From Text to Knowledge: a Unifying Document-Centered View of Analyzed Medical Language

Although medical language processing (MLP) has achieved some success, the actual use and dissemination of data extracted from free text by MLP systems is still very limited. We claim that the adoption of an ‘enricheddocument’ paradigm (or ‘document-centered’ view) can help to address this issue. We present this paradigm and explain how it can be implemented, then discuss its expected benefits both for end-users and MLP researchers.

Issues in the Structuring and Acquisition of an Ontology for Medical Language Understanding

Medical natural language understanding basically aims at representing the contents of medical texts in a formal, conceptual representation. The understanding process itself increasingly relies on a body of domain knowledge, generally expressed in the same conceptual formalism. The design of such a conceptual representation is a key knowledge-acquisition issue. When representing knowledge, the most important point is to ensure that the formal exploitation of the knowledge representation conforms to its meaning in the domain. We examined some methodological and theoretical principles to enforce this conformity. These principles result from our experience in MENELAS, a medical language understanding project.

Knowledge Representation and Management, It's Time to Integrate!

Objectives: To select, present, and summarize the best papers published in 2016 in the field of Knowledge Representation and Management (KRM). Methods: A comprehensive and standardized review of the medical informatics literature was performed based on a PubMed query. Results: Among the 1,421 retrieved papers, the review process resulted in the selection of four best papers focused on the integration of heterogeneous data via the development and the alignment of terminological resources. In the first article, the authors provide a curated and standardized version of the publicly available US FDA Adverse Event Reporting System. Such a resource will improve the quality of the underlying data, and enable standardized analyses using common vocabularies. The second article describes a project developed in order to facilitate heterogeneous data integration in the i2b2 framework. The originality is to allow users integrate the data described in different terminologies and to build a new repository, with a unique model able to support the representation of the various data. The third paper is dedicated to model the association between multiple phenotypic traits described within the Human Phenotype Ontology (HPO) and the corresponding genotype in the specific context of rare diseases (rare variants). Finally, the fourth paper presents solutions to annotation-ontology mapping in genome-scale data. Of particular interest in this work is the Experimental Factor Ontology (EFO) and its generic association model, the Ontology of Biomedical AssociatioN (OBAN). Conclusion: Ontologies have started to show their efficiency to integrate medical data for various tasks in medical informatics: electronic health records data management, clinical research, and knowledge-based systems development.

Towards ontology-based decision support systems for complex ultrasound diagnosis in obstetrics and gynecology

INTRODUCTION

We have developed a new knowledge base intelligent system for obstetrics and gynecology ultrasound imaging, based on an ontology and a reference image collection. This study evaluates the new system to support accurate annotations of ultrasound images. We have used the early ultrasound diagnosis of ectopic pregnancies as a model clinical issue.

MATERIAL AND METHODS

The ectopic pregnancy ontology was derived from medical texts (4260 ultrasound reports of ectopic pregnancy from a specialist center in the UK and 2795 Pubmed abstracts indexed with the MeSH term "Pregnancy, Ectopic") and the reference image collection was built on a selection from 106 publications. We conducted a retrospective analysis of the signs in 35 scans of ectopic pregnancy by six observers using the new system.

RESULTS

The resulting ectopic pregnancy ontology consisted of 1395 terms, and 80 images were collected for the reference collection. The observers used the knowledge base intelligent system to provide a total of 1486 sign annotations. The precision, recall and F-measure for the annotations were 0.83, 0.62 and 0.71, respectively. The global proportion of agreement was 40.35% 95% CI [38.64-42.05].

DISCUSSION

The ontology-based intelligent system provides accurate annotations of ultrasound images and suggests that it may benefit non-expert operators. The precision rate is appropriate for accurate input of a computer-based clinical decision support and could be used to support medical imaging diagnosis of complex conditions in obstetrics and gynecology.

Efficient Results in Semantic Interoperability for Health Care. Findings from the Section on Knowledge Representation and Management

OBJECTIVES

To summarize excellent current research in the field of Knowledge Representation and Management (KRM) within the health and medical care domain.

METHOD

We provide a synopsis of the 2016 IMIA selected articles as well as a related synthetic overview of the current and future field activities. A first step of the selection was performed through MEDLINE querying with a list of MeSH descriptors completed by a list of terms adapted to the KRM section. The second step of the selection was completed by the two section editors who separately evaluated the set of 1,432 articles. The third step of the selection consisted of a collective work that merged the evaluation results to retain 15 articles for peer-review.

RESULTS

The selection and evaluation process of this Yearbook's section on Knowledge Representation and Management has yielded four excellent and interesting articles regarding semantic interoperability for health care by gathering heterogeneous sources (knowledge and data) and auditing ontologies. In the first article, the authors present a solution based on standards and Semantic Web technologies to access distributed and heterogeneous datasets in the domain of breast cancer clinical trials. The second article describes a knowledge-based recommendation system that relies on ontologies and Semantic Web rules in the context of chronic diseases dietary. The third article is related to concept-recognition and text-mining to derive common human diseases model and a phenotypic network of common diseases. In the fourth article, the authors highlight the need for auditing the SNOMED CT. They propose to use a crowdbased method for ontology engineering.

CONCLUSIONS

The current research activities further illustrate the continuous convergence of Knowledge Representation and Medical Informatics, with a focus this year on dedicated tools and methods to advance clinical care by proposing solutions to cope with the problem of semantic interoperability. Indeed, there is a need for powerful tools able to manage and interpret complex, large-scale and distributed datasets and knowledge bases, but also a need for user-friendly tools developed for the clinicians in their daily practice.

Knowledge Representation and Management. From Ontology to Annotation. Findings from the Yearbook 2015 Section on Knowledge Representation and Management

OBJECTIVE

To summarize the best papers in the field of Knowledge Representation and Management (KRM).

METHODS

A comprehensive review of medical informatics literature was performed to select some of the most interesting papers of KRM published in 2014.

RESULTS

Four articles were selected, two focused on annotation and information retrieval using an ontology. The two others focused mainly on ontologies, one dealing with the usage of a temporal ontology in order to analyze the content of narrative document, one describing a methodology for building multilingual ontologies.

CONCLUSION

Semantic models began to show their efficiency, coupled with annotation tools.

Managing free text for secondary use of health data

OBJECTIVE

To summarize the best papers in the field of Knowledge Representation and Management (KRM).

METHODS

A comprehensive review of medical informatics literature was performed to select some of the most interesting papers of KRM and natural language processing (NLP) published in 2013.

RESULTS

Four articles were selected, one focuses on Electronic Health Record (EHR) interoperability for clinical pathway personalization based on structured data. The other three focus on NLP (corpus creation, de-identification, and co-reference resolution) and highlight the increase in NLP tools performances.

CONCLUSION

NLP tools are close to being seriously concurrent to humans in some annotation tasks. Their use could increase drastically the amount of data usable for meaningful use of EHR.

Knowledge Representation and Management: Towards an Integration of a Semantic Web in Daily Health Practice

Objective: To summarize the best papers in the field of Knowledge Representation and Management (KRM).

Methods: A synopsis of the four selected articles for the IMIA Yearbook 2013 KRM section is provided, as well as highlights of current KRM trends, in particular, of the semantic web in daily health practice. The manual selection was performed in three stages: first a set of 3,106 articles, then a second set of 86 articles followed by a third set of 15 articles, and finally the last set of four chosen articles.

Results: Among the four selected articles (see Table 1), one focuses on knowledge engineering to prevent adverse drug events; the objective of the second is to propose mappings between clinical archetypes and SNOMED CT in the context of clinical practice; the third presents an ontology to create a question-answering system; the fourth describes a biomonitoring network based on semantic web technologies.

Conclusion: These four articles clearly indicate that the health semantic web has become a part of daily practice of health professionals since 2012. In the review of the second set of 86 articles, the same topics included in the previous IMIA yearbook remain active research fields: Knowledge extraction, automatic indexing, information retrieval, natural language processing, management of health terminologies and ontologies.

Knowledge representation and management: towards an integration of a semantic web in daily health practice

OBJECTIVE

To summarize the best papers in the field of Knowledge Representation and Management (KRM).

METHODS

A synopsis of the four selected articles for the IMIA Yearbook 2013 KRM section is provided, as well as highlights of current KRM trends, in particular, of the semantic web in daily health practice. The manual selection was performed in three stages: first a set of 3,106 articles, then a second set of 86 articles followed by a third set of 15 articles, and finally the last set of four chosen articles.

RESULTS

Among the four selected articles (see Table 1), one focuses on knowledge engineering to prevent adverse drug events; the objective of the second is to propose mappings between clinical archetypes and SNOMED CT in the context of clinical practice; the third presents an ontology to create a question-answering system; the fourth describes a biomonitoring network based on semantic web technologies.

CONCLUSION

These four articles clearly indicate that the health semantic web has become a part of daily practice of health professionals since 2012. In the review of the second set of 86 articles, the same topics included in the previous IMIA yearbook remain active research fields: Knowledge extraction, automatic indexing, information retrieval, natural language processing, management of health terminologies and ontologies.

Rare diseases knowledge management: the contribution of proximity measurements in OntoOrpha and OMIM

In this paper, we introduce an application of Proxima and define a new measure of proximity between two concepts present in an ontology. The approach is based on the three dimensions of a conceptualization: intention with relations between concepts, expression with terms denoting concepts, and extension with instances of concepts. This preliminary work, in the field of rare diseases, involved the Orphanet Ontology of Rare Diseases (OntoOrpha) and corpus of texts extracted from Online Inheritance in Man (OMIM). The proximity measurements are consistent with an appropriate representation of groups of diseases in the ontology, which are derived from the Orphanet classifications of rare diseases. Other semantic relations are explored and new perspectives in medical knowledge curation are proposed.

[Representation of knowledge in respiratory medicine: ontology should help the coding process]

INTRODUCTION

Access to medical knowledge is a major issue for health professionals and requires the development of terminologies. The objective of the reported work was to construct an ontology of respiratory medicine, i.e. an organized and formalized terminology composed by specific knowledge. The purpose is to help the medico-economical coding process and to represent the relevant knowledge about the patient.

METHODS

Our researches cover the whole life cycle of an ontology, from the development of a methodology, to building it from texts, to its use in an operational system. A computerized tool, based on the ontology, allows both a medico-economical coding and a graphical medical one. This second one will be used to index hospital reports.

RESULTS

Our ontology counts 1913 concepts and contains all the knowledge included in the PMSI part of the SPLF thesaurus. Our tool has been evaluated and showed a recall of 80% and an accuracy of 85% regarding the medico-economical coding.

CONCLUSION

The work presented in this paper justifies the approach that has been used. It must be continued on a large scale to validate our coding principles and the possibility of making enquiries on patient reports concerning clinical research.

Use of electronic health records to evaluate practice individualization

Medical decisions are usually evaluated by checking their compliance with guidelines. We propose an approach to determine to which extent and how decisions are individualized to patients' particular needs, beyond or against guidelines. For this purpose, we have to link the content of electronic health records with a specific ontology to allow formal and detailed representations of cases.

From text to knowledge: a unifying document-centered view of analyzed medical language

Although medical language processing (MLP) has achieved some success, the actual use and dissemination of data extracted from free text by MLP systems is still very limited. We claim that the adoption of an 'enriched-document' paradigm (or 'document-centered' view) can help to address this issue. We present this paradigm and explain how it can be implemented, then discuss its expected benefits both for end-users and MLP researchers.

Hospitexte: towards a document-based hypertextual electronic medical record

The patient record is a repository for knowledge about a patient. Work in Artificial Intelligence and knowledge representation has evidenced the intrinsic difficulty of formalizing knowledge for computer processing. It is therefore not a surprise that most attempts at computerizing the patient record have only had a limited degree of success or applicability. We claim that this is due to the fact that medicine is an empirical domain, and thus fundamentally resists formalization. Therefore, the only way medical knowledge can be fully expressed is through natural languages which is indeed what clinicians actually use. We proposed and designed an electronic medical record which adheres to this hypothesis and where structured documents play a prominent role.

Evaluating a normalized conceptual representation produced from natural language patient discharge summaries

The Menelas project aimed to produce a normalized conceptual representation from natural language patient discharge summaries. Because of the complex and detailed nature of conceptual representations, evaluating the quality of output of such a system is difficult. We present the method designed to measure the quality of Menelas output, and its application to the state of the French Menelas prototype as of the end of the project. We examine this method in the framework recently proposed by Friedman and Hripcsak. We also propose two conditions which enable to reduce the evaluation preparation workload.

Issues in the structuring and acquisition of an ontology for medical language understanding

Medical natural language understanding basically aims at representing the contents of medical texts in a formal, conceptual representation. The understanding process itself increasingly relies on a body of domain knowledge, generally expressed in the same conceptual formalism. The design of such a conceptual representation is a key knowledge-acquisition issue. When representing knowledge, the most important point is to ensure that the formal exploitation of the knowledge representation conforms to its meaning in the domain. We examined some methodological and theoretical principles to enforce this conformity. These principles result from our experience in MENELAS, a medical language understanding project.

A multi-lingual architecture for building a normalised conceptual representation from medical language

The overall goal of MENELAS is to provide better access to the information contained in natural language patient discharge summaries (PDSs), through the design and implementation of a prototype able to analyse medical texts. The approach taken by MENELAS is based on the following key principles: (i) to maximise the usefulness of natural language analysis and the usability of its results, the output of natural language analysis must be a normalised conceptual representation of medical information; and (ii) to maximise the reuse of resources, language analysis should be domain-independent and conceptual representation should be language-independent. This paper discusses the results obtained and the issues raised when implementing these principles during the project.

Male influences on cervical cancer risk

Risk factors for squamous cell carcinoma of the uterine cervix were studied among low income, married Hispanic women and their husbands, using a case-control design. A total of 45 eligible cases were identified at public hospitals and community clinics in the San Francisco Bay Area. For each case, a control was selected within two years of age from among Hispanic women seen at the same institution. Thirty-nine matched pairs of couples were interviewed to assess histories of sexual behavior and other possible risk factors. Cases and controls differed markedly in the number of past sexual partners of their husbands. Cases were 5.3 times more likely to be married to husbands who had had 20 or more sexual partners than were controls. Cases and controls themselves did not differ in their number of sexual partners, but cases were younger at first intercourse than were controls. The association with husband's sexual history persisted after adjusting for the woman's number of sexual partners or age at first intercourse. These results support the infectious and venereal transmission of cervical cancer and indicate the important role of husbands in its occurrence in a population with high incidence rates.