Συστήματα στήριξης απόφασης για
την ανίχνευση συμπτωμάτων της
κατάθλιψης
Decision Support System approach
2
Trend Analysis - Goals
• Recognition of deterioration / improvement patterns based on past values
• Step 1: Baseline estimation – based on norms or on individual data
• Step 2: Identify trends away from the norm – identifying incidents vs
Identifying longer-term trend changes
• Design considerations:
• Detect variations in the presence of outliers
• Select of appropriate statistical measures that :
• Yield robust control limits
• Provide accurate determination of trends away from the norm
• Different approach-time window to be followed regarding the
nature/importance of changing trends for each health
indicator/parameter, e.g. weight change for patients facing risk of CHF,
sleep patterns require longer-term trend analysis for more meaningful
results
• Forecasting/prediction of future deterioration for alert production
Recognition of deterioration /
improvement patterns
Trend Analysis
Sleep linear
deterioration
Insomnia Hypersomnia
Forecasting of future deterioration for
alert production
Possible Medical Alarm
delivered to the expert
FUZZY COGNITIVE MAPS
• Are designed by experts through an interactive procedure of
knowledge acquisition
• Learning procedures can be combined to adapt structure of the FCM
• Abstract modelling methodology to model and represent the
behaviour of a system
• Concepts stand for observables/indications (e.g. vertigo, allergies at
several substances), conditions (e.g. hemiparesis), therapies
(pharmaceutical or interventional)
• Interconnected concepts with weighted arcs representing causal
reasoning
• Fuzzy Graph Structure: human knowledge on the system is reflected in
the methodology for developing FCM
FUZZY COGNITIVE MAPS




Nodes-concepts = key factors of the system (input, output,
intermediate)
Weighted arcs = causal relations among the nodes
Experts -> Number & type of
concepts, Initial weights.
Convergence to a steady state
Strength of causal relationship between two concepts.
Relationship between two concepts can be positive or negative, or
no correlation:
•Degree of relationship between two concepts.
Numerical values of weights belong to the interval [-1,1]
inference
N
Ai (k  1)  f( (2 Ai (k )  1)   (2 Aj (k )  1) W ji )
j i
j 1
Construction of Fuzzy Cognitive Maps
Determination of the initial configuration of the FCM by the experts
Experts are pooled to determine
the relevant factors that will be
represented as concepts
Negative
Positive
No Influence
Strong
Weak
…
Each expert determines the
influence of a concept on
another using linguistic notion
Linguistic weights are assigned
on each arc by each expert
Linguistic variables are combined
and the aggregated linguistic
variable is transformed to a
single linguistic weight
SUM
technique
Defuzzification to obtain a
numerical value in [-1,1] for each
weight
Center of Area
technique
The advantage of this technique
is the avoidance of direct numerical
weights assignment from the experts.
Formalize Expert Knowledge with Fuzzy Cognitive
Maps (FCMs)
• A weight matrix depicting concept cause – effect
relationships
• Relationships could be - negative , positive or zero
• Negative means that the existence of a concept (cause)
reduces the probability of existence another concept
(effect), e.g. existence of apathy reduces the existence
probability of psychomotor agitation
• Positive means the opposite, e.g. depressive mood
(cause) increases the presence of depression (effect)
• Zero means the involved concepts have no cause – effect
relationship
9
Formalize Expert Knowledge with Fuzzy Cognitive
Maps (FCMs)
• Relationships also are characterized by the strength of
their impact
• Fuzzification
• T(influence) = { negatively very very strong,
negatively very strong, negatively strong, negatively
medium, negatively weak, negatively very weak,
zero, positively very weak, positively weak, positively
medium, positively strong, positively very strong,
positively very very strong}
10
Psychomotor
Agitation (C1)
+ {v.weak}
Insomnia
(C5)
+ {strong}
Reduced
Interest
(C4)
Depression
Mood (C3)
Depression
(C19)
11
Effect
Effect
Effect
Effect
Effect
Cause
C1
C3
C4
C5
C19
C1
zero
-med
+med
+high
zero
C3
+v.v.weak
zero
v.strong
strong
strong
C4
zero
v.weak
Zero
+med
strong
C5
v.weak
weak
med
zero
strong
C19
zero
zero
zero
zero
zero
Depression Use Case - Concepts
Concept No
Concept Name
Concept No
Concept Name
C1
Psychomotor Agitation
C10
Poor Interventional
Performance
C2
Psychomotor retardation
C11
Apathy
C3
Depressive mood
C12
Diminished ability to
concentrate
C4
Reduced interest for daily
functioning
C13
Indecisiveness
C5
Insomnia
C14
Extreme self-criticism
C6
Hypersomnia
C15
Chronic muscle problems
C7
Fatigue or loss of energy
C16
Reduced mobility
C8
Feelings of Worthlessness
C17
Reduced autonomous
functioning
C9
Recurrent thoughts of
death
C18
Depression (Outcome
Concept)
12
FCM Depression Model
13
FCM-based therapy suggestion
14
Correlation of Low-Level Events with clinical
assessment tools
FeetElevation
SittingSpeed
StepCount
-.635*
PHQ- 7 (concentration deficits)
PHQ-8 (agitation/retardation)
PHQ-9 (2)
WalkingSpeed
Emotion
SpeechArousal
-.716*
-.515*
.049
.036
.020
-.771**
-.697*
.005
.025
FaceAngry
FaceBlinkingRate
FaceHR
FaceRed
.833*
.592*
-.641*
.020
.042
.025
.669*
-.568*
.813**
PHQ-2 (depressive mood)
.004
.692*
PHQ-4 (loss of energy)
.026
.017
MMSE
Chair Stand Test
-.919**
-.711*
.003
.048
.011
0.0007787
-.774*
.041
.012
.032
Foot Up & Go Test 2
.790**
-.823*
-.645*
2- Minute Step Test
.681*
-.741*
.044
.036
-.819**
SF (MCS)
.004
.640*
.046
ICECAP-O (sum)
-.616*
BOWELS
.025
.a
TOILET USE
TRANSFER
.697*
0.000
.025
.a
.697*
0.000
.025
-.590*
INT4. Current SCRQol
Independence
15
.034
.a
.848**
0.000
.002
0.0271266
-.759*
.616*
-.666*
.015
.018
Scatter plots - 1
16
Scatter plots - 2
17
@HOME PILOTS
18
Unobtrusive system setup
• Five (5) seniors’ homes
• Broadband / 3G mobile network availability
• 1-2 hrs typical installation time
Smart TV
WWU
Charger
Kinect
19
Body Scale
Recruitment
• Five (5) elderly, 75,6±4,72 years old and 14.8±6.57 years of
education , 4/5 memory problems, 2/5 depressive symptoms
• Inclusion criteria
 Living alone
 Either exhibit depressive symptomatology or memory problems
 Mini Mental State Examination (MMSE) greater than 24
 No antidepressants 90 days before the start of the pilot
• Exclusion criteria






Illiterate
Alcoholics or drug addicted
Sensory (visual or auditory) deficits
History of head trauma with loss of consciousness
Recent bereavement
Psychotic or bipolar disorder, according to the Diagnostic and Statistical
Manual of Mental Disorders, 5th edition criterion
 Aberrant motor activity (tremor, rigidity, Parkinsonism) as defined by the
Movement Disorder Society Unified Parkinson Disease Rating Scale
20
© USEFIL Consortium 2011-2014; EU-ICT Collaborative Project
Baseline assessment & Follow Up
• Mini Mental State Examination (MMSE) – cognitive function
• Patient Health Questionnaire (PHQ-9 ) – depression
symptomatology
• SF-12, ICECAP-O, ASCOT INT 4 – Quality of Life Indices
• Barthel Index – functional assessment
• Fullerton Fitness Test – physical ability
• Baseline, one-month follow-up and two-month follow-up
• Usability, satisfaction & unobtrusiveness evaluation, focus group at
end of 2-months
21
© USEFIL Consortium 2011-2014; EU-ICT Collaborative Project
Intelligent Monitoring Analysis & Results
• Long-term analysis of single
sensor low-level events
• Absence of High-Level
events led to the analysis of
uni-modal trends, based on
the findings of lab
experiments
• 2 case studies
• Recognition of possible
depressive symptoms
• Mobility problems and fall
risk assessment
Daily Average of
LLE, e.g.
WalkingSpeed
Identification of
Baseline period
and time period
of interest
Model parameter
as a statistical
process (mean
value,
upper/lower
control limits)
Control charts
Split baseline
time period in
smaller time
windows and
extract statistical
measures
22
© USEFIL Consortium 2011-2014; EU-ICT Collaborative Project
Participant #5 emotional profile
PHQ-1
PHQ-2
(loss
of (depressive
interest)
mood)
P
PH H PHQ-5 (loss of
Q-3 Q apetite)
-4
P
PHQ-7
H
(concentration
Q
deficits)
-6
PH PH
Q- Q8
9
Baseline
27/1/2015
1
2
3
3
3
0
3
0
0
Interim
27/2/2015
3
2
3
3
0
0
1
0
0
Follow up
3
17/3/2015
3
3
3
0
0
3
0
0
23
© USEFIL Consortium 2011-2014; EU-ICT Collaborative Project
Control chart – Participant #5
Step Count (#daily steps)
Walking Speed (m/s)
Follow up
period
Follow up
period
Interim
period
Interim
period
Speech Arousal (abstract units 1-10)
Interim
period
Follow up
period
24
© USEFIL Consortium 2011-2014; EU-ICT Collaborative Project