4 Conclusion and Future Work
In this paper, we studied annotation systems of the digital health domain available in
industrial and research areas in order to propose a unified classification of this kind of
system that is omnipresent in hospital information systems. This panoramic view provided is based on the classification of thirty different annotation systems developed in
the literature over the past two decades. This organization of annotation tools is built on
the basis of five criteria: type of annotation (computational/cognitive); category of
annotation system (application/plug-in/website); type of annotative activity (manual/
semi-automatic/automatic); type of annotated resource (text/Web page/video/image/
database) and practitioner (biologist/doctor/radiologist/nurse, etc.). This classification
based on criteria, already explained in our study, which are transversal organizational
criteria, facilitates the identification of limitations and possible challenges in the area of
the medical annotation systems. Based on this, we proposed an ontology that covers the
identified challenges and lead to a more intelligent annotation system. In future research,
we try to use the results of this study to create an annotation template for PHCs and then
try to generalize them to be functional for all professionals in different domains. We are
also trying to create the computer services which allow the PHC to be assisted
throughout the care cycle.
References
1. http://aclweb.org/anthology/P18-4012
2. Clean tools. https://arxiv.org/ftp/arxiv/papers/1808/1808.03806.pdf
3. Ma, J., Meng, J.: Interactive genomic visualization for R/bioconductor. In: International
Conference on Biological Information and Biomedical Engineering, BIBE 2018, pp. 1–4.
VDE, June 2018
4. Storck, M., Krumm, R., Dugas, M.: ODM summary: a tool for automatic structured
comparison of multiple medical forms based on semantic annotation with the unified medical
language system. PLoS ONE 11(10), e0164569 (2017)
5. Segura, J., et al.: 3DBIONOTES v2. 0: a web server for the automatic annotation of
macromolecular structures. Bioinformatics 33(22), 3655–3657 (2017)
6. McKain, M.R., Hartsock, R.H., Wohl, M.M., Kellogg, E.A.: Verdant: automated annotation,
alignment and phylogenetic analysis of whole chloroplast genomes. Bioinformatics 33(1),
130–132 (2016)
7. https://www.best.edu.au/s/5njv62ar?data=8%400!9%4028320!10%40-27251.5&version=1
8. https://www.micromd.com/emr/
9. Vizza, P., Guzzi, P.H., Veltri, P., Cascini, G.L., Curia, R., Sisca, L.: GIDAC: a prototype for
bioimages annotation and clinical data integration. In: 2016 IEEE International Conference
on Bioinformatics and Biomedicine (BIBM), pp. 1028–1031. IEEE, December 2016
10. Hart, S.N., Duffy, P., Quest, D.J., Hossain, A., Meiners, M.A., Kocher, J.P.: VCF-Miner:
GUI-based application for mining variants and annotations stored in VCF files. Brief.
Bioinform. 17(2), 346–351 (2016)
11. https://diegocantor.com/projects/
12. Qualter, J., et al.: The BioDigital human: a Web-based 3D platform for medical visualization
and education. Stud. Health Technol. Inform. 173, 359–361 (2016)
Study of Healthcare Professionals’ Interaction
325
In this paper, we studied annotation systems of the digital health domain available in
industrial and research areas in order to propose a unified classification of this kind of
system that is omnipresent in hospital information systems. This panoramic view provided is based on the classification of thirty different annotation systems developed in
the literature over the past two decades. This organization of annotation tools is built on
the basis of five criteria: type of annotation (computational/cognitive); category of
annotation system (application/plug-in/website); type of annotative activity (manual/
semi-automatic/automatic); type of annotated resource (text/Web page/video/image/
database) and practitioner (biologist/doctor/radiologist/nurse, etc.). This classification
based on criteria, already explained in our study, which are transversal organizational
criteria, facilitates the identification of limitations and possible challenges in the area of
the medical annotation systems. Based on this, we proposed an ontology that covers the
identified challenges and lead to a more intelligent annotation system. In future research,
we try to use the results of this study to create an annotation template for PHCs and then
try to generalize them to be functional for all professionals in different domains. We are
also trying to create the computer services which allow the PHC to be assisted
throughout the care cycle.
References
1. http://aclweb.org/anthology/P18-4012
2. Clean tools. https://arxiv.org/ftp/arxiv/papers/1808/1808.03806.pdf
3. Ma, J., Meng, J.: Interactive genomic visualization for R/bioconductor. In: International
Conference on Biological Information and Biomedical Engineering, BIBE 2018, pp. 1–4.
VDE, June 2018
4. Storck, M., Krumm, R., Dugas, M.: ODM summary: a tool for automatic structured
comparison of multiple medical forms based on semantic annotation with the unified medical
language system. PLoS ONE 11(10), e0164569 (2017)
5. Segura, J., et al.: 3DBIONOTES v2. 0: a web server for the automatic annotation of
macromolecular structures. Bioinformatics 33(22), 3655–3657 (2017)
6. McKain, M.R., Hartsock, R.H., Wohl, M.M., Kellogg, E.A.: Verdant: automated annotation,
alignment and phylogenetic analysis of whole chloroplast genomes. Bioinformatics 33(1),
130–132 (2016)
7. https://www.best.edu.au/s/5njv62ar?data=8%400!9%4028320!10%40-27251.5&version=1
8. https://www.micromd.com/emr/
9. Vizza, P., Guzzi, P.H., Veltri, P., Cascini, G.L., Curia, R., Sisca, L.: GIDAC: a prototype for
bioimages annotation and clinical data integration. In: 2016 IEEE International Conference
on Bioinformatics and Biomedicine (BIBM), pp. 1028–1031. IEEE, December 2016
10. Hart, S.N., Duffy, P., Quest, D.J., Hossain, A., Meiners, M.A., Kocher, J.P.: VCF-Miner:
GUI-based application for mining variants and annotations stored in VCF files. Brief.
Bioinform. 17(2), 346–351 (2016)
11. https://diegocantor.com/projects/
12. Qualter, J., et al.: The BioDigital human: a Web-based 3D platform for medical visualization
and education. Stud. Health Technol. Inform. 173, 359–361 (2016)
Study of Healthcare Professionals’ Interaction
325