| Full text | |
| Author(s): |
Macedo, Alessandra A.
;
Baranauskas, Jose A.
;
Bulcao-Neto, Renato de F.
;
Ganzha, M
;
Maciaszek, L
;
Paprzycki, M
Total Authors: 6
|
| Document type: | Journal article |
| Source: | PROCEEDINGS OF THE 2018 FEDERATED CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS (FEDCSIS); v. N/A, p. 9-pg., 2018-01-01. |
| Abstract | |
The literature describes examples of software frameworks providing developers with generic and reusable functionality for building healthcare applications. Using concepts and technologies from Information Retrieval, Machine Learning, and Semantic Web, we present a novel software framework called HSSF (Health Surveillance Software Framework) which aims to facilitate the development of applications to support health professionals in the prevention of chronic diseases. The main contribution of this paper includes lessons learned distilled from (i) the reuse and evolution of the HSSF components on the development of three new health surveillance applications, and (ii) a quantitative evaluation of the HSSF reusability in terms of time spent and artifacts reused on such development task. Lessons learned are summarized as advantages and drawbacks regarding HSSF reusability. The HSSF allows healthcare applications not only to relate scientific research evidences, exams and treatments, but also to incorporate them together into the clinical practice. (AU) | |
| FAPESP's process: | 16/13206-4 - Semnatic Enrichment of Medical Information from Networking Complex Heterogeneous |
| Grantee: | Alessandra Alaniz Macedo |
| Support Opportunities: | Regular Research Grants |