AD-SISCOLO: a decision-support tool to aid the management of a cervical cancer screening program

Abstract

This paper aims to develop a data warehouse (AD-SISCOLO) in order to support the management of the cervical cancer screening program in the municipality of Rio de Janeiro/Brazil. As a part of the management process, the program managers of the municipality perform tedious manual work in order to calculate a series of performance indicators and then take decisions based on them. Methods: AD-SISCOLO was implemented using the Pentaho BI Suite Business Intelligence Platform and the MySQL database management system. The indicators to be calculated and visualized in the tool were based on the municipal data of the cytopathology and histopathology tests from January 2012 until December 2014, which was obtained from the Information System of Cervical Cancer (SISCOLO) after a record linkage process. The follow-up indicators were based on a simplified version of the Brazilian guidelines for the cervical cancer screening. Results: AD-SISCOLO allows the visualization of a set of test-based and follow-up indicators from different views and dimensions, which enable managers to monitor all the phases of the screening process and to identify the process' failures. Conclusions: Compared with the current available environments in Brazil, AD-SISCOLO is unique in its visualization of the follow-up indicators of groups of women, according to their test results and age. Thereby it provides presentation flexibility to suit the program manager's needs.

Machine Learning Methods for Diagnosing Autism Spectrum Disorder and Attention- Deficit/Hyperactivity Disorder Using Functional and Structural MRI: A Survey

“…This is apparent by substantial work that has been carried out in developing machine-learning models, and deep-learning techniques to process high-dimensional MRI data to model neural pathways that govern the brains of various mental disorders (Vieira et al, 2017). These efforts have resulted in development of machine-learning methods to classify Alzheimer's, Mild Cognitive impairment (Duchesnay et al, 2011), Temporal Lobe Epilepsy, Schizophrenia, Parkinson (Bind et al, 2015), Dementia (Ye et al, 2011;Ahmed et al, 2018;Pellegrini et al, 2018), ADHD (Eslami and Saeed, 2018b;Itani et al, 2019), ASD (Pagnozzi et al, 2018;Hyde et al, 2019), and major depression (Gao et al, 2018). These machine-learning models rely on statistical algorithms, and are suitable for complex problems involving combinatorial explosion of possibilities or non-linear processes where traditional computational models fail in quality or scalability.…”