1 Introduction
The year 2018 has produced a large amount of publications related to Knowledge Representation
and Management (KRM) in Medicine. KRM focuses on the development of techniques to
be used and leveraged in other medical informatics domains. In this review, we present
a selection of the best papers published in 2018 in the KRM domain, based either on
their impact or on the novelty of the approach they proposed in the medical knowledge
representation and management field.
2 Paper Selection Method
We conducted the selection of KRM papers based on a new set of queries. In comparison
with the previous editions of the IMIA Yearbook, both PubMed/MELDINE and Web of Knowledge
were used to search for KRM articles published in 2018 [1]. We followed the generic method commonly used in all sections of the Yearbook to
select the best papers. As for the last four years, the search was performed on MEDLINE
by querying PubMed. This year, we also performed an additional query on the ISI Web
of Knowledge database (WoL).
Our query includes Medical Subject Headings (MeSH) descriptors related to KRM in the
context of medical informatics with a restriction to international peer-reviewed journals,
including conference proceedings indexed in PubMed. Only original research articles
published in 2018 (from 01/01/2018 to 12/31/2018) were considered; we excluded the
following publications types: reviews, editorials, comments, and letters to the editors.
The selection of the best papers was performed among the results of the query process,
in three steps. At the first step, the section editors reviewed all titles, abstracts,
and publication types in order to establish a short list of 15 candidate best papers.
At the second step, five expert reviewers (including the section editors) reviewed
the candidate best papers using the IMIA Yearbook quality criteria scoring method.
More specifically, the following aspects of the papers were evaluated: significance,
quality of scientific content, originality and innovativeness, coverage of related
literature, organization, and quality of the presentation. The final step of best
papers’ selection was achieved during a meeting gathering the whole editorial board,
based on the reviews and the report of the two section editors.
3 Results
For 2018, the KRM query retrieved 928 citations from PubMed and 34 additional citations
from WoL. This new optimized query set accounts for 52% decrease of retrieved papers
in comparison with results of the query used in 2017, with an overall improved precision
of KRM relevant papers. Section editors achieved a first selection of 100 papers based
on title and abstract. After a second review of this set of papers, including full
text reviews, a selection of 15 candidate best papers was established [2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]. Five reviewers reviewed these papers and four papers were finally selected as the
best papers [2]
[3]
[4]
[5].
In direct line with the research presented last year [1], the 2018 four best papers demonstrated even further the added-value of ontology-based
integration approaches for phenotype-genotype association mining.
4 Discussion and Outlook
4.1 Best Papers Selection for 2018
The paper authored by Arguello Casteleiro et al., and selected as a best paper, aims at automatically identify term variants or acceptable
alternative free-text terms for gene and protein names in PubMed biomedical publications
[2]. The use of a domain knowledge ontology, the Cardio Vascular Disease Ontology (CVDO),
was associated with the best results. This study led to performance improvements for
both Continuous Bag of Words (CBOW) and Skip-gram on a gene/protein synonym detection
task by adding knowledge formalized in the CVDO and without modifying the word embeddings
created. Hence, the CVDO supplies context that is effective in inducing term variability
for both CBOW and Skip-gram while reducing ambiguity. In an other best paper, Le et al., presents Spfy, a platform that exploits semantic technologies with a graph database
that allows rapid phenotype identification through a novel bioinformatics pipeline,
as well as efficient storage and downstream comparative analysis of thousands of genome
sequences [3]. In their paper, Osumi-Sutherland et al., use OWL- based (Ontology Web Language) reasoning on Gene Ontology (GO) to generate
novel, biologically relevant groupings of GO terms to support mapping with a controlled
vocabulary [4]. The GO term groupings generated by this approach can be used in over-representation
analysis to detect cell and tissue type signatures in whole genome expression datasets.
Also one of the best papers for 2018, the work of Yu et al., [5] presents a phenotyping algorithm (Phe- Norm) that does not require expert-labeled
samples at the training step. This completely annotation-free 2-step classification
method for phenotyping involves an initial normalization step of highly predictive
features of the target phenotype, followed by a denoising step to leverage additional
information contained in the remaining candidate features. This work introduces a
method especially relevant for big data processing, which is the case for EHR-driven
(Electronic Health Record) phenotyping. The four best papers are listed in [table 1] and detailed in the appendix.
Table 1
Best paper selection of articles for the IMIA Yearbook of Medical Informatics 2019
in the section ‘Knowledge Representation and Management’. The articles are listed
in alphabetical order of the first author’s surname.
|
Section
Knowledge Representation and Management
|
|
▪ Arguello Casteleiro M, Demetriou G, Read W, Fernandez Prieto MJ, Maroto N, Maseda
Fernandez D, Nenadic G, Klein J, Keane J, Stevens R. Deep learning meets ontologies:
experiments to anchor the cardiovascular disease ontology in the biomedical literature.
J Biomed Semantics 2018;9(1):13.
|
|
▪ Le KK, Whiteside MD, Hopkins JE, Gannon VPJ, Laing CR. Spfy: an integrated graph
database for real-time prediction of bacterial phenotypes and downstream comparative
analyses. Database (Oxford) 2018;2018:1-10.
|
|
▪ Osumi-Sutherland DJ, Ponta E, Courtot M, Parkinson H, Badi L. Using OWL reasoning
to support the generation of novel gene sets for enrichment analysis. J Biomed Semantics
2018;9(1):10.
|
|
▪ Yu S, Ma Y, Gronsbell J, Cai T, Ananthakrishnan AN, Gainer VS, Churchill SE, Szolovits
P, Murphy SN, Kohane IS, Liao KP, Cai T. Enabling phenotypic big data with PheNorm.
J Am Med Inform Assoc 2018;25(1):54-60.
|
4.2 Main Trends in KRM in 2018
Among the 11 other candidate best papers from the short list for 2018, we observed
four directions in research, (i) the research on ontology-based data integration for
phenotype-genotype association mining; (ii) the design of ontologies and their application,
the common direction of our field; (iii) the works regarding the semantic annotation
of texts; and (iv) an experience about the deployment of OMOP-CDM (Observational Medical
Outcomes Partnership Common Data Model) in Germany.
4.2.1 Semantics for Genomic Data Management
In a long article, Al Kawam et al., [15] tackled fundamental bioinformatics challenges involving semantic representations:
genomic data generation, storage, representation, and utilization in conjunction with
clinical data. For each aspect, they provided a detailed discussion on the current
research directions, outstanding challenges, and possible resolutions. This paper
seeks to help narrow the gap between genomic applications, which are being predominantly
utilized in research settings, and the clinical adoption of these applications.
In differential diagnoses and disease gene prioritization, the Human Phenotype Ontology
(HPO) is often used to compare a phenotype profile against gold-standard phenotype
profiles of diseases or genes. In his article [7], Köhler investigated how this comparison can be improved by exploiting structure
and information existing in annotation datasets or full text disease descriptions.
He tested a study-wise annotation model for diseases annotated with HPO classes and
for genes annotated with GO classes. This paper adds weight to the need for enhancing
simple flat list representations of disease or gene annotations.
Cheng et al., [8] addressed the question of finding similarities of terms between different ontologies
(e.g. HPO, Disease Ontology (DO), „.etc.). They took advantage of the gene functional
interaction network (GFIN) to explore such inter-ontology similarities of terms. They
proposed InfAcrOnt to infer similarities between terms across ontologies and acquired
similarities between terms across ontologies through modeling the information flow
within the network by random walk. Comparisons of InfAcrOnt results and prior knowledge
on pair-wise DO-HPO terms and pair-wise DO-GO terms showed high correlations.
4.2.2 Ontology Design and Documentation
Five of the candidate best papers present research in the ontology design domain [6], [10]
[11]
[12], [16]. This theme is common in the KRM section. While it was declining in recent years,
it has become more present in 2018 than in previous years. In each case, articles
described the motivation for building the ontology and developed the application that
uses it for validation.
Traverso et al., [12] developed a Radiation Oncology Ontology (ROO). This ontology takes into account
a few standard ontologies as the Fundational Model of Anatomy (FMA), the National
Cancer Institute thesaurus (NCIt), and others terminologies. Authors demonstrated
the possible conversion of clinical data following the FAIR principles (Findability,
Accessibility, Interoperability, and Reusability), by using a combination of ontologies
and Semantic Web (SW) technologies. This work proposes, using SW technologies based
on existing ontologies, to efficiently and easily query data from different sources
(relational databases) without knowing a priori their structures.
Bibault et al, [10] developed a Radiation Oncology Structures (ROS) Ontology. This ontology also relies
on several standard ontologies as FMA, Radlex, and others. Authors provided annotations
of EHR radiation oncology data with ROS concepts and integrated them into their clinical
data warehouse. Finally, they showed the utility of the ontology in order to integrate
dosimetric data.
Jing et al., [6] described the motivation and the building of OntoKBCF, an ontology for the cystic
fibrosis domain. They illustrated the lack of sufficient clinical actionable knowledge
that is related to molecular genetic information. The Cystic fibrosis ontology (OntoKBCF)
is just a use case example, but given its structure, it should be relatively straightforward
to extend the prototype to cover different genetic conditions. The principles underpinning
its development could efficiently serve the design of knowledge bases for alternative
human monogenetic diseases.
Facing the significant time cost to build ontologies, Zhao et al., proposed a data-driven sublanguage pattern mining method that can be used to create
a knowledge model [16]. They combined standard Natural Language Processing (NLP) and semantic network analysis
in their model generation pipeline. The results suggest that their pipeline is able
to produce a comprehensive content-based knowledge model to represent context from
various sources in the same domain.
In line with the publication of ontologies, Matentzoglu et al., [11] proposed the Minimum Information for Reporting an Ontology (MIRO) guidelines as
a means to facilitate a higher degree of completeness and consistency between ontology
documentation, including published papers, and ultimately a higher standard of report
quality. These guidelines result from a survey among the KRM community that is detailed
in the article. An illustrative review of 15 recently published ontology description
reports from three important journals in the Semantic Web and Biomedical domain analyzed
them for compliance with the MIRO guidelines. Only 41.38% of MIRO items were covered
by these reports.
4.2.3 Semantics and Clinical Notes
Two candidate best papers presented a research involving semantic formalization associated
with NLP approaches to process clinical texts. The first paper by Catling et al., [13] explored methods for representing clinical text using hierarchical clinical coding
ontologies. This study demonstrates that hierarchically-structured medical knowledge
can be incorporated into statistical models to produce improved performance for automated
clinical coding. However, the authors reported that the data processing was difficult:
they used a supervised learning approach with manually-as-signed clinical codes for
the training dataset. Consequently, learning good representations of rare diseases
in clinical coding ontologies from data alone remains challenging.
Viani et al., [9] proposed an ontology-driven approach to identify events (and their attributes) from
episodes of care in medical reports written in Italian. Authors developed an ontology
that can be easily enriched and translated. For this language, shared resources for
clinical information extraction are not easily accessible. The proposed approach performed
well on the considered Italian medical corpus, with a percentage of correct annotations
above 90% for most considered clinical events.
4.2.4 Interoperability and Data Integration
The paper from Maier et al., [14] reports the experiment of implementing an OMOP/OHDSI-based pilot within a consortium
of eight German University hospitals. Authors evaluated the applicability to support
data harmonization and sharing among University hospitals, and they identified potential
enhancement requirements. In order to facilitate the work of hospital centers, they
provided a virtual machine preconfigured with the OMOP database and the OHDSI tools
as well as the jobs to import the data and conduct the analysis. This work is encouraging,
even if taking into account important vocabularies for Germany remains to be done.
Such a paper shows the difficulties of moving from a model to a real implementation.
5 Conclusions
In the KRM selection for 2018, research on semantic representations demonstrated their
added-value for enhanced deep learning approaches in text mining and for designing
novel bioinformatics pipelines based on graph database. In addition, the ontology
structure can enrich the analyses of whole genome expression data. Finally, semantic
representations demonstrated promising results to process phenotypic big data.
