Structuring Medical Records with Apache Stanbol. Rafa Haro, Senior Software Engineer, Athento Antonio Pérez Morales, Senior Software Engineer, Ixxus

Transcription

1 Structuring Medical Records with Apache Stanbol Rafa Haro, Senior Software Engineer, Athento Antonio Pérez Morales, Senior Software Engineer, Ixxus

2 Committer, PMC Apache Stanbol, Apache ManifoldCF Topics: Document Analysis, NLP, Machine Learning, Semantic Technologies, ECM Apache Stanbol, Apache ManifoldCF Topics: ECM, Semantic Search, ETL, Machine Learning

3 Apache Stanbol provides a set of reusable components for semantic content management. It extends existing CMSs with a number of semantic services. Traditional Semantic CMS

4 Software Architecture for Semantically Enabled CM and ECM systems

5 Apache Stanbol Story Started within FP7 European Project IKS (Interactive Knowledge Stack ) 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

6 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

7 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

8 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 Apache Stanbol Enhancer Apache Stanbol EntityHub Apache Stanbol Ontology Manager Apache Stanbol Reasoners Stanbol Enhancement Engines Apache Stanbol ContentHub Apache Stanbol FactStore Apache Stanbol CMS Adapter Apache Stanbol Rules Apache Stanbol Component Layer

9 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

10 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

11 Integration Scenarios Stand-Alone Server (Stanbol Launchers) Web Application (Servlet-Container) Embedded within an OSGi environment Source: What Apache Stanbol Can Do for You?. Fabian Christ. ApacheCon Europe 2012

12 Project Current Status Incubation (Nov 2010) Apache Stanbol incubating (Aug 2012) Graduation (October 2012) IKS Project Ending (Dec 2012) Apache Stanbol (March 2014) Apache Stanbol (October 2016) Contributions (commits) to Trunk Since Incubation

13 Project Current Status (II) 22 PMC Members (Last Addition Jul 2016) 26 Committers (Last Addition May 2015) 3-5 active committers last 2 years dev@stanbol.apache.org: 228 subscribers Activity has been gradually decreasing 3 major releases Source: Apache Stanbol Committee Report Helper (

14 Stanbol Enhancer RDF

15 Stanbol Enhancer (II)

16 Stanbol Enhancer (III)

17 Stanbol Enhancement Chains Define how Content is processed by the Enhancer through an ExecutionPlan Different Implementations: API: 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 /enhancer: executes the default chain /enhancer/chain/{chain-name}: executes a concrete named chain /enhancer/engine/{engine-name}: executes a concrete named engine

18 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

19 Stanbol EntityHub

20 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) curl -X POST -d "name=lyon&limit=10" \ CRUD for Managed Sites LDPath support for: Graph Path Retrieval (Used for dereferencing) Schema Translation Simple Reasoning friend-names = foaf:knows/foaf:name schema:name = rdfs:label[@en];

21 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

22 Hexin: Architecture Event Detection Process ETL URX BIG DATA (HDFS + HIVE) Data Source PatientId Date Structured Events Semantic Events Symptoms: Cough Unrest Reference Cases Detection Process New Case BI Rules Cassandra Unrest Cough Fever>38 Patient Validation Analysis

23 Hexin: Semantic Tagging

24 Hexin: Objective Paciente diabético desde los 5 años y con EPOC moderada grado 2 de la GOLD

25 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

26 Hexin: Ontology

27 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 hexin:* hexin:label > rdfs:label 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

28 Hexin: Enhancement Chain Lang. Detect. OpenNLP-Sent. OpenNLP-Token OpenNLP-POS OpenNLP-Chunker Hexin Linking Fact Extract. Negex 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

29 Hexin: Linking

30 Hexin: Linking (II)

31 Hexin: public class MyEngine implements EnhancementEngine public void activate(componentcontext c) { // initialize, configure,... } public int canenhance(contentitem item) { if(...item matches our expectations...) { return ENHANCE_SYNCHRONOUS; } else { return CANNOT_ENHANCE; } } Maven build maven-bundleplugin adds OSGI metadata maven-scr-plugin adds services metadata OSGi bundle MANIFEST.MF OSGi metadata registered by OSGi MyEngine Service } public void computeenhancements(contentitem item) { // run the engine and add results to item s // RDF graph based on the item s InputStream } Install in Stanbol no restart needed

32 NLP at Apache Stanbol

33 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 Token Sentence Chunk Span Types: Token Chunk Sentence Text Section Analyzed Text

34 Hexin Custom Engine: Negex Context/Negex: Algorithm for Negation Detection Based on Triggers-Terms + Regex public abstract class AbstractNegexDetector implements NegexDetector public Set<IRI> detectnegations(string language, Graph metadata, AnalysedText at) throws NegexException{} protected abstract boolean isnegated(string language, String concept, String sentence); } 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):

35 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)

36 Hexin Custom Engine: Fact Extraction Paciente diabético desde los 5 años y con EPOC moderada grado 2 de la GOLD

37 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

38 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

39 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)

40 Thanks for your attention!