Monday, August 25, 2014

Ontologies for Addiction and Mental Disease: Enabling Translational Research and Clinical Decision Support

In a previous post titled Why do we need ontologies in healthcare applications, I elaborated on what ontologies are and why they are different from information models of data structures like relational database schemas and XML schemas commonly used in healthcare informatics applications. In this post, I discuss two interesting applications of ontology engineering related to addiction and mental disease treatment. The first is the use of ontologies for achieving semantic interoperability in  translational research. The second is the use of ontologies for modeling complex medical knowledge in clinical practice guidelines (CPGs) for the purpose of automated reasoning during execution in clinical decision support systems (CDS) at the point of care.

Why Semantic Interoperability is needed in biomedical translational research?


In order to accelerate the discovery of new effective therapeutics for mental health and addiction treatment, there is a need to integrate data across disciplines spanning biomedical research and clinical care delivery [1]. For example, linking data across disciplines can facilitate a better understanding of treatment response variability among patients in addiction treatment. These disciplines include:

  • Genetics, the study of genes.
  • Chemistry, the study of chemical compounds including substances of abuse like heroin.
  • Neuroscience, the study of the nervous system and the brain (addiction is a chronic disease of the brain)
  • Psychiatry which is focused on the diagnosis, treatment, and prevention of addiction and mental disorders.

Each of these disciplines has its own terminology or controlled vocabularies. In the clinical domain for example, DSM5 and RrxNorm are used for documenting clinical care. In biomedical research, several ontologies have been developed over the last few years including:
  • The Gene Ontology (GO)
  • The Chemical Entities of Biological Interest Ontology (CHEBI)
  • NeuroLex, an OWL ontology covering major domains of neuroscience: anatomy, cell, subcellular, molecule, function, and dysfunction.

To facilitate semantic interoperability between these ontologies, there are best practices established by the Open Biomedical Ontology (OBO) community. An example of best practice is the use of an upper-level ontology called the Basic Formal Ontology (BFO) which acts as a common foundational ontology upon which  new ontologies can be created. OBO ontologies and principles are available on the OBO Foundry web site.

Among the ontologies available on the OBO Foundry is the Mental Functioning Ontology (MF) [2, 3]. The MF is being developed as a collaboration between the University of Geneva in Switzerland and the University at Buffalo in the United States. The project also includes a Mental Disease Ontology (MD) which extends the MF and the Ontology for General Medical Science (OGMS). The Basic Formal Ontology (BFO) is an upper-level ontology for both the MF and the OGMS. The picture below is a view of the class hierarchy of the MD showing details of the class "Paranoid Schizophrenia" in the right pane of the windows of the beta release of Protege 5, an open source ontology development environment (click on the image to enlarge it).

The following is a tree view of the "Mental Disease Course" class (click on the image to enlarge it):



Ontology constructs defined by the OWL2 language can help establish common semantics (meaning) and relationships between entities across domains. These constructs provide automated inferencing capabilities such as equivalence (e.g., owl:sameAs and owl:equivalentClass) and subsumption (e.g., rdfs:subClassOf) relationships between entities.

In addition, publishing data sources following Linked Open Data (LOD) principles and semantic search using federated SPARQL queries can help answer new research questions. Another application is semantic annotation for natural language processing (NLP) applications.

 

Ontologies as knowledge representation formalism for clinical decision support (CDS)


As knowledge representation formalism, ontologies are well suited for modeling complex medical knowledge and can facilitate reasoning during the automated execution of clinical practice guidelines (CPGs) and Care Pathways (CPs) based on patient data at the point of care. Several approaches to modelling CPGs and CPs have been proposed in the past including PROforma, HELEN, EON, GLIF, PRODIGY, and SAGE. However, the lack of free and open source tooling has been a major impediment to a wide adoption of these knowledge representation formalisms. OWL has the advantage of being a widely implemented W3C Recommendation with available mature open source  tools.

In practice, the medical knowledge contained in CPGs can be manually translated into IF-THEN statements in most programming languages. Executable CDS rules (like other complex types of business rules) can be implemented with a production rule engine using forward chaining. This is the approach taken by OpenCDS and some large scale CDS implementations in real world healthcare delivery settings. This allows CDS software developers to externalize the medical knowledge contained in clinical guidelines in the form of declarative rules as opposed to embedding that knowledge in procedural code. Many viable open source business rule management systems (BRMS) are available today and provide capabilities such as a rule authoring user interface, a rules repository, and a testing environment.

However, production rule systems have a limitation. They do not scale because they require writing a rule for each clinical concept code (there are more than 311,000 active concepts in SNOMED CT alone). An alternative is to exploit the class hierarchy in an ontology so that subclasses of a given superclass can inherit the clinical rules that are applicable to the superclass (this is called subsumption). In addition to subsumption, an OWL ontology also support reasoning with description logic (DL) axioms [4].

An ontology designed for a clinical decision support (CDS) system can integrate the clinical rules from a CPG, a domain ontology like the Mental Disorder (MD) ontology, and the patient medical record from an EHR database in order to provide inferences in the form of treatment recommendations at the point of care. The OWL API [5] facilitates the integration of ontologies into software applications. It supports inferencing using reasoners like Pellet and HermiT. OWL2 reasoning capabilites can be enhanced with rules represented in SWRL (Semantic Web Rule Language) which is implemented by reasoners like Pellet as well as the Protege OWL development environement. In addition to inferencing, another benefit of an OWL-based approach is transparency: the CDS system can provide an explanation or justification of how it arrives at the treatment recommendations.

Nonetheless, these approaches are not mutually exclusive: a production rule system can be integrated with business processes, ontologies, and predictive analytics models. Predictive analytics models provide a probabilistic approach to treatment recommendations to assist in the clinical decision making process.

References


[1]  Janna Hastings, Werner Ceusters, Mark Jensen, Kevin Mulligan and Barry Smith. Representing mental functioning: Ontologies for mental health and disease. Proceedings of the Mental Functioning Ontologies workshop of ICBO 2012, Graz, Austria.

[2]  Ceusters, W. and Smith, B. (2010a). Foundations for a realist ontology of mental disease. Journal of Biomedical Semantics, 1(1), 10.

[3] Hastings, J., Smith, B., Ceusters, W., and Mulligan, K. (2012). The mental functioning ontology. http://code.google.com/p/mental-functioning-ontology/, last accessed August 24, 2014

[4] Sesen MB, Peake MD, Banares-Alcantara R, Tse D, Kadir T, Stanley R, Gleeson F, Brady M. 2014 Lung Cancer Assistant: a hybrid clinical decision support application for lung cancer care. J. R. Soc. Interface 11: 20140534.

[5] Matthew Horridge, Sean Bechhofer. The OWL API: A Java API for OWL Ontologies Semantic Web Journal 2(1), Special Issue on Semantic Web Tools and Systems, pp. 11-21, 2011.

Sunday, August 17, 2014

Natural Language Processing (NLP) for Clinical Decision Support: A Practical Approach

A significant portion of the electronic documentation of clinical care is captured in the form of unstructured narrative text like psychotherapy and progress notes. Despite the big push to adopt structured data entry (as required by the Meaningful Use incentive program for example), many clinicians still like to document care using free narrative text. The advantage of using narrative text as opposed to coded entries is that narrative text can tell the story of the patient and the care provided particularly in complex cases. My opinion is that free narrative text should be used to complement coded entries when necessary to capture relevant information.

Furthermore, medical knowledge is expanding very rapidly. For example, PubMed has more than 24 millions citations for biomedical literature from MEDLINE, life science journals, and online books. It is impossible for the human brain to keep up with that amount of knowledge. These unstructured sources of knowledge contain the scientific evidence that is required for effective clinical decision making in what is referred to as Evidence-Based Medicine (EBM).

In this blog, I discuss two practical applications of Natural Language Processing (NLP). The first is the use of NLP tools and techniques to automatically extract clinical concepts and other insight from clinical notes for the purpose of providing treatment recommendations in Clinical Decision Support (CDS) systems. The second is the use of text analytics techniques like clustering and summarization for Clinical Question Answering (CQA).

The emphasis of this post is on a practical approach using freely available and mature open source tools as opposed to an academic or theoretical approach. For a theoretical treatment of the subject, please refer to the book Speech and Language Processing by Daniel Jurafsky and James Martin.


Clinical NLP with Apache cTAKES


Based on the Apache Unstructured Information Management Architecture (UIMA) framework and the Apache OpenNLP natural language processing toolkit, Apache cTAKES provides a modular architecture utilizing both rule-based and machine learning techniques for information extraction from clinical notes. cTAKES can extract named entities (clinical concepts) from clinical notes in plain text or HL7 CDA format and map these entities to various dictionaries including the following Unified Medical Language System (UMLS) semantic types: diseases/disorders, signs/symptoms, anatomical sites, procedures, and medications.

cTAKES includes the following key components which can be assembled to create processing pipelines:

  • Sentence boundary detector based on the OpenNLP Maximum Entropy (ME) sentence detector.
  • Tokenizor
  • Normalizer using the National Library of Medicine's Lexical Variant Generation (LVG) tool
  • Part-of-speech (POS) tagger
  • Shallow parser
  • Named Entity Recognition (NER) annotator using dictionary look-up to UMLS concepts and semantic types. The Drug NER can extract drug entities and their attributes such as dosage, strength, route, etc.
  • Assertion module which determines the subject of the statement (e.g., is the subject of the statement the patient or a parent of the patient) and whether a named entity or event is negated (e.g., does the presence of the word "depression" in the text implies that the patient has depression).
Apache cTAKES 3.2 has added YTEX, a set of extensions developed at Yale University which provide integration with MetaMap, semantic similarity, export to Machine Learning packages like Weka and R, and feature engineering.

The following diagram from the Apache cTAKES Wiki provides an overview of these components and their dependencies (click to enlarge):


Massively Parallel Clinical Text Analytics in the Cloud with GATECloud


The General Architecture for Text Engineering (GATE) is a mature, comprehensive, and open source text analytics platform. GATE is a family of tools which includes:

  • GATE Developer: an integrated development environment (IDE) for language processing components with a comprehensive set of available plugins called CREOLE (Collection of REusable Objects for Language Engineering). 
  • GATE Embedded: an object library for embedding services developed with GATE Developer into third-party applications.
  • GATE Teamware: a collaborative semantic annotation environment based on a workflow engine for creating manually annotated corpora for applying machine learning algorithms. 
  • GATE M√≠mir: the "Multi-paradigm Information Management Index and Repository" which supports a multi-paradigm approach to index and search over text, ontologies, and semantic metadata.
  • GATE Cloud: a massively parallel clinical text analytics platform (Platform as a Service or PaaS) built on the Amazon AWS Cloud.
What makes GATE particularly attractive is the recent addition of GATECloud.net PaaS which can boost the productivity of people involved in large scale text analytics tasks.

 

Clustering, Classification, Text Summarization, and Clinical Question Answering (CQA)

 

An unsupervised machine learning approach called Clustering can be used to classify large volumes of medical literature into groups (clusters) based on some similarity measure (such as the Euclidean distance). Clustering can be applied at the document, search result, and word/topic levels. Carrot2 and Apache Mahout are open source projects that provide several methods for document clustering. For example, the Latent Dirichlet Allocation learning algorithm in Apache Mahout automatically clusters words into topics and documents into mixtures of topics. Other clustering algorithms in Apache Mahout include: Canopy, Mean-Shift, Spectral, K-Means and Fuzzy K-Means. Apache Mahout is part of the Hadoop ecosystem and can therefore scale to very large volumes of unstructured text.

Document classification essentially consists in assigning predefined set of labels to documents. This can be achieved through supervised machine learning algorithms. Apache Mahout implements the Naive Bayes classifier.

Text summarization techniques can be used to present succinct and clinically relevant evidence to clinicians at the point of care. MEAD (http://www.summarization.com/mead/) is an open source project that implements multiple summarization algorithms. In the biomedical domain, SemRep is a program that extracts semantic predications (subject-relation-object triples) from biomedical free text. Subject and object arguments of each predication are concepts from the UMLS Metathesaurus and the relation is from the UMLS Semantic Network (e.g., TREATS, Co-OCCURS_WITH). The SemRep summarization provides a short summary of these concepts and their semantic relations.

AskHermes (Help clinicians to Extract and aRrticulate Multimedia information for answering clinical quEstionS) is a project that attempts to implement these techniques in the clinical domain. It allows clinicians to enter questions in natural language and uses the following unstructured information sources: MEDLINE abstracts, PubMed Central full-text articles, eMedicine documents, clinical guidelines, and Wikipedia articles.

The processing pipeline in AskHermes includes the following: Question Analysis, Related Questions Extraction, Information Retrieval, Summarization and Answer Presentation. AskHermes performs question classification using MMTx (MetaMap Technology Transfer) to map keywords to UMLS concepts and semantic types. Classification is achieved through supervised machine learning algorithms such as Support Vector Machine (SVM) and conditional random fields (CFRs). Summarization and answer presentation are based on clustering techniques. AskHermes is powered by open source components including: JBoss Seam, Weka, Mallet , Carrot2 , Lucene/Solr, and WordNet (a lexical database for the English language).

Saturday, August 9, 2014

Enabling Scalable Realtime Healthcare Analytics with Apache Spark


Modern and massively parallel computing platforms can process humongous amounts of data in real time to obtain actionable insights for effective clinical decision making. In this blog, I discuss an emerging Big Data platform called Apache Spark and its application to remote real-time healthcare monitoring using data from medical devices and wearable sensors. The goal is to provide effective remote care for an increasingly aging population as well as public health surveillance.


The Apache Spark Framework


Apache Spark has emerged during the last couple of years as an innovative platform for Big Data and in-memory cluster computing capable of running programs up to 100x faster than traditional Hadoop MapReduce. Apache Spark is written in Scala, a functional programming language (see my previous post titled Navigating in Scala land). Spark also offers a Java and a Python APIs. The Scala API allows developers to interact with Spark by using very concise and expressive Scala code.






The Spark stack also includes the following integrated tools:

  • Spark SQL which allows relational queries expressed in SQL, HiveQL, or Scala to be executed using Spark through a data abstraction called SchemaRDD. Supported data sources include Parquet files (a columnar storage format for Hadoop), JSON datasets, or data stored in Apache Hive.

  • Spark Streaming which enables fault-tolerant stream processing of live data streams. Data can be ingested from many sources like Kafka, Flume, Twitter, ZeroMQ or plain old TCP sockets. The ingested data can be directly processed with Spark built-in Machine Learning algorithms.

  • MLlib (Machine Learning Library) provides a library of practical Machine Learning algorithms including support vector machines (SVM), logistic regression, decision trees, naive Bayes, and k-means clustering.

  • GraphX which provides graph-parallel computation for graph-analytics application like social networks.


Apache Spark can also play nicely with other frameworks within the Hadoop ecosystem. For example, it can run standalone or on a Hadoop 2's YARN cluster manager, on Amazon EC2 or a Mesos cluster manager. Spark can also read data from HFDS, HBase, Cassandra or any other Hadoop data source. Other noteworthy integrations include:

  • SparkR, an R package allowing the use of Spark from R, a very popular open source software environment for statistical computing with more that 5800 packages including Machine Learning packages; and

  • H2O-Sparkling which provides an integration with the H2O platform through in-memory sharing with Tachyon, a memory-centric distributed file system for data sharing across cluster frameworks. This allows Spark applications to leverage advanced distributed Machine Learning algorithms supported by the H2O platform like emerging Deep Learning algorithms.

 

Wearable Sensors for Remote Healthcare Monitoring 


Three factors are contributing to the availability of massive amounts of clinical data: the rising adoption of EHRs by providers thanks in part to the Meaningful Use incentive program; the increasing use of medical devices including wearable sensors used by patients outside of healthcare facilities; and medical knowledge (for example in the form of medical research literature).

One promising area in Healthcare Informatics where Big Data architectures like the one provided by Apache Spark can make a difference is in applications using data from wearable health monitoring sensors for anomaly detection, care alerting, diagnosis, care planning, and prediction. For example, anomaly detection can be performed at scale using the k-means clustering machine learning algorithm in Spark.

These sensors and devices are part of a larger trend called the "Internet of Things". They enable new capabilities such as remote health monitoring for personalized medicine and chronic care management for an increasingly aging population as well as public health surveillance for outbreaks and epidemics.

Wearable sensors can collect vital signs data like weight, temperature, blood pressure (BP), heart rate (HR), blood glucose (BG), respiratory rate (RR), electrocardiogram (ECG), oxygen saturation (SpO2), and Photoplethysmography (PPG). Spark Streaming can be used to perform real-time stream processing on sensors data and the data can be processed and analyzed using the Machine Learning algorithms available in MLlib and the other integrated frameworks like R and H2O. What makes Spark particularly suitable for this type of applications is that sensor data meet the Big Data criteria of volume, velocity, and variety.

Researchers predict that internet use on mobile phones will increase 20-fold in Africa in the next five years. The number of mobile subscriptions in sub-Saharan Africa is expected to reach 635 millions by the end of this year. This unprecedented level of connectivity (fueled in part by the historical lack of land line infrastructure) provides opportunities for effective public health surveillance and disease management in the developing world.

Apache Spark is the type of open source computing infrastructure that is needed for distributed, scalable, and real-time healthcare analytics for reducing healthcare costs and improving outcomes.