K. Gibert
K. Gibert(1) A. García-Rudolph (2) et alt
Knowledge Engineering and Machine Learning group
Universitat Politècnica de Catalunya, Barcelona, SPAIN
(1)Department of Statistics and Operation Research
22nd International Congress of the European Federation for Medical Informatics, Sarajevo, August 31st, 2009
(2) Institut Guttmann, Hospital de Neurorehabilitació, Badalona, SPAIN
K. Gibert
Outline 1.- Introduction
ISD, KDD, AI and STATS, KLASS
3.- Context of the Research
Institute Guttmann
Rehabilitation
Spinal Cord Injury
4.- Application
Long-term quality of life perception
5.- Conclusions, future work and impact of the research
5.- Methodological overview
Clustering based on rules (by States)
Class panel graph, Traffic Light Panel
Trajectories Diagram
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Our Research Applied approach (real domains)
Ill-structured domains (ISD) [Gibert 94]
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D
John
Data Sex Eyes Weight Height
Numerical Heterogeneous data
Additional Knowledge on domain structure
Partial knowledge
Ill-structured domains [AIComm94]
D?
Categorical
Heterogeneous
John 85 1.85 ... ... . .
J 85 1.85 M azul . .
.
. . .
.
.
K. Gibert
Solving problems of knowledge discovery on ISD
to support complex DECISION-MAKING
Applied approach (real domains) Ill-structured domains (ISD) [AIComm 94]
Multidisciplinar
approach
K. Gibert
Include interaction with expert as part of the methodology itself
K. Gibert
Our Research
Design of hybrid methodologies in the AI & Stats field
Applied approach (real domains) Ill-structured domains (ISD) [Gibert 94]
Solving problems of knowledge discovery on ISD
K. Gibert
Building hybrid Systems mainly oriented to KDD using Clustering as main Data Mining tool
Applied approach (real domains) Ill-structured domains (ISD) [Gibert 94]
Solving problems of knowledge discovery on ISD
Design of hybrid methodologies in the AI & Stats field
Focus on prior knowledge exploitation Support for implicit knowledge elicitation Focus on interpretation support tools Post-processing discovered knowledge
Distinguishable groups of
homogeneous objects
(patients)
K. Gibert
Collaboration between our research group and
Institute Guttmann neurorehabilitation hospital (Barcelona, SPAIN)
First hospital in Spain for neurorehabilitation (from 1965) The referral center in Catalonia Interdisciplinary team work: more than 400 professionals Pioneer in electronic health record building and maintenance
14.000 patients treated in 40 years (4000 --1000 new-- per year) New cases in 2008
(2800 new cases in Spain )
ACQUIRED BRAIN DAMAGE
539
SPINAL CORD INJURY
244
(1200 new cases in Spain ) OTHERS
214
cerebral palsy Multiple sclerosis Post-polio syndrome
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1 - Hospital phase medical and surgery treatment and comprehensive rehabilitation
2 - Follow-up phase prevention and
treatment of complications
Periodic Integral Evaluation (PIE)
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Develop a proper AI&Stats methodology for discover typical patterns of
– quality of life perception over time QoL Multidimensional construct
emotional wellness (IBP) functional autonomy (CIF) social inclusion (ESIG)
– of spinal cord injury patients – taking into account prior expert knowledge
Apply methodology to 109 paraplejic and tetraplejic patients followed at Institute Guttmann – after clinical discharge (2002-2008) – Using available data from 3 consecutive annual PIEs collected in EHR
IBP (6 variables) (Emotional Wellness) CIF (7 variables) (International Classification of Functioning) ESIG (19 variables) (social scale from Guttmann Institute)
K. Gibert
Involves interaction
with expert
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KLASS Knowledge
Base
e1 τ e2 τ eE τ …
e1 e2
Pe2 PeE
Pe1
… eE
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r1: If [wellness high] AND [depression low] AND [sphinter є {INDEP, INDEP WITH DEVICES}] AND [self-cleaning є {INDEP, INDEP WITH DEVICES}] AutonomosPos
r2: If [wellness low] AND [depression high] AND [vejiga є {INDEP, INDEP WITH DEVICES}] AND [higiene є {INDEP, INDEP WITH DEVICES}] AutonomosNeg
r3: If [higher dressing є {complete dependency}] AND [bed-chair transfer є {complete dependency}] Dependent
crossed by evolution time of lesion (years) {1-2, 3-5, 6-9, 10-15, 16-30, >30}]
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Use the KB to find the Rules Induced Partition
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Hierarchically cluster every
Rules-induced class
Find Rules-induced
prototypes
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Hierarchically cluster new dataset
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Hierarchically cluster new dataset
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Retrieve hierarchical
Structures of Rules-
induced prototypes
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Iterpretation tools: CPGs Psycho Emotional test [NNW05]
Classes
C1 C2 C3 C4
V1 V2 … V5 V6 Variables :
Histogram
of V1|C1
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Show class
particularities
Highest levels of
selfcontrol
Lowest levels of
wellbeing
Highest levels of health
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HIGH
LOW
MEDIUM
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Traffic lights panel [AIM08] for Emotional assessment variables
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TLP supports expert conceptualization
?
Emotionally Negative
Class labelling
Repeat also with other packs
of variables
(functionality, social inclusion)
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KNOWLEDGE DISCOVERY: New Domain Model
Most negative
Positive
Most positive
Negative
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1st Assessment
Allows representation of patients’ TRAJECTORIES over time
2nd Assessment 3rd Assessment
IndepModAnt C49
IndepPos C55
Dependents C54
DepEstoics
C64
IndepPositius
C63
SemiDepNeg C46
IndepModerat C62
SemidepHetero C56
DepEstoics
C52
IndepPos
C59
IndepMod C57
Results: Experts’ interpreted and labeled classes for every PIE
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• Find all different trajectories
*Estimate probability p frequentists approach No markov process assumptions
* Select a threshold (γ)
* Select trajectories p > γ
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1st Assessment
T6
T12
T4
T7
TRAJECTORIES
2nd Assessment 3rd Assessment
IndepModAnt C49
IndepPos C55
Dependents C54
DepEstoics
C64
IndepPositius
C63
SemiDepNeg C46
IndepModerat C62
SemidepHetero C56
DepEstoics
C52
IndepPos
C59
IndepMod C57
Results: More typical patterns (γ≥0.05)
K. Gibert
T6
T12
T4
T7
TRAJECTORIES
VIP2 VIP3
IndepModAnt C49
IndepPositius C55
Depenents C54
DepEstoics
C64
IndepPositius
C63
SemiDepNeg C46
IndepModerat C62
SemidepHetero C56
DepEstoics
C52
IndepPositius
C59
IndepMod C57
Expert’s conceptualization of patterns Physical autonomy and psychological
wellness maintained over time 1st Assessment
High impairment. Starting with different
coping strategies. Long term adaptation to
moderate distress, no anxiety Beginning: Functional autonomy, some
distress. Health problems appear with time and loose functionality. Different coping
strategies. Old people, old lesion.
Younger. Recent lesion. Some
distress. Physical autonomy.
They keep stable
K. Gibert
Involves interaction
with expert
K. Gibert
KDD useful complement to partial prior expert knowledge
Hybrid AI&Stats methodologies allows KDD in complex medical domains
ClBR and ClBRxE resulted useful tools for KDD
Interpretation-oriented tools crucial for understandable results – (CPG, TLP and Trajectories Diagram good support interpretation tools)
Expert should be integrated as part of the methodology itself
KDD helps elicitation of implicit expert knowledge
Patients evolving to decreasing QoL were identified
Particularities of those patients were analyzed: Distress is not only related with loose of functionality but with coping, personality, social support perception, resources, dysfunctional couple, incomplete or medical lesions, pain
Hospital is now plannig support and preventive actions to keep QoL
K. Gibert
MIE 2009 Sarajevo, 30th Aug-2nd Sep 2009
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