1. No category

Mining the Changes of
Medical Behaviors for
Clinical Pathways
Zhengxing Huang, Chenxi Gan, Huilong Duan
1
Medical behavior changes in CP
INRTODUCTION
2
Process mining methods for CP
3
Changes detection for CP
h"p://www.ppthi-­‐hoo.com
INRTODUCTION
1
Medical behavior changes in CP
p  Medical behavior: medical activity at
specific timestamp
INRTODUCTION
2
Process mining methods for CP
p  How changes happen:
time-linked process, i.e.,
medical evolvements on
3
Changes detection for CP
conditions & traditions
p  What changes indicate:
Needing direction for clinical pathway
improvement, i.e.,
to include what’s new;
to remove what’s unnecessary; minute adjustment.
h"p://www.ppthi-­‐hoo.com
INRTODUCTION
1
Medical behavior changes in CP
p  CANs:
INRTODUCTION
analyze from external perspective, e.g.,
2
Process mining methods for CP
LOS, costs, referral rate;
workflow pattern mining;
model segmentation;
3
Changes detection for CP
variation prediction.
p  CANNOTs:
have insights of medical behavior changes:
discover critical medical behaviors;
discover and analyze medical behavior
changes.
h"p://www.ppthi-­‐hoo.com
INRTODUCTION
1
Medical behavior changes in CP
p  Objective:
For CPs in two different time periods,
INRTODUCTION
to discover medical behavior patterns;
2
Process mining methods for CP
to design similarity measurement indicators;
to detect the significant changes between
patterns in both activities and timestamps.
3
Changes detection for CP
p  Contributions:
effectively handle the mass mount of complex
CP data;
provide detailed information on medical
p  Objective:
behavior
changes;
For CPsreferences
provide
in two different
for clinical
time periods,
experts
to discover medical
scientifically
design behavior
and improve
patterns;
CPs.
h"p://www.ppthi-­‐hoo.com
METHOD
Billedet
kan ikke
vises.
Computere
METHOD
Preprocessing
Billedet kan ikke vises. Computeren har
muligvis ikke hukommelse nok til at
åbne billedet, eller billedet er muligvis
blevet beskadiget. Genstart computeren,
og åbn derefter filen igen. Hvis det røde
x stadig vises, skal du muligvis slette
billedet og indsætte det igen.
Clinical Behavior Record
Change Pa6ern Detec9on：
① Category
② Similarity Measurement
③ Support Change
Measurement
Change Pa6erns
Clinical Event Log
Medical Behavior Pa6erns Mining
Medical Behavior Pa6erns
h"p://www.ppthi-­‐hoo.com
Preprocessing
Change pattern detection
Pattern mining
p  previous work:
Clinical Event Log
Clinical Event
Summarizing clinical pathways from event logs Z Huang , X Lu, , H Duan , W Fan Journal of Biomedical InformaCcs, 46(1): 111–
127, 2013 Pa9ent Trace
p  method basis:
dynamic programming (DP)-­‐based log segmentaCon algorithm; frequent-­‐pa"ern mining methods, such as Apriori (Frequent) Medical and FP-­‐growth
Behavior Pa6ern
Support
Preprocessing
Pattern mining
Change pattern detection
{a a ∈ φ .A  a ∈ φ .A}
sim( φ .A,φ .A ) =
=
φ .A  φ .A φ .A + φ .A — {a a ∈ φ .A  a ∈ φ .A}
φa .A  φb .A
a
a
b
b
a
b
a
b
a
min(t a+ ,t b+ ) — max(t a— ,t b— )
sim( φa .T ,φb .T ) =
max(t ,t ) — min(t
+
a
+
b
—
a
,t
—
b
b
,min(t a+ ,t b+ ) — max(t a— ,t b— ) > 0,
)
0,others
similarity in time domains Preprocessing
Pattern mining
Change pattern detection
Change Pa6ern
Abs.
Implica9on
Perished Pa"ern
PP
pa"erns which have perished
Added Pa"ern
AP
new pa"erns
Unexpected Change
UC
pa"erns that have change in some way
Emerging Pa"ern
EP
pa"erns with high similarity and significant change of support
Preprocessing
Pattern mining
Change pattern detection
Perished
Pattern
SIM<PMTL
Log A Pa6ern set A
SUPP α
SIM
Pa6ern set B
Log B SIM>PMTH
SC>=SCT?
PMTL≤SIM≤PMT
H
SUPP Added
Pattern
Y
Emerging
Pattern
Unexpected
Change
EXPERIMENTS
&
RESULTS
Pattern mining
Crucial clinical activities with represented alphabets
Change pattern detection
Abstraction
a
b
c
d
e
f
g
h
i
j
k
l
m
n
o
p
q
r
s
t
u
v
w
x
ya
yb
z
ct
Clinical activities
Admission
Color ultrasound examination
ECG
Pulmonary function tests
Cardiac color Doppler ultrasound
Catheterization
Venous catheterization
Indwelling urethral catheterization start
Indwelling urethral catheterization complete
Postoperative drainage start
Postoperative drainage complete
Atomizing inhalation
Pleural puncture
Radical surgery of lung cancer
Bronchoscopic treatment
Pleaural effusion B-ultrasound and positioning
Determination of left ventricular function
Configuration of anti-tumor chemotherapy
Infrared treatment
Cleansing enema
Electrolyte
Liver and kidney of sugar
Routine blood test
High-sensitivity CRP
Anesthetic (isoflurane)
Anesthetic (sevoflurane)
Discharge
CT examination
Preprocessing
Preprocessing
Pattern mining
Change pattern detection
Billedet kan ikke vises. Computeren har muligvis ikke hukommelse nok til at åbne billedet, eller billedet er muligvis blevet beskadiget. Genstart computeren, og åbn derefter filen
igen. Hvis det røde x stadig vises, skal du muligvis slette billedet og indsætte det igen.
Frequent medical behavior patterns
minsupp=0.3
p  time stages:
Admission (Ad.) p  time
Pre-­‐OP Days stages:
Admission (Ad.) Pre-­‐OP Days OperaCon (OP) Days Preprocessing
Pattern mining
Change pattern detection
Similarity values on different values of α
Log
No.
α=0
α = .25
α = .5
α =.75
α =1
1
1.000
0.854
0.708
0.562
0.417
2
1.000
0.886
0.773
0.659
0.545
3
0.000
0.019
0.038
0.058
0.077
4
0.933
0.789
0.645
0.501
0.357
2008-
5
0.933
0.789
0.645
0.501
0.357
2009
6
0.389
0.349
0.310
0.270
0.231
(LA)
7
0.389
0.308
0.228
0.150
0.200
8
0.389
0.308
0.228
0.275
0.333
9
0.143
0.157
0.202
0.268
0.333
10
0.143
0.157
0.171
0.186
0.200
11
0.143
0.157
0.171
0.186
0.200
1
1.000
0.854
0.708
0.562
0.417
2
1.000
0.886
0.773
0.659
0.545
3
0.933
0.789
0.645
0.501
0.357
4
0.100
0.158
0.217
0.275
0.333
5
0.143
0.157
0.171
0.214
0.286
2011
(LB)
(A
)
(B
)
Impact of parameter α on SIMa (A), SIMb (B)
Preprocessing
Pattern mining
Change pattern detection
α=0.5, SCT=0.4, PMTL=0.2, PMTH=0.7
Results of change pattern detection
Patterns
Perished pattern
a3, a10, a11
Added pattern
b5
Unexpected change
a4, a5, a6, a7, a8, a9
Emerging pattern
Others
Details for change patterns
Pattern
Period
Mode
Sim.T
Sim.A
Changes
(Abs.)
a3
Pre-OP
PP
0.000
0.077
-
a10
Dis.
PP
0.143
0.200
-
a11
Dis.
PP
0.143
0.200
-
None
b5
Dis.
AP
0.143
0.286
-
a1 ( SC( φa1 , φb1 ) = 0 )
a4
OP
UC
0.933
0.357
ya, u, v, w, x
a2 ( SC( φa 2 , φb1 ) = 0 )
a5
OP
UC
0.933
0.357
ya, u, v, w, x
a6
Post-OP
UC
0.389
0.231
u, v, w, x
a7
Post-OP
UC
0.389
0.200
u, v, w, x
totaled 10 medical behavior change patterns
CONCLUSION
&
DISCUSSION
CONCLUSION
Innovation
p  Specialized analysis for medical behavior changes in CP
p  First employs emerging pattern detection method to CP analysis
p  core methods:
change pa"ern detecCon: similarity measurement & pa6ern divide p  contributions:
accurate detecCon of 4 types of change pa"erns; detailed review of the changes’ degree and direcCon; p  significance:
handle the mass mount of complex CP data; summarize medical experiences; help CP analysis and improvement. Summary
FUTURE WORK
larger data sets & more diseases
NOT only within one institution
• 
between different institutions
to analyze impacts of factors on clinical pathway
execution, such as local environment, healthcare
conditions, medical traditions
• 
between templates and actual patterns for CP adherence check
h"p://www.ppthi-­‐hoo.com
REFERENCES
1. 
Bromberg PM. Shadow and substance: A relational per-spective on clinical process. Psy-choanalytic Psychology
1993: 10(2): 147-168.
2. 
Huang Z, Lu X and Duan H. On mining clinical pathway patterns from medical behaviors. Artificial Intelligence in
Medicine 2012: 35–50.
3. 
Dong G and Li J. Efficient mining of emerging patterns: Discovering trends and differ-ences. In Proceedings of the
fifth International Conference on Knowledge Discovery and Data Mining, San Diego, USA, (SIGKDD 99), 1999:
43-52.
4. 
Huang Z, Lu X, Duan H and Fan W. Summarizing clinical pathways from event logs. Journal of Biomedical
Informatics 2012, accepted.
5. 
Agrawal R and Srikant R. Fast algorithms for mining asso-ciation rules. 1994 International conference on very large
data bases, 1994: 487-499.
6. 
Han J, Pei J and Yin Y. Mining frequent patterns without candidate generation: a frequent-pattern tree approach.
Data Min Knowledge Discovery 2004: 8:53-87.
7. 
Combi C, Gozzi M, Oliboni B, Juarez JM and Marin R. Temporal similarity measures for querying clinical work-flows.
Artificial Intelligence in Medicine 2009: 37-54.
8. 
Peleg M, Mulyar N and Van Der Aalst WMP. Pattern-based analysis of computer-interpretable guidelines: Don't
forget the context. Artificial Intelligence in Medicine 2012: 73-74.
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THANKS !
WELCOME FOR QUESTIONS
Biomedical InformaCcs, ZJU