rule

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What Do We Want to Do with the Time Series Data?



47http://didawiki.cli.di.unipi.it/lib/exe/fetch.php/dm/time_series_2017.pdfhttp://amid.fish/anomaly-detection-with-k-means-clustering

What to take home?



48

■ Patient Data Management Systems are currently expected to assist clinicians at every level

of intensive care

■ Currently, decision support via ICU scoring systems

■ Data is generated by sensors

■ Preprocessing of time series

■ Chance to improve decision support with the help of machine learning

scoring systems in the intensive care unit and time series ... · scoring systems in the intensive...

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