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Artificial-intelligence-augmented clinical medicine
Klaus-Peter Adlassnig
Section for Medical Expert and Knowledge-Based SystemsCenter for Medical Statistics, Informatics, and Intelligent SystemsMedical University of ViennaSpitalgasse 23, A-1090 Viennawww.meduniwien.ac.at/kpa
Einführung in Medizinische Informatik, WS 2013/14, 30. Oktober 2013
Artificial Intelligence (AI)
• Definition 1: AI is a field of science and engineering concerned with the computational understanding of what is commonly called intelligent behavior, and with the creation of artifacts that exhibit such behavior.
from: Shapiro, S.C. (1992) Artificial Intelligence. In Shapiro, S.C. (ed.) Encyclopedia of Artificial Intelligence, 2nd ed., vol. 1, Wiley, New York, 54–57.
• Definition 2: AI is the science of artificial simulation of human thought processes with computers.from: Feigenbaum, E.A. & Feldman, J. (eds.) (1995) Computers & Thought. AAAI Press, Menlo Park, back cover.
Artificial Intelligence—applicable to clinical medicine
• It is the decomposition of an entire clinical thought process and its separate artificial simulation—also of simple instances of “clinical thought”—that make the task of AI in clinical medicine manageable.
• A functionally-driven science of AI that extends clinicians through computersystems step by step can immediately be established.
⇓artificial-intelligence-augmented clinical medicine
Computational intelligence in medical research
biomolecular researchmedical statistics
clustering and classification data and knowledge mining
consensus conferences
definitional, causal, statistical, and heuristic knowledge
molecular biomedicine
medical knowledge
facts consensus generalization
medical studies
evidence-based medicine
Computational intelligence in patient care
decision-oriented analysis and interpretation
of patient data
definitional, causal, statistical, and heuristic knowledge
human-to-human human-to-humanAI-augmentation
patient-physician encounter
patient-physician follow-up
medical knowledge modules
medical knowledge
Computers in clinical medicine—steps of natural progression
• step 1: patient administration• admission, transfer, discharge, and billing
• step 2: documentation of patients’ medical data• electronic health record: all media, distributed, life-long (partially fulfilled)
• step 3: patient and hospital analytics• data warehouses, quality measures, reporting and research databases,
patient recruitment… population-specific
• step 4: clinical decision support• safety net, quality assurance, evidence-based
… patient-specific
Clinical medicine
medical guidelines
history data
symptoms
signs
laboratorytest results
biosignals
images
genetic data
prognosispatient patient
symptomatic therapy
differentialtherapy
differentialdiagnosis
symptoms
signs
test results
findings
examination subspecialities clinic
medication history
radiological diagnosis
laboratorydiagnosis
…
Clinical medicine
medical guidelines
history data
symptoms
signs
laboratorytest results
biosignals
images
genetic data
prognosispatient patient
symptomatic therapy
differentialtherapy
differentialdiagnosis
symptoms
signs
test results
findings
examination subspecialities clinic
medication history
radiological diagnosis
laboratorydiagnosis
…
QMRDxPlainCADIAG
MYCIN
…
ANNs
SVMs
…
…
personalizedmedicine
+
+
Clinical medicine: high complexity
• sources of medical knowledge‒ definitional‒ causal‒ statistical‒ heuristic
• layers of medical knowledge‒ observational and measurement level‒ interpretation, abstraction, aggregation, summation‒ pathophysiological states‒ diseases/diagnoses, therapies, prognoses, management decisions
• imprecision, uncertainty, and incompleteness‒ imprecision (=fuzziness) of medical concepts
* due to the unsharpness of boundaries of linguistic concepts‒ uncertainty of medical conclusions
* due to the uncertainty of the occurrence and co-occurrence of imprecise medical concepts‒ incompleteness of medical data and medical theory
* due to only partially known data and partially known explanations for medical phenomena• “gigantic” amount of medical data and medical knowledge
‒ patient history, physical examination, laboratory test results, clinical findings‒ symptom-disease relationships, disease-therapy relationships, …‒ terminologies, ontologies: SNOMED CT, LOINC, UMLS, …
specialisation, teamwork, quality management, computer support
• studies in Colorado and Utah and in New York (1997)
– errors in the delivery of health care leading to the death of as many as 98,000 US citizens annually
• causes of errors– error or delay in diagnosis– failure to employ indicated tests– use of outmoded tests or therapy– failure to act on results of testing or
monitoring– error in the performance of a test, procedure,
or operation– error in administering the treatment – error in the dose or method of using a drug– avoidable delay in treatment or in responding
to an abnormal test– inappropriate (not indicated) care– failure of communication– equipment failure
• prevention of errors– we must systematically design safety into
processes of care
errors
prevention
Medical information and knowledge-based systems
symptomssigns
test resultsclinical findings
biosignalsimages
diagnosestherapies
nursing data
•••
standardizationtelecommunication
chip cards
anatomybiochemistryphysiology
pathophysiologypathologynosology
therapeutic knowledgedisease management
•••
subjective experienceintuition
knowledge-based systems
patient’s medical data physician’s medical knowledge
medical statisticsclustering & classificationdata & knowledge mining
machine learning
clinical decision supportmedical expert systems
manypatients
single patient
diagnosistherapy
prognosismanagement
generalknowledge
•••
generalknowledge
telemedicine telemedicineintegration
information systems
induction
deduction
Clinical decision support and quality assurance (in general)
patients’ structured medical data
patient management guidelines & quality assurance• evidence-based reminders and processes• computerized clinical guidelines, protocols, SOPs• healthcare-associated infection surveillance
prognostic prediction• illness severity scores, prediction rules• trend detection and visualization
therapy advice• drug alerts, reminders, calculations
– indication, contraindications, redundant medications, substitutions
– adverse drug events, interactions,dosage calculations, consequent orders
• management of antimicrobial therapies, resistance• (open-loop) control systems
diagnostic support• clinical alerts, reminders, calculations• data interpretation, (tele)monitoring• differential diagnostic consultation
– rare diseases, rare syndromes– further or redundant investigations – pathological signs accounted for
• consensus-criteria-based evaluation– definitions– classification criteria
according toKensaku Kawamoto,University of Utah, 2012:
“A Holy Grail of clinical informatics is scalable, interoperable clinical decision support.”
What have we done?
Interpretation of
hepatitis serology test results
test results
interpretation
ORBIS Experter: Hepatitis serology diagnostics
Interpretation of
hepatitis serology test results
Differentialdiagnose rheumatischerErkrankungen
ComputergestützteEntwöhnung vom
Respirator
Personalized clinical decision support
history physicalsigns
lab tests
clinicalfindings + genomic
data
patient medical data
present (personalized) CDS
future personalized CDS
unavoidable, more specific diagnostics, extends the realm of therapy
Solution at the Vienna General Hospital
Arden Syntax development &
test environment
data & concept mining
data & knowledge
services center
Arden Syntax rule engine
knowledge
knowledge
resultsdatain
terf
aces
Arden Syntax server
wards
HIS
departments
HIS
units
PDMS
clinical laboratories
LIS
genomic data laboratories
LIS
hospital
extended documen-
tation& research
data base
Arden Syntax and Health Level Seven (HL7)
• A standard language for writing situation-action rules that can trigger alerts based on abnormal clinical events detected by a clinical information system.
• Each module, referred to as a Medical Logic Module (MLM), contains sufficient knowledge to make a single decision.
extended by packages of MLMs for complex clinical decision support
• The Health Level Seven Arden Syntax for Medical Logic Systems, Version 2.9—including fuzzy methodologies—was approved by the American National Standards Institute (ANSI) and by Health Level Seven International (HL7) on 14 March 2013.
continuous development since 1989
General MLM LayoutMaintenance Category Library Category Knowledge Category Resources Category
Identify an MLMData TypesOperators
Basic OperatorsCurly Braces List OperatorsLogical OperatorsComparison OperatorsString OperatorsArithmetic OperatorsOther Operators
Control StatementsCall/Write Statements and Trigger
Sample MLM (excerpt)
Arden Syntax, Arden Syntax server, and health care informationsystems
functionality
integration
HIS, MIS, PDMS, LIS, medical practice SW, web-based EHR, telemedicine applications,health portals,…
reminders and alerts, monitoring, surveillance, diagnostic andtherapeutic decision support, …
*operational:- harmonized input data- Arden Syntax MLMs - collected reasoning dataexploratory:- rule learning/tuning- data and concept mining
*data & knowledge services center
service-oriented
web-based
Arden Syntax server and software components
• web-services-based Arden Syntax server including
‒ Arden Syntax engine‒ MLM manager‒ XML-protocol-based
interfaces, e.g., SOAP, REST, and HL7
‒ a project-specific dataand knowledge servicescenter may be hosted
• Java libraries ‒ Arden Syntax compiler ‒ Arden Syntax engine
• Arden Syntax integrated development and test environment (IDE) including‒ Medical logic module
(MLM) editor and authoring tool
‒ Arden Syntax compiler(syntax versions 2.1, 2.5, 2.6, 2.7, 2.8, and 2.9)
‒ Arden Syntax engine‒ MLM test environment‒ MLM export component
• command-line Arden Syntax compiler
Arden Syntax development & test
environment
data & knowledge services center
Arden Syntax rule engine
knowledge
knowledge
resultsdatah
ealt
h c
are
info
rmat
ion
sys
tem
results
reporting toolsknowledge administration
data
inte
rfac
es2)
Arden Syntax server 1)
1) integrated, local, or remote2) local and web services, web frontend
Arden Syntax development & test
environment
data & knowledge services center
Arden Syntax rule engine
knowledge
knowledge
resultsdatah
ealt
h c
are
info
rmat
ion
sys
tem
results
reporting toolsknowledge administration
data
inte
rfac
es2)
Arden Syntax server 1)
1) integrated, local, or remote2) local and web services, web frontend
Fuzzy Arden Syntax: Modelling uncertainty in medicine
• linguistic uncertainty
‒ due to the unsharpness (fuzziness) of boundaries of linguistic concepts; gradual transition from one concept to another
‒ modeled by fuzzy sets, e.g., fever, increased glucose level
• propositional uncertainty
‒ due to the uncertainty (or incompleteness) of medical conclusions; includes definitional and causal, statistical and subjective relationships
‒ modeled by truth values between zero and one, e.g., usually, almost confirming
Crisp sets vs. fuzzy sets
yes/no decision
gradual transition
age
1
χY young
0 threshold
U = [0, 120]Y ⊆ U with Y = {(µY (x)/ x)x ∈ U}µY: U → [0, 1]
11 + (0.04 x)2
∀ x ∈ U0 age
1
µY young
threshold0
U = [0, 120]Y ⊆ U with Y = {(χY (x)/ x)x ∈ U}χ Y: U → {0, 1}
χ Y (x) = ∀ x ∈ U0 x > threshold1 x ≤ threshold
1 x ≤ threshold
x > thresholdµY (x) =
0
Crisp sets vs. fuzzy sets
age
1
χY young
0 threshold
0 age
1
µY young
threshold0
0
“arbitrary” yes/no decisions• cause of unfruitful
discussions• often simply wrong
“intuitive” gradual transitions
x50
0.50
1.00
µA (x)
100 150 200
µ↑ (x) = 0.82
highlydecreased
↓↓decreased
↓normal
⊥increased
↑
highlyincreased
↑↑
[mg/dl]
µ⊥ (x) = 0.18
µ↓↓ (x) = 0.00µ↓ (x) = 0.00µ↑↑ (x) = 0.00
glucose level in serum of 130 mg/dl
0.00
Degree of compatibility [= degree of membership]
Integration into i.s.h.medat the
Vienna General Hospital
SOP checkingin melanoma patients
receiving chemotherapy
Towards a science of clinical medicine
patient’s medical data and
healthcare processes for
machine processing
patient’s medical data and
healthcare processes for
human processing
≠
“Measure what is measurable, and make measurable what is not so.”Galileo Galilei
1564–1642
Crucial point in clinical medicine:
“Digitize what is digitizable, and make digitizable what is not so.”Klaus-Peter Adlassnig
observations measurementse.g., temperature chart e.g., CRP
skin color (jaundice, livid, …) color measurement… …
The medical world becomes flat
after Thomas L. FriedmanThe World is Flat.Penguin Books, 2006.
• in a local world‒ decision support will be part of clinical information systems
• in a global world‒ any activity—where we can digitize and decompose the value chain*,
and move the work around—will get moved around
* patient value chain: patient examination, diagnosis, therapy, prognosis, health caredecisions, patient care
Future of artificial-intelligence-augmented clinical medicine
predictable future
medical data collection, storage,
& distribution
clinical decision support
tomorrowtoday
043
21
41 1
HIS 1.0
HIS 2.0
personalized medicine
implants, prostheses,
robotics
Closing remark: formalism vs. reality
Pure mathematics is much easier to understand, much simpler, than the
messy real world!
Gregory Chaitin (2005) Meta Math!: The Quest for Omega,Pantheon Books, New York.
Clinical informatics deals with the “messy real patient”.