p - MedUni Wien

Wissensbasierte
Toxoplasmose-Diagnostik
in der Schwangerschaft
Klaus-Peter Adlassnig 1), Dieter Kopecky 1), Michael Hayde 2),
Birgit Panzenböck 2), Arnold Pollak 2)
1) Institut
für Medizinische Computerwissenschaften
Abteilung für Medizinische Experten- und Wissensbasierte Systeme
2) Universitätsklinik für Kinder und Jugendheilkunde
Klin. Abteilung für Neonatologie, angeborene Störungen und Intensivmedizin
Medizinische Fakultät der Universität Wien
Toxopert
• Ziel:
– Automatische Interpretation von Toxoplasmosetests im Zeitverlauf
• Methode:
– Integration des Toxopert in das Routine-EDV-System der
jeweiligen medizinischen Einrichtung
– Implementierung eines Entscheidungsgraphen
– Befundausdruck mit Diagnose- und Therapievorschlag
Oozysten von Toxoplasma gondii
Aus dem Katzenkot. Natürliche Größe 12μm.
(Aus: H. Aspöck, Toxoplasmose, Hoffmann-La Roche Wien, Wien 1992).
Antibody Concentration after
Toxoplasmosis Infection
years
years
specific IgM detection
months
from: J.A.Pinard et al. (2003) Maternal Serologic Screening for Toxoplasmosis. Journal of Midwifery & Women’s
Health 48, 308–316.
Beispiel
berechneter
IgG-Trend
Befund/
Untersuchung
Datum
SSW
IgG
Vorbefund
07.01.1999
10
negativ
1.
05.03.1999
18
negativ
⊥
2.
30.04.1999
26
1:256
↑↑
positiv
3.
27.05.1999
30
1:16 384
↑↑
positiv
IgM
Beispiel
Start
neg
Vorbefund
pos
keine Infektion
1. Untersuchung
IgG≥1:4 und
IgG≤1:256
IgG neg
IgG neg
2. Untersuchung
Infektion
keine Infektion
latente Infektion
IgG≥1:4 und
IgG≤1:256
IgG≥1:1024
Verdacht auf
akute Infektion
3. Untersuchung
↓↓,↓,⊥,↑
latente Infektion oder
widerspr. Befunde
Akute Infektion
↑↑
akute Infektion
IgG≥1:1024
akute Infektion
TOXOPERT-I
• Stand-alone Small Talk-Version
TOXOPERT-Serologie
Evaluierung von TOXOPERT-I
Arzt →
keine
unzureichende oder
Infektion inkonsistente Daten
akut
latent
akut
17
0
0
0
17
latent
0
361
0
7
368
keine Infektion
0
0
606
0
606
unzureichende oder
inkonsistente Daten
0
2
0
7
9
gesamt
17
363
606
14
1000
TOXOPERT-I ↓
gesamt
TOXOPERT-II
• Integrierte Laborversion
Diskussion
• Modellierung des medizinischen Wissens als
Entscheidungsgraph
– zeitliche Abfolge der Befunde implizit enthalten
• Verlaufsbeurteilung bei Abweichung von
Standardverläufen unzureichend
– explizite Darstellung der Zeit erforderlich
• Visualisierung des Inferenzpfades
FuzzyTempTOXOPERT:
Interpretation of Toxoplasmosis
Serology Test Results
Dieter Kopecky 1), Klaus-Peter Adlassnig 1), Michael
Hayde 2), Andrea-Romana Prusa 2), and
Birgit Panzenböck 2)
Department of Medical Computer Sciences
Section on Medical Expert and Knowledge-Based Systems
University of Vienna Medical School, Spitalgasse 23, A-1090 Vienna, Austria
1)
2) Department
of Pediatrics
Division of Neonatology and Intensive Care
University of Vienna Medical School, Währinger Gürtel 18–20
A-1090 Vienna, Austria
General Considerations
•
medical problem
– transplacental transmission of Toxoplasma gondii has to be treated
immediately
* question: Did an acute toxoplasmosis infection occur after
conception?
•
objective
– automatic interpretation of toxoplasmosis antibody test results in their
course of time
* IgG SFT, IgM ISAGA
– automated generation of therapy proposals, if necessary
•
implementation
– decision graph, nonmonotonic reasoning, temporal distances are modelled
by fuzzy sets
– integration into the routine laboratory information system of the
toxoplasmosis laboratory
– web-based interpretive system
Decision Graph
Start
PF = ?
T = [•]
T = [•]
1.01 (I)
T = [•]
GA ≤ 29 ∧ IgM ∈ [−,±] ∧
IgG ∈[1: 4 ,1: 1024]
2.04 (N)
T = [[2,4],•]
GA ≤ 30 ∧
IgG = −
2.01 (N)
T = [[2,4],•]
T = [•]
3.08 (L)
IgG ≥ 1: 4 ∧
[
TT ∈ ⇓, ↓, ⊥, ↑
]
3.03 (A)
IgG ∈ [1: 4,1:1024] ∧
IgM = +
3.08 (L)
Latent (preconceptual) infection with Toxoplasma gondii is confirmed.
The unborn is protected against an infection with Toxoplasma gondii.
Additional serological control of this and future pregnancies is not necessary.
T = [4,•]
T = [4,•]
GA ∈ [31,34] ∧
IgG = −
2.02 (N)
T = [4,•]
GA ≥ 35 ∧
IgG = −
T = [4,•]
T = [4,•]
2.03 (N)
FuzzyTOXOPERT-III
At Least 3 to 5 Weeks
⎧ 0,..................∀t , t ∈ [0,3)
⎪t
⎪
μ temp (t ) = ⎨ − 1.5,..........∀t , t ∈ [3,5]
⎪2
⎪⎩1,...................∀t , t ∈ (5,+∞ ]
temporal
compatibility
μ
1.00
0.75
0.50
0.25
0.00
1
2
3
4
5
6
7
8
9 10 11 12
t in weeks
Within 7 Weeks
⎧1,....................∀t , t ∈ [0,7]
⎪ t
⎪
μ temp (t ) = ⎨− + 4.5........∀t , t ∈ (7,9 )
⎪ 2
⎪⎩0,...................∀t , t ∈ [9,+∞ )
temporal
compatibility
μ
1.00
0.75
0.50
0.25
0.00
1
2
3
4
5
6
7
8
9 10 11 12
t in weeks
Knowledge Base Taking Temporal
Compatibility into Account–I
Interpretation 3.08
[0.0-0.3]
Latent (preconceptual) infection with Toxoplasma gondii is very probable.
[0.3-0.8]
Latent (preconceptual) infection with Toxoplasma gondii is rather certain.
[0.8-1.0]
Latent (preconceptual) infection with Toxoplasma gondii is confirmed. The unborn is protected against
an infection with Toxoplasma gondii.
]0.0-0.3]
Serological followup required at 1-2 weeks.
[0.3-0.8]
Serological followup required at 2-3 weeks.
[0.8-1.0]
Additional serological control of this and future pregnancies is not necessary.
]0.0-0.99]
Since the obligate time interval of @1 weeks has not been kept between the two tests on @2 and @3,
it is possible, that a probable titer rise has not been recognized.
IF
Graph
Start
1.01
2.05
3.08
Prel.Find.
?
Findings
GA
IgG
IgG trend
1
2
3-i
[30,37]
[1:4,1:1024]
≥ 1:4
≥ 1:4
⇓↓⊥↑
⇓↓⊥↑
IgM
±+
Time
•
•
[[2, 4], •]
•
Knowledge Base Taking Temporal
Compatibility into Account–II
OR
Graph
Start
1.01
2.04
3.08
OR
Graph
Start
1.02
2.05
3.08
OR
Graph
Start
1.02
2.15
3.08
OR
Graph
Start
1.02
2.04
3.08
Prel.Find.
?
Prel.Find.
–
Prel.Find.
–
Prel.Find.
–
Findings
GA
IgG
IgG trend
1
2
3-i
≤ 29
[1:4,1:1024]
≥ 1:4
≥ 1:4
⇓↓⊥↑
⇓↓⊥↑
Findings
GA
IgG
IgG trend
1
2
3-i
[30,37]
[1:4,1:1024]
≥ 1:4
≥ 1:4
⇓↓⊥↑
⇓↓⊥↑
Findings
GA
IgG
IgG trend
1
2
3-i
≤ 37
[1:4,1:1024]
≥ 1:4
≥ 1:4
⇓↓⊥↑
⇓↓⊥↑
Findings
GA
IgG
IgG trend
1
2
3-i
≤ 29
[1:4,1:1024]
≥ 1:4
≥ 1:4
⇓↓⊥↑
⇓↓⊥↑
IgM
±+
IgM
–±
IgM
+
IgM
–±
Time
•
•
[[2, 4], •]
•
Time
•
•
[[2, 4], •]
•
Time
•
•
[[2, 4], •]
•
Time
•
•
[[2, 4], •]
•
Diagnostic interpretation, therapy
recommendation, and proposals for
further examinations for a woman
with acute toxoplasmosis.
The second inference cycle tried to
eliminate test results that have
lowered the overall temporal
compatibility.
Discussion on FuzzyToxopert
• modeling of temporal concepts by fuzzy sets
• degrees of applicability of rules < 1 (attributed to decision
paths) result in soft (= less decisive) interpretations
Logikbasierte Repräsentation
CADIAG: Computer-Assisted Diagnosis
• predecessor (1968–1974):
– propositional logic: hepatology, rheumatology
• CADIAG-I (1976–1983, 1990–1992):
– three-valued logic, predicate logic: rheumatology,
gastroenterology, hepatology
• CADIAG-II (1978–present):
– fuzzy set theory, fuzzy logic, compositional rule of inference with
occurrence and confirmability (numerical values): rheumatology,
gastroenterology, hepatology, neurology, nosocomial infections)
• CADIAG-III (1992–1994):
– extension of fuzzy operators (missing values in medicine), fix
point inference
• MedFrame/CADIAG-IV (1986, 1994–present):
– extended data-to-symbol conversion, inference with SDoccurrence, SnotD-occurrence, SD-confirmability, SnotDconfirmability (linguistic values), generalization to symptoms,
diseases, and therapies
Computer-Assisted Medical Diagnosis
and Therapy
• “Reasoning Foundations of Medical Diagnosis” (1959)
in Science by Ledley and Lusted
– symbolic logic
∗ symptom complexes = logic combinations; diagnoses
implicated or excluded
– probability
∗ frequency of symptoms with diseases, frequency of symptoms
and frequency of diseases in a population; most probable
diagnosis
– value theory
∗ decision trees; optimal treatment
Relationships in CADIAG-I
positive association
necessity
sufficiency
– O: obligatory occurring
– F: facultative occurring
– C: confirming
– N: not confirming
OC: obligatory occurring and confirming
FC: facultative occurring and confirming
ON: obligatory occurring and not confirming
FN: facultative occurring and not confirming
exclusion
EX: excluding
Medizinische Relationen in
Prädikatenlogik
•
Relation OB (obligatorisch auftretend und beweisend):
1. alle p mit S haben D
2. alle p mit D haben S
3. es gibt mindestens ein p mit S
∧
S OB D = ∀p[S(p)→D(p)] ∧ ∀p[D(p)→S(p)] ∧ ∃p[S(p)]
•
Relation FB (fakultativ auftretend und beweisend):
1. alle p mit S haben D
2. nicht alle p mit D haben S
3. es gibt mindestens ein p mit S
∧
S FB D = ∀p[S(p)→D(p)] ∧ ∃p[D(p)∧¬S(p)] ∧ ∃p[S(p)]
•
Relation ON (obligatorisch auftretend aber nicht beweisend):
1. alle p mit D haben S
2. nicht alle p mit S haben D
3. es gibt mindestens ein p mit D
∧
S ON D = ∀p[D(p)→S(p)] ∧ ∃p[S(p)∧¬D(p)] ∧ ∃p[D(p)]
•
Relation FN (fakultativ auftretend und nicht beweisend):
1. nicht alle p mit S haben D
2. nicht alle p mit D haben S
3. es gibt mindestens ein p mit S und D
∧
S FN D = ∃p[S(p)∧¬D(p)] ∧ ∃p[D(p)∧¬S(p)] ∧ ∃p[S(p)∧D(p)]
•
Relation A (ausschließend)
1. alle p mit S schließen D aus
2. es gibt mindestens ein p mit S und nicht D
3. es gibt mindestens ein p mit D und nicht S
∧
S A D = ∀p[S(p)→¬D(p)] ∧ ∃p[S(p)∧¬D(p)] ∧ ∃p[D(p)∧¬S(p)]
Relationships in CADIAG-II
positive association
necessity ∈ (0,1]
sufficiency ∈ (0,1]
exclusion
necessity = [0]
sufficiency = [0]
Logikmodule
IF. . .
an order for DIGOXIN
is placed
Pharmacist enters
medication order
The system works behind
the scenes as an
“active agents”
evaluating
clinical
scenarios.
AND
The patient is already
receiving FUROSEMIDE
System checks
patient’s medication
System checks
patient’s lab results
CONDITIONS
MET
“fi
res
”
The patient’s POTASSIUM
LEVEL is low
The system immediately
takes the following
predefined actions:
Notify the ordering
pharmacist of the
drug interaction and
contact the requesting
physician immediately
AND
rul
e
AND
THEN. . .
Notify the clinical
pharmacist for
follow-up and
documentation
AND
Notify the requesting
physician of the
situation and suggest
a potassium
supplement
Drug-Laboratory Triggers
Clinical Pathways and Guidelines
Enacting Guidelines
FuzzyKBWean:
A Fuzzy Control System for
Weaning from Artificial Ventilation
C. Schuh1, M. Hiesmayr2, K.-P. Adlassnig1, M. Kolb2
1Department
of Medical Computer Sciences
2Department of Cardiothoracic and Vascular Anaesthesia and Intensive Care
University of Vienna Medical School
Objective
• mechanically ventilated patients after cardiothoracic
surgery in an intensive care unit (ICU)
• proposals for changes in ventilator settings during the three
phases of mechanical ventilation (stabilization, weaning,
and finally extubation of the patient)
• open-loop system: integration into the patient data
management system (PDMS); time resolution of 1 minute
• closed-loop system as a long-term objective: integration
into the ventilator (auto-mode)
Motivation
We found that nurses and respiratory therapists, using
protocol guidance, weaned patients from mechanical
ventilation safely and more quickly than the team following
the traditional practice of physician-directed weaning.
Koleff MH, et al : A Randomized, Controlled Trial of Protocoll-Directed
versus Physician-Directed Weaning from Mechanical Ventilation.
Critical Care Medicine 1997; 25, 567–574.
Structure of FuzzyKBWean
Methods
• phase-dependent fuzzy sets
• linguistic if/then rules
– if: patient’s physiological parameters and ventilator measurement
parameters (in a defined context)
– then: proposals for changes in ventilator settings
• fuzzification step
– arithmetic, statistical, comparative, logical, temporal, and control
operators
• defuzzification step
– Sugeno’s center of gravity method
• verification by the attending physician, i.e., open-loop
Patient’s Physiological Parameters
• oxygenation O2
– arterial oxygen partial pressure pO2 (directly)
– oxygen saturation SpO2 (indirectly: pulsoxymetry)
• ventilation: CO2-elimination (~ to alveolar ventilation)
– arterial carbondioxide partial pressure pCO2 (directly)
endtidal carbondioxide EtCO2 (indirectly)
• expired tidal volume Tve
• respiratory rate Vrate
Fuzzy Control
inference process
with
linguistic fuzzy rules
defuzzification
with Sugeno’s
center of gravity
method
fuzzification
with
fuzzy sets
PaO2 [mmHg]
PaCO2 [mmHg]
measurement and observational
level
FiO2-change
[%]
Knowledge Base
fuzzy sets
PaO2
1
low
very low
0
70
PaCO2
low
1
very low
0
35
90 95 100 [mmHg]
40
45 50
very high
55
linguistic fuzzy rules
rule 1: If PaO2 = very low and PaCO2 = low, then FiO2-change = +10
rule 2: If PaO2 = normal and PaCO2 = normal, then FiO2-change = –5
rule 3: If PaO2 = high and PaCO2 = normal, then FiO2-change = –10
etc.
85
high
high
normal
30
75
normal
[mmHg]
PATIENT:
PaO2 = 96 mmHg, PaCO2 = 42 mmHg
fuzzification
PaO2
1
0.8
low
very low
normal
high
0.2
70
PaCO2
low
1
very low
0
35
85
high
40 45 50
42
90 95 100 [mmHg]
96
very high
normal
30
75
55
inference process
rule 2: If PaO2 = normal and PaCO2 = normal, then FiO2-change –5
rule 3: If PaO2 = high and PaCO2 = normal, then FiO2-change –10
rule 2: min (PaO2 = normal, PaCO2 = normal) = min (0.8, 1) = 0.8
rule 3: min (PaO2 = high, PaCO2 = normal) = min (0.2, 1) = 0.2
defuzzification
FiO2-change = 0.8*(–5)+0.2*(–10) = –6
0.8+0.2
[mmHg]
Results–I
• 23 variables
– 74 fuzzy sets (phase-dependent)
• 16 if/then rules
– 4 rules checking for measurement errors and validity
– 3 rules for ventilation (normal range, hypoventilation,
hyperventilation)
– 4 rules for oxygenation (stabilization, oxygenation normal,
hypoxia, severe hypoxia)
– 4 rules for intermediate states (increased EtCO2, decreased EtCO2,
phase changes)
– 1 rule for extubation
Results–II
• 10 prospectively randomized patients
– FuzzyKBWean reacted correctly 131 (SEM 47) minutes earlier
than the attending physician
– adjustment of ventilation parameters was more reliable than
adjustment of oxygenation (EtCO2 is more reliable as SpO2)
– phase-specific rules often proposed too small changes of the
ventilator settings
• temporal rule blocking, fuzzy set adaptations, employing
thresholds to avoid oscillations
Results–III
Delay of Staff Reaction in Case of
Hyperventilation
patient
episode
proposed change at
effective change at
delay (min)
M
1
2
3
1
1
1
2
1
2
1
2
1
2
3
4
1
16:30
23:50
04:27
21:17
22:57
22:30
01:15
13:45
20:20
17:03
08:48
20:02
20:12
20:30
22:47
21:05
19:42
01:55
07:43
03:58
23:16
01:15
01:50
14:45
20:48
18:27
16:36
20:12
20:30
22:47
23:22
21:40
192
125
196
401
19
165
35
60
28
104
468
10
18
137
35
35
B
D
E
K
C
G
K
Discussion on FuzzyKBWean
• methodology
– minimal number of therapeutically significant classes per variable
– gradual transition between variable classes
• adequate consideration of the inherent fuzziness of medical
concepts
– intuitive if/then rules at the knowledge level
• physician’s medical knowledge was transferred to
FuzzyKBWean
• clinical trial
– periods of deviation from the target parameters are shorter
• contribution to the patient’s safety and comfort
– closed-loop: detection of artifacts and information obtained by
direct observation of the patient
FuzzyARDS:
Knowledge-Based Monitoring
and Decision Support
F. Steimann1, H. Steltzer2, K.-P. Adlassnig1
1Department
of Medical Computer Sciences
Section on Medical Expert and Knowledge-Based Systems
University of Vienna Medical School, Austria
2 Department
of Anesthesiology and General Intensive Care Medicine
University of Vienna Medical School, Austria
Objective
• knowledge-based decision support
– monitoring patients with acute respiratory distress syndrome
(ARDS)
– early detection of ARDS
– therapy advice in ARDS cases
• international study (Vienna, Berlin, Marburg, Paris, Milan)
– to improve ARDS definition
– to compare therapy entry criteria
Wir beobachteten, daß Krankenschwestern und Therapeuten,
die für die Entwöhnung der Patienten von der künstlichen
Beatmung Protokolle verwendeten, diese schneller und
sicherer entwöhnten, als das Team, welches
traditionellerweise den Anordnungen der Ärzte folgte.
Koleff MH, et al. : A Randomized, Controlled Trial of Protocoll-Directed
versus Physician-Directed Weaning from Mechanical Ventilation.
Critical Care Medicine 1997; 25, 567–574.
VM
Ventilator Manager: Stanford University Medical School, 1979
•
experimental, rule-based expert system for on-line data interpretation at the ICU
STATUS-RULE: STABLE HEMODYNAMICS
DEFINITION: defines stable hemodynamics based on blood pressure and heart rate
APPLIES TO: patients on volume, cmv, assist, T-piece
COMMENT: look at mean arterial pressure for changes in blood pressure and systolic
blood pressure for maximum pressures
IF
1) heart rate is acceptable, and
2) pulse rate does not change by 20 beats/minute in 15 minutes, and
3) mean arterial pressure is acceptable, and
4) mean arterial pressure does not change by 15 torr in 15 minutes, and
5) systolic blood pressure is acceptable,
THEN
the hemodynamics are stable.
Example of a heuristic VM-rule.
First Approach: Method
• if/then rule for the diagnosis of ARDS under consideration
of risk factors and first clinical signs
• if/then rule for the diagnosis of manifest ARDS, based on
Murray and Morel scores
ARDS: Rule for Early Detection
risk =
.or.
.or.
.or.
.or.
.or.
.or.
.or.
.or.
.or.
.or.
.or.
.or.
SEPSIS;
TRAUMA;
ASPIRATION;
PNEUMONIA;
SHOCK;
TRANSFUSION;
INHALATION;
PANKREATITIS;
DROWNING;
FAT EMBOLISM;
COAGULOPATHY;
BURNING;
KIDNEY FAILURE
.or.
.or.
.or.
.or.
.or.
.or.
.or.
PAO2 < 75 .and. FIO2 ≥ 0.5;
PAO2 ≤ 65 .and. FIO2 ≥ 0.4;
PAO2 < 250 .and. FIO2 = 1.0;
PAO2 / FIO2 < 250;
ROENTSTAD ≥ 1;
SHUNT > 20;
TOTRAUMVEN;
COMPLIANCE < 50
signs =
signs = signs .and. PCWP < 18
ARDS: rule for early detection = risk .and. signs
First Approach: Retrospective Tests
•
•
1,104 data records from 32 patients suffering from ARDS
116 data records from 8 patients not suffering from ARDS
results in ARDS diagnosis:
•
•
sensitivity: 89.6%
specificity: 18.1%
results in manifest ARDS:
•
•
sensitivity: 68.8%
specificity: 69.8%
•
diagnosis in accordance with Murray and Morel scores: 69.7%
(all 1,220 data records)
First Approach: Problems
•
ARDS is a fuzzily defined nosological entity
⇒ a crisp definition of ARDS is inadequate
⇒ in particular, the commitment to crisp limits of findings is unintuitive (see
figure)
⇒ thresholds of PaO2 and FiO2 for patients suffering from ARDS:
PaO2
FiO2
⇒ hypothesis: it is practically impossible to characterize ARDS by a crisp
definition
Second Approach: Methods
•
development of a state transition diagram (deterministic
automaton)
–
–
mutually exclusive states (no ARDS, suspected ARDS, early
detection of ARDS, confirmed ARDS, manifest ARDS, severe
ARDS, ...)
crisp transition conditions (PaO2/FiO2 < 250, ...)
monitoring
chest radiograph,
spontaneous breathing,
artificial respiration, and
circulation negative
PaO2/FiO2 < 250
for 1 day
no
risk factors
suspected
ARDS
risk
factors
exclusion criteria
not clarified
PaO2/FiO2
no
ARDS
confirmed
ARDS
manifest
ARDS
CT: pulmonary lesion < 1/3
and Morel score < 2.5
and Murray score < 2.5
or ...
ARDS
diagnosis
risk
factors
no
risk factors
CT: pulmonary lesion < 2/3
and Morel score > 2.5
and Murray score > 2.5
or ...
severe
ARDS
chest radiograph,
spontaneous breathing,
artificial respiration, or
circulation pathological
CT: pulmonary lesion > 2/3
and Morel score = 4
and Murray score > 3.5
ECCO2/
ECMO
Weighted Scores
Scoring System
definition of two criteria:
PaO2/FiO2 < 150 (with weight 2)
shunt > 30
(with weight 1)
case 1:
criteria
PaO2/FiO2 = 100
shunt = 25
score
fulfillment
2/(2+1)
0/(2+1)
2/3
case 2:
criteria
PaO2/FiO2 = 160
shunt = 35
score
fulfillment
0/(2+1)
1/(2+1)
1/3
New Approach: Methods
• development of a fuzzy state transition diagram (fuzzy
automaton)
– with mutually non exclusive states (gradual transition from one
state to another, concurrent partial presence of several states; the
clinician is able to consider the situation carefully)
– with fuzzy conditions for the transition of states (fuzzy medical
concepts, fuzzy trend detection)
monitoring
chest radiograph,
spontaneous breathing,
artificial respiration, and
circulation negative
PaO2/FiO2 < 250
for 1 day
no
risk factors
suspected
ARDS
risk
factors
exclusion criteria
not clarified
PaO2/FiO2
no
ARDS
no
risk factors
ARDS
confirmed
manifest
ARDS
CT: pulmonary lesion < 1/3
and Morel score < 2.5
and Murray score < 2.5
or ...
ARDS
diagnosis
risk
factors
CT: pulmonary lesion < 2/3
and Morel score > 2.5
and Murray score > 2.5
or ...
severe
ARDS
chest radiograph,
spontaneous breathing,
artificial respiration, or
circulation pathological
CT: pulmonary lesion > 2/3
and Morel score = 4
and Murray score > 3.5
ECCO2/
ECMO
Scoring System with
Fuzzy Concepts
Scoring System with Fuzzy Concepts
PaO2 / FiO2 < 150 (200) with weight 2
Shunt > 30 (20) with weight 1
criteria
PaO2/FiO2 = 164
shunt = 23
score
} 2+1 = 3
score and fulfillment
0.72 * (2/3) = 0.72 * 2/3
0.3 * (1/3) = 0.3 * 1/3
0.58
degree
degree
1
1
0.72
0.3
0
150 164
200
PaO2/FiO2
0
20
23
30
shunt
Patient Conditions
Condition
Definition with “fuzzified by”
adequate oxygenation
SaO2 above 97% (93%) for 5 minutes
hypoxemia
SaO2 between 90% and 93% (87% and
97%) for 2 minutes
high FiO2
FiO2 above 60% for 30 seconds
low FiO2
FiO2 below 60% for 30 seconds
rapidly improving
oxygenation
SaO2 increasing from 87–95%
(85–99%) to 97–100% (93–100%)
within 30–90 seconds
(5–120 seconds)
slowly decreasing
oxygenation
SaO2 above 96% (91%) steady or
decreasing to 94% (89%) within 25
minutes
Graphical representation
Trend Detection Applied to SaO2
(in part filtered through moving average)
12 hour period
State Transition Diagram
of the Automaton
start
normal
improved after
hand bagging
responding
to high FiO2
hypoxic
not improved after
hand bagging
not responding
to high FiO2
Definition of States under
Consideration
State
Interpretation
start
normal
initial state, undecided
oxygenation is satisfactory without additional
effort such as increased FiO2
oxygenation is too low and should be
improved
high FiO2 has affected oxygenation positively
high FiO2 does not have the desired effect
hand bagging has persistently improved
oxygenation
hypoxic
responding to high FiO2
not responding to high FiO2
improved after hand
bagging
not improved after hand
bagging
hand bagging shows no satisfactory effect
Linguistic State Transitions
From state
On condition
To state
start
adequate oxygenation
hypoxemia
normal
hypoxic
normal
hypoxemia
hypoxic
hypoxic
low FiO2 ∧ adequate oxygenation
high FiO2 ∧ rapidly improving
oxygenation
high FiO2 ∧ hypoxemia
normal
responding to high FiO2
not responding to high FiO2
low FiO2 ∧ slowly decreasing
oxygenation
low FiO2 ∧ hypoxemia
improved after hand bagging
not responding to high
FiO2
low FiO2 ∧ hypoxemia
high FiO2 ∧ adequate oxygenation
hypoxic
responding to high FiO2
improved after hand
bagging
adequate oxygenation
hypoxemia
normal
hypoxic
not improved
after hand bagging
hypoxemia
hypoxic
responding to high FiO2
not improved after hand bagging
Distribution of States over Time
Discussion on FuzzyARDS
• concept modeling and trend detection with fuzzy sets
– inherent fuzziness of medical and temporal concepts, unsharpness
of boundaries
• monitoring with fuzzy automata
– fuzzily defined nosological concepts (partial lack of medical
theory)
• visualization on the ICU monitor
– integration in the PDMS
– dynamic visualization of changes of states
– display of state transitions over time