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Structuring Medical Records with Apache Stanbol
Rafa Haro, Senior Software Engineer, Athento Antonio Pérez Morales, Senior Software Engineer, Ixxus
• Committer, PMC Member @ Apache Stanbol, Apache ManifoldCF
• Topics: Document Analysis, NLP, Machine Learning, Semantic Technologies, ECM
• Committer @ Apache Stanbol, Apache ManifoldCF
• Topics: ECM, Semantic Search, ETL, Machine Learning
Apache Stanbol provides a set of reusable components for semantic content management. It extends existing CMSs with a number of semantic services.
CMS
Traditional Semantic
Software Architecture for Semantically Enabled CM and ECM systems
Apache Stanbol Story
• Started within FP7 European Project IKS (Interactive Knowledge Stack. 2009 - 2012)
• IKS project brought together an Open Source Community for Defining and Building Platforms in the Semantic CMS Space
• Incubated in November 2010
• Successfully promoted within CMS and ECM industry through IKS Early Adopters Program
• Graduated to Top-Level Apache Project in October 2012
What is a Semantic CMS?
Traditional CMS
Atomic Unit: Document
Properties as meta-data (key-value schemas)
Keyword Search
Document Management Document Types
Document Workflow
Semantic CMS
Atomic Unit: Entity
Semantic meta-data (RDF)
Semantic Search
Knowledge Management Entity Management
Ontologies
Source: What Apache Stanbol Can Do for You?. Fabian Christ. ApacheCon Europe 2012
Key Points• Designed to bring Semantic Technologies to existing CMS
• Non-intrusive set of RESTful ‘Semantic’ Services
• Extremely Modular: Use only the modules you need
• Main Features: • Multilingual Content Enhancement: Structure Content through Semantic
Metadata
• Knowledge Bases Management
• Knowledge Models and Reasoning
• Semantic Indexing and Search
Stanbol Components• Stanbol components provide:
• RESTful API • Java APIs and OSGi services
• Stanbol components do NOT depend on each other • however they can be easily combined to
www.iks-project.eu
Page:
Apache Stanbol Service Layer
Apache StanbolComponent Layer
ApacheStanbol
Reasoners
ApacheStanbol
Enhancer
ApacheStanbol Rules
ApacheStanbol
Ontology Manager
ApacheStanbol
ContentHub
ApacheStanbol
EntityHub
ApacheStanbol
FactStoreStanbolEnhancement
Engines
VIE - User Interface LayerVIE VIE
Widgets
ApacheStanbol
CMS Adapter
Copyright IKS Consortium
6
Service-Oriented View
Stanbol Components (II)• Enhancer: Extracts Knowledge from unstructured parsed content
• EntityHub: Manage Domain Entities and Topics (Knowledge Bases)
• ContentHub: Semantic Indexing / Search over your - semantic enhanced - Content
• CMS Adapter: Sync. your CMS with Apache Stanbol (JCR/CMIS)
• Ontology Manager: Manage you formal Domain Knowledge
• Reasoners & Rules: Apply Domain Knowledge to improve / validate extracted Information. Refactor / refine knowledge to align it to public schemas such as schema.org
Built on Top of Apache….
• Apache Felix as OSGi environment
• Apache Sling launchers and OSGi Tools
• Apache Maven for building
• Apache Clerezza as RDF Framework
• Apache Jena as TripleStore
• Apache Solr for Knowledge Bases Management
• Apache Tika for converting input
• Apache OpenNLP for NLP Processing
Integration Scenarios
Source: What Apache Stanbol Can Do for You?. Fabian Christ. ApacheCon Europe 2012
• Stand-Alone Server (Stanbol Launchers)
• Web Application (Servlet-Container)
• Embedded within an OSGi environment
Project Current Status
Contributions (commits) to Trunk Since Incubation
Incubation (Nov 2010)
Apache Stanbol 0.9.0-incubating
(Aug 2012)
Graduation (October 2012)
IKS Project Ending (Dec 2012)
Apache Stanbol 0.12.0
(March 2014)
Apache Stanbol 1.0.0
(October 2016)
Project Current Status (II)
• 22 PMC Members (Last Addition Jul 2016) • 26 Committers (Last Addition May 2015)
• 3-5 active committers last 2 years • [email protected]: 228 subscribers
• Activity has been gradually decreasing • 3 major releases
Source: Apache Stanbol Committee Report Helper (https://reporter.apache.org/?stanbol)
Stanbol Enhancer
RDF
Stanbol Enhancer (II)
Stanbol Enhancer (III)
Stanbol Enhancement Chains• Define how Content is processed by the Enhancer through an ExecutionPlan • Different Implementations:
• ListChain: in order sequential enhancement engines execution. Parallel Execution of engines not supported
• WeightedChain: ExecutionPlan is calculated using the engines order metadata. Parallel Execution of engines allowed
• API: • /enhancer: executes the default chain • /enhancer/chain/{chain-name}: executes a concrete named chain • /enhancer/engine/{engine-name}: executes a concrete named engine
Current Enhancement Engines• Preprocessing
• Tika Engine • content type detection • text extraction from several document formats • metadata extraction from several document formats
• Natural Language Processing • Language Detection (different implementations) • Sentence Detection (OpenNLP, SmartCN, REST) • Tokenizer (OpenNLP, SmartCN, REST) • POS Tagging (OpenNLP, REST) • Chunking (OpenNLP, REST) • NER (OpenNLP, OpenCalais, REST)
• Entity Linking • Named Entity Linking • EntityHub Linking Engine • FST (Lucene Finit State Transducer) Linking Engine • Entity Co-mention • Commercial Engines (OpenCalais, Zemanta, CELI…)
• Sentiment Analysis • Disambiguation
• DBPedia Spotlight • Solr MLT based
• PostProcessing: • Dereferencing
Stanbol EntityHub
Stanbol EntityHub (II)• Manage Multiple Entity Sources (Knowledge Bases)
• Allows Fast Entity-Lookup using Apache Solr
• Referenced Site (Remote LD + Local Caches) Vs Managed Site (Entity CRUD Api over manually configured Sites)
• API: • Query for Entities (used by Entity Linking Engines)
• CRUD for Managed Sites • LDPath support for:
• Graph Path Retrieval (Used for dereferencing) • Schema Translation • Simple Reasoning
schema:name = rdfs:label[@en];
friend-names = foaf:knows/foaf:name
curl -X POST -d "name=lyon&limit=10" \ http://localhost:8080/entityhub/site/dbpedia/find
Use Case: Hexin Project - Structuring Medical Records
• R&D Project for Sergas (Galician Public Health Office) • Clinical Data Analysis Platform for supporting:
• Clinical Assistance • Epidemiology studies • Medical Research
• Big Data approach for analyzing both structured historical clinical data and unstructured medical records
• Medical Records are written in Spanish and Galician
Hexin: Architecture
Validation AnalysisPatient
Data Source
URX
ETL
BIG DATA (HDFS +
HIVE)
Event Detection Process
Cassandra
Reference Cases Detection Process
New Case
BIPatientId Date Structured Events Semantic Events Symptoms: • Cough • Unrest
Unrest Cough Fever>38
Rules
Hexin: Semantic Tagging
Hexin: Objective
“Paciente diabético desde los 5 años y con EPOC moderada grado 2 de la GOLD”
Hexin:Solution Design
• Structure Medical Records using Apache Stanbol Enhancer • Custom Ontology:
• Symptoms • Diseases • Diagnosis Tests • Family and Personal History
• Custom Enhancement Chain: • Language Detection > NLP > Entity Linking > Negation
Detection > Fact Extraction
Hexin: Ontology
Hexin: Ontology Indexing
• For supporting the Entity Linking process against Hexin Ontology, an EntityHub site must be created
• 2 options: • ManagedSite: full CRUD storage <-> DYNAMIC • ReferencedSite: READ-ONLY remote site + local index
• Stanbol EntityHub Indexing Tool: • RDF —> JenaTDB —> Solr Index
• Configure Custom Namespaces, Mappings and Properties • Generates an OSGi Bundle with the Yard and YardSite default
configurations • Copy the index to Stanbol /datafiles folder and install the bundle
using Apache Felix OSGi Web Console
hexin:*hexin:label > rdfs:label
NegexFact Extract.Hexin Linking
Hexin: Enhancement Chain
OpenNLP-ChunkerOpenNLP-POSOpenNLP-TokenOpenNLP-Sent.Lang. Detect.
Custom Hexin Engine. Implemented for the project
Entity Linking Engine. Available in Stanbol with a Custom Configuration for this use case
NLP Engines. Available in Stanbol. Default Configuration
Pre-Processing Engine. Available in Stanbol
Hexin: Linking
Hexin: Linking (II)
Hexin: Custom Engines
@Component@Servicepublic class MyEngine implements EnhancementEngine {
@Activate public void activate(ComponentContext c) { // initialize, configure, ... }
public int canEnhance(ContentItem item) { if(...item matches our expectations...) { return ENHANCE_SYNCHRONOUS; } else { return CANNOT_ENHANCE; } }
public void computeEnhancements(ContentItem item) { // run the engine and add results to item’s // RDF graph based on the item’s InputStream }}
maven-bundle-plugin
adds OSGI metadata
Maven build
maven-scr-pluginadds services metadata
registered by OSGi
MyEngineService
MANIFEST.MF
OSGi metadata
OSGi bundle
Install in Stanbol no restart
needed
NLP at Apache Stanbol
NLP at Apache Stanbol (II)• Browsable Map with Spans
• Spans sorted by Natural Order • Iterator based API that allows
concurrent Modifications • Annotations supported at Spans Level
• POS Annotation • PosTag
tag (e.g. NE) lexical category (e.g. Noun)
• Phrase Annotation (chunks) • PhraseTag
tag (e.g. NP) lexical-category (e.g. NounPhrase)
• Sentiment Annotation • SentimentTag:: Double
Stanbol is an Amazing Tool
Sentence
Chunk
Token
Span Types: • Token • Chunk • Sentence • Text Section • Analyzed Text
Hexin Custom Engine: Negex
• Context/Negex: Algorithm for Negation Detection • Based on Triggers-Terms + Regex
Chapman WW, Bridewell W, Hanbury P, Cooper GF, Buchanan BG. A simple algorithm for identifying negated findings and diseases in discharge summaries. J Biomed Inform. Oct 2001;34(5):301-310.
public abstract class AbstractNegexDetector implements NegexDetector {
@Overridepublic Set<IRI> detectNegations(String language, Graph metadata, AnalysedText at) throws NegexException{}
protected abstract boolean isNegated(String language, String concept, String sentence);
}
Hexin Custom Engine: Negex (II)• Triggers Types:
• Pre-condition Negation terms (e.g. absence of) • Pseudo Negation terms (e.g. no increase) • Pre-condition possibility phrase (e.g. rule him out) • Post-condition negation terms (e.g. unlikely) • Termination terms (e.g. but, however)
• Implementation available under Apache License 2.0 • Engine Implementation Challenges:
• Entity Annotations as Targets • AnalyzedText and EntityAnnotations relationships are currently obfuscated • GLUE CODE for locating Entity Annotations Spans by using START - END Text
Annotations properties • Once Entity Annotation sentence is located, is used as context along with the Entity
surface-form (mention) for applying the algorithm • Negation Returned as a Custom Property for the TextAnnotation (negated = True or False)
Hexin Custom Engine: Fact Extraction
“Paciente diabético desde los 5 años y con EPOC moderada grado 2 de la GOLD”
Hexin Custom Engine: Fact Extraction (II)
• In-Context Entity Fact Extraction • Facts returned as Entity RDF Metadata like the rest of Entity
Properties • Different Implementations of Context (all extracted from
AnalyzedText structure) • Sentence Context (default and usually enough) • Window of Text Context • Paragraph Context
• Rule Based Approach: • Regex over RAW Text or POS tags Sequence
• ENTITY reserved word -> OR expression for all ENTITY labels
Hexin Custom Engine: Fact Extraction (III)
• Supported Expressions: • diabetes|diabético|DM desde los N años • diabetes|diabético|DM a los N años • Debut diabetes|diabético|DM a los N años
Hexin Custom Engine: Fact Extraction (IV)
• POS based Rules: Diabetes diagnosed when he was 5 years old
NNS VB WRB PRP VBD CD NNS JJ ENTITY \s VB * VB[be] (CD) years old or simply
ENTITY \s VB * VB[be] (CD)
Thanks for your attention!