Data mining in HIV-AIDS surveillance system: application to portuguese data

• Published in: Journal of medical systems Vol. 41; no. 4; p. 51
• Main Authors: Oliveira, Alexandra, Faria, Brigida Monica, Gaio, Rita A., Reis, L. P.
• Format: Journal Article
• Language: English
• Published: New York Springer Science+Business Media 01-04-2017; Springer US; Springer Nature B.V
• Subjects: Acquired Immunodeficiency Syndrome - diagnosis; Acquired Immunodeficiency Syndrome - epidemiology; Algorithms; Artificial intelligence; Bayes Theorem; Data mining; Data Mining - methods; Health Informatics; Health Information Systems & Technologies; Health Sciences; HIV; HIV Infections - diagnosis; HIV Infections - epidemiology; Human immunodeficiency virus; Humans; Medicine; Medicine & Public Health; Neural networks; Neural Networks (Computer); Portugal - epidemiology; Reporting delay; Science & Technology; Statistics for Life Sciences; Support Vector Machine; Surveillance data; Surveillance system; Systems-Level Quality Improvement; Reporting delay; Surveillance system; HIV-AIDS; Data mining; Surveillance data
• ISSN: 0148-5598; 1573-689X
• DOI: 10.1007/s10916-017-0697-4

The Human Immunodeficiency Virus (HIV) is an infectious agent that attacks the immune system cells. Without a strong immune system, the body becomes very susceptible to serious life threatening opportunistic diseases. In spite of the great progresses on medication and prevention over the last years, HIV infection continues to be a major global public health issue, having claimed more than 36 million lives over the last 35 years since the recognition of the disease. Monitoring, through registries, of HIV-AIDS cases is vital to assess general health care needs and to support long-term health-policy control planning. Surveillance systems are therefore established in almost all developed countries. Typically, this is a complex system depending on several stakeholders, such as health care providers, the general population and laboratories, which challenges an efficient and effective reporting of diagnosed cases. One issue that often arises is the administrative delay in reports of diagnosed cases. This paper aims to identify the main factors influencing reporting delays of HIV-AIDS cases within the portuguese surveillance system. The used methodologies included multilayer artificial neural networks (MLP), naive bayesian classifiers (NB), support vector machines (SVM) and the k-nearest neighbor algorithm (KNN). The highest classification accuracy, precision and recall were obtained for MLP and the results suggested homogeneous administrative and clinical practices within the reporting process. Guidelines for reductions of the delays should therefore be developed nationwise and transversally to all stakeholders.
- A. Rita Gaio was partially supported by CMUP (UID/MAT/00144/2013), which is funded by FCT (Portugal) with national (MEC) and European structural funds (FEDER), under the partnership agreement PT2020. Luis Paulo Reis was partially by the European Regional Development Fund through the programme COMPETE by FCT (Portugal) in the scope of the project PEst - UID/ CEC/00027/2015 Luis Paulo Reis and Brigida Monica Faria were partially funded by QVida+: Estimacao Continua de Qualidade de Vida para Auxilio Eficaz a Decisao Clinica, NORTE-01-0247-FEDER-003446, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement.