Session Lamparter Healthcare From Patients to Information and Back
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Transcript of Session Lamparter Healthcare From Patients to Information and Back
8/6/2019 Session Lamparter Healthcare From Patients to Information and Back
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Copyright © Siemens AG 2010. All rights reserved.
Corporate Technology
Steffen Lamparter
Siemens AGCorporate TechnologyGlobal Technology Field Autonomous Systems
Corporate Technology
Copyright © Siemens AG 2010. All rights reserved.
From Patients to Information and Back
How semantic technologies support this round trip
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Towards information overload…
Patient
Records
Vital Data
Activity
Monitoring
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How can we use patient information to improvequality and efficiency of medical services?
From patients to information… …and back???
Health Archive Health Archive?
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Corporate TechnologyAgenda
Motivation
Semantic Technologies
Summary
Ambient Assisted Living
Patient Data Management
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Semantic Technologies
Semantic Annotation
Knowledge Representation Formal Domain Models
• Rule-/DL-based Models
• Incomplete / Uncertain Information
• Temporal / Spatial Dependencies
• …
Reasoning Methods
Logical Reasoning Algorithms
• Deductive Reasoning Methods
• Abductive Reasoning Methods
• …
Data Integration and Exchange
(Semi-) Automated Monitoring & Diagnosis
Information Retrieval & Search
Basic Functionalities
• Automation due to formal model
• Higher relevance & precision in search
applications
Structuring unstructured Information
• Automated annotation of texts and images
• Natural Language Processing
• Ontology Learning
• Sensor Data Preprocessing / Data Fusion• …
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Copyright © Siemens AG 2010. All rights reserved.
Corporate TechnologyAgenda
Motivation
Semantic Technologies
Patient Data Management
Summary
Ambient Assisted Living
8/6/2019 Session Lamparter Healthcare From Patients to Information and Back
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Semantic Search for Patient Data Management
Knowledge Representation
Reasoning Methods
Data Integration and Exchange
(Semi-) Automated Monitoring & Diagnosis
Information Retrieval & Search
P a t i e n t
D a t a M a
n a g e m e n
t
Integrated Health ArchiveIntegrated Health Archive
(Semantically Annotated Documents)(Semantically Annotated Documents)
Query Answering
Improve relevance and precision of
retrieval process through semantic
query interpretation and documentannotation.
Semantic Search
Semantic AnnotationOCR &
Ontology LearningImage Annotation
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Semantic Search for Patient Data Management
P a t i e n t
D a t a M a
n a g e m e n
t
OCR &OntologyLearning
IntegratedIntegrated Health ArchiveHealth Archive
((SemanticallySemantically AnnotatedAnnotated DocumentsDocuments))
Query Answering
Improve relevance and precision of
retrieval process through semantic
query interpretation and documentannotation.
Semantic Search
How do we representsemantic annotation of
documents?
ImageAnnotation
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Ontologies can be used for annotating documentsand images
DoctorbelongsTo
Graphical representation
of the ontological model
definesAllergies Patient Record
Concept
Property
definesAlerts
issuedBy
specifiesOrders
Person
subClassOf
Ontology = Shared model that formally describes arbitrary entities(„concepts“, „classes“) and their interrelationships („roles“, „properties“).
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PatientRecord ≡ =1 belongsTo.Person ⊓ ∀ specifiesOrders.Order ⊓ ∀ definesAllergies.Allergy
Allergy ≡ LatexAllergy PeanutAllergy …
…
Underlying logical model
Ontology Representations
<?xml version="1.0"?>
<owl:Ontology rdf:about=""/>
<owl:Class rdf:about="#PatientRecord">
<rdfs:subClassOf rdf:resource="&owl;Thing"/>
</owl:Class>
<owl:Class rdf:about="#Name"><rdfs:subClassOf rdf:resource="&owl;Thing"/>
</owl:Class>
<owl:Class rdf:about="#Allergy">
<rdfs:subClassOf rdf:resource="&owl;Thing"/>
</owl:Class>
<owl:Class rdf:about="#Orders">
<rdfs:subClassOf rdf:resource ="&owl;Thing"/> "/>
</owl:Class>
…
OWL/XML representation
of the ontological model
Person
Order
belongsTo
definesAllergies Patient Record
Allergy
Alert
Drug
Latex
PeanutsdefinesAlert
issuedBy
specifiesOrders
Doctor
subClassOf
Graphical representation
of the ontological model
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Many ontologies exist in the healthcare domainMany ontologies exist in the healthcare domain……
Medical ontologies
SNOMED: 379.000 Concepts, 52 Roles
GALEN: 2740 Concepts, 413 Roles
RadLex: 1500 Concepts
…
Existing Ontologies
Existing large taxonomies/ontologies are a
big advantage of the medical domain
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Semantic Search for Patient Data Management
P a t i e n t
D a t a M a
n a g e m e n
t
IntegratedIntegrated Health ArchiveHealth Archive
((SemanticallySemantically AnnotatedAnnotated DocumentsDocuments))
Query Answering
Improve relevance and precision of
retrieval process through semantic
query interpretation and documentannotation.
Semantic Search
How do we automaticallyderive semantic
annotations?
OCR &OntologyLearning
ImageAnnotation
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Semantic Annotation
Generate structured, semantically annotated information from unstructured
information such as texts
DoctorbelongsTo
definesAllergies
Patient Record
definesAlert
issuedBy
specifiesOrders
Person
Concept
Property
Example: Semantic annotation of patient record
instance
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Semantic Annotation from Texts: Simple Example
Influenza, commonly referred
to as the flu, is an infectious
disease caused by RNAviruses of the family
Orthomyxoviridae, that affects
birds and mammals. The most
common symptoms of thedisease are chills, fever,
sore throat, muscle pains or
general discomfort.
Influenza
FluInfectious
DiseaseRNA viruses
of the family
Orthomyxoviridae
Birds Mammels
common symptoms
of the disease
Chills
Fever
Sore throat
General
discomfort
Ontology learning steps:
• Part of speech tagging
• Apply patterns for concept label identification
• Complete taxonomy from domain ontology• Apply patterns for relation label identification
commonly
referred to as
RNA viruses
Orthomyxoviridae
Living Entities
family
affects
causedBy
Common
symptoms
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Semantic Annotation from Texts
Part of speech tagging (noun, verb, etc.)
Named Entity Recognition (Munich City) Hearst Patterns ( … is a … subclass)
Word Sense Disambiguation
(jaguar animal vs. car)
Ontological background knowledge(flu is a subclass of illness)
Examples of basic linguistic methods
B ui t el a a
r ,2 0 0 5
Paul Buitelaar, Philipp Cimiano, Bernardo Magnini: Ontology Learning from Text: Methods, Evaluation and Applications Frontiers in Artificial Intelligence and Applications Series, Vol. 123, IOS Press, July 2005.
Basic Linguistic Processing: Many free and commercial tools available GATE (Univ. Sheffield)
http://gate.ac.uk/ Alvey Natural Language Tools (Univ. Cambridge, Edinburgh and Lancaster)
http://www.cl.cam.ac.uk/research/nl/anlt.html
Term/Taxonomy/Relation Extraction Text2Onto (Univ. Karlsruhe)
http://ontoware.org/projects/text2onto/ OntoLT/RelExt (DFKI)
http://jatke.opendfki.de
http://olp.dfki.de/OntoLT/OntoLT.htm OntoGen (JSI, Ljubljana)
http://www.textmining.net ASIUM (Univ. Paris)
OntoLearn (Univ. Rome)
http://www.dsi.uniroma1.it/~navigli/
Available Tools
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Semantic Search for Patient Data Management
P a t i e n t
D a t a M a
n a g e m e n t
IntegratedIntegrated Health ArchiveHealth Archive
((SemanticallySemantically AnnotatedAnnotated DocumentsDocuments))
Query Answering
Improve relevance and precision of
retrieval process through semantic
query interpretation and documentannotation.
Semantic Search
How do we understand
the user information
need?
OCR &OntologyLearning
ImageAnnotation
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Semantic Keyword Interpretation
Query Answering:Interpretation of Keywords
Map query to the source schemata (e.g. select shortest path)
Mueller Munich
KeywordSearch
Patient
lives in
Linguistic analysis of query (Word Sense Disambiguation, etc.)
Context (other queries/tasks)
Rewrite query (e.g. synonyms)
Identify user‘s real need
Hospital
Munich
Müller
has name
Person
Müller
has name
attends
Located in
Doctor
works for
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Query Answering:Iterative user-based query refinement
RankedRanked
SearchSearch
ResultsResults
SemanticSearch
IntegratedIntegrated
Health ArchiveHealth Archive
Query
IntegratedIntegrated
Health ArchiveHealth Archive
SemanticSearch
Query
Mueller Munich
Lives in Munich or
is in Munich Hospital?
Mueller living in
Munich
…
Assist the user inspecifying the query
Questions
disambiguate
keywords in query
Avoids trail & error approach
Proactive Search
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Summary: Semantic Search for Patient DataManagement
• Scalability for huge health archives
(approx. 106 concepts, terabytes of data)
• Robustness of semantic annotation
approaches (for texts, images, etc.)
Future Challenges
• Semantic annotation of patient records
and semantic query interpretation can be
used for improving patient recordretrieval
• Rather mature semantic search engines
available (TrueKnowledge, Powerset,
Hakia,…)
• Medical ontologies/taxonomies already
available (SNOMED CT, GALEN,
RadLex)
State of the art
P a t i e n t
D a t a M a
n a g e m e n t
IntegratedIntegrated Health ArchiveHealth Archive
((SemanticallySemantically AnnotatedAnnotated DocumentsDocuments))
Query Answering
Improve relevance and precision of
retrieval process through semantic
query interpretation and documentannotation.
Semantic Search
OCR &OntologyLearning
ImageAnnotation
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Corporate TechnologyAgenda
Motivation
Overview: Semantic Technologies
Patient Data Management
Summary
Ambient Assisted Living
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Situation Understanding for Ambient Assisted Living
Knowledge Representation
Reasoning Methods
Decision Support
(Semi-) Automated Monitoring & Diagnosis
Semantic Annotation
Information Retrieval & Search
A m b i e
n t A s s i s t e d L i v i n g
Event Identification
Monitor vital parameters and behavior
of patient in order to recognize critical
situation as early as possible.
Ambient Assisted Living
Short-term SituationUnderstanding
Long-term BehaviorMonitoring
Patient Monitoring Data
Body-worn and ambient sensors
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Automated detection of critical situations
Long-term deviationsUntypical behaviorChanged behaviorChanges movement patternVital data
Acute emergenciesHelplessness (e.g. after fall)No activity (motionlessness)Critical vital parametersManual alarm
Vital dataPulse
Blood pressureWeightBreathing frequencyMovementDistance/speedRoom changes
ADLsSleepPersonal hygienePreparation of mealsToilet usageAnalysisTrendsDeviation from norm
HCM Parameter
Sensor data
Situations
Sensor data of Vital data sensorsEnvironmental sensors
Activity sensors (Position tracking)
EMERGE: Emergency Monitoring and Prevention
Partners: Fraunhofer IESE (Coordinator), Westpfalz Klinikum Kaiserslautern, Siemens,
Microsoft EMIC, Art of Technology, Demokritos, e-ISOTIS, Bay Zoltan Foundation
EU Research Project EMERGE
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Knowledge Base
User ModelHuman Capability Model
Interpretation of Human Behaviorenables Services for Ambient Assisted Living
Long-term Behavior Monitoring
“Untypical” behavior
Changes in activity patterns
User Information Medical pre-conditions and
deficits
User specific reference values
Vital data Pulse rate
Blood pressure
Body weight, …
Behavior parameters Activities of Daily Living
Physical Activity
Motion, …
Assistance
Services
Body-worn and
ambient sensors
I n f o r m a t i o n I n t e g r a t i o n
a n d I n t e r p r e t a t
i o n
Short-term Situation Understanding
Activity Recognition
Emergency Detection
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Rules describes how
Acute critical situations
Long-term deviations
are derived from
Vital data
Activities of daily live
Sleep (day, night)
Personal Hygiene
Toilet Usage (day, night)
Prep. Meals
General Mobility
Rule-based Situation Understanding
Example Pulse rate
Customizedthrough User
Model
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Rule-based Situation Understanding:Dynamic thresholds
Activity of daily live assessment
Example: Parameter “Toilet Usage”
Daily measure (orange)
Personalized region of normality (red)
Calculated dynamically
day -> last week
week -> last month
month -> last half year
Upper limit
Lower limit
ADL Toilet Usage#/day
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Project EMERGE: Field Trial & Evaluation
Duration: 09/2009 – 11/2009
2 test apartments at a retirement home
(Westpfalz Seniorenresidenz in
Kaiserslautern)
Field Trial: Four types of sensors
Emergency detection with rule-basedformalization of Human Capability Models
(10 parameters, ~120 rules)
Implementation of movement monitoring
component
Results are currently evaluated…
PresencePressurePower
Contact
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Evaluation of Human Capability Model
Simulation of persons with
different defects over 250
days
Comparison of expectedalarms set by non-biased
physician to alarmsprovided by HCMassessment
0% 20% 40% 60% 80% 100%
Blood Pressure (sys)
Pulse Rate
Body Weight
Toilet Usage
Bed Occupany
# of interruptions
Preparing Meals
Personal Hygiene
Scenario 3_4: Dehydration
Matching Rate False Positive False Negative
Example: Detection of Dehydration
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Situation Understanding for Ambient Assisted Living
A m b i e n t A s s i s t e d L i v i n g
Event Identification
Monitor vital parameters and behavior
of patient in order to recognize critical
situation as early as possible.
Ambient Assisted Living
Short-term SituationUnderstanding
Long-term BehaviorMonitoring
User ModelHuman Capability
Model
Body-worn and ambient sensors
• More complex and robust situation
understanding algorithms
•Leverage combinations of sensors
•User-specific and activity-specificparameterization of rules
• User acceptance requires minimal
number of false positives and negatives
• Legal regulations (privacy,..)
Future Challenges
• Level of maturity: First prototypes
available
• Monitoring of “simple” situations
• Evaluation phase to be continued
State of the art
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Copyright © Siemens AG 2010. All rights reserved.
Corporate TechnologyAgenda
Motivation
Semantic Technologies
Patient Data Management
Summary
Ambient Assisted Living
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Summary
…and back???
Health Archive
?
• Information overflow in healthcare
systems can be addressed by
leveraging semantic models
• Semantic Search
• increases precision and recall
of patient record retrieval
• Rule-based patient monitoring
• automates the recognition of
critical situations and thus
enables faster response on
emergencies
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Thank you for your attention!
Dr. Steffen Lamparter
Siemens AGGTF Autonomous Systems
Otto-Hahn-Ring 6
81739 München
Phone: 089 - 636 40383
Fax: 089 - 636 41423
E-mail: