Reseña del Libro: "Medicine-Based Informatics and Engineering" Lecture Notes in Bioengineering, Springer, 2022 / Book Review: "Medicine-Based Informatics and Engineering" Lecture Notes in Bioengineering, Springer, 2022

RESUMEN El presente trabajo es una reseña original sobre el libro "Medicine-Based Informatics and Engineering" publicado en la colección Lecture Notes in Bioengineering de Springer, en 2022, cuyos editores son los investigadores Franco Simini y Pedro Bertemes-Filho. La reseña busca sintetizar el contenido de los trabajos presentados en los capítulos centrándose en las soluciones desarrolladas. Los editores, a partir de la diversidad de los aportes, vislumbran la integración de visiones, en lo que denominan "Ingeniería Médica".

ABSTRACT This paper is an original review of the book "Medicine-Based Informatics and Engineering" published in Springer's Lecture Notes in Bioengineering collection, in 2022, whose editors are Franco Simini and Pedro Bertemes-Filho. The review aims to synthesize the content of the papers presented in the chapters focusing on the solutions developed. The editors, from the diversity of the contributions, envision the integration of visions, in what they call "Medical Engineering".

Predicting restriction of life-space mobility: a machine learning analysis of the IMIAS study.

BACKGROUND: Some studies have employed machine learning (ML) methods for mobility prediction modeling in older adults. ML methods could be a helpful tool for life-space mobility (LSM) data analysis. AIM: This study aimed to evaluate the predictive value of ML algorithms for the restriction of life-space mobility (LSM) among elderly people and to identify the most important risk factors for that prediction model. METHODS: A 2-year LSM reduction prediction model was developed using the ML-based algorithms decision tree, random forest, and eXtreme gradient boosting (XGBoost), and tested on an independent validation cohort. The data were collected from the International Mobility in Aging Study (IMIAS) from 2012 to 2014, comprising 372 older patients (≥ 65 years of age). LSM was measured by the Life-Space Assessment questionnaire (LSA) with five levels of living space during the month before assessment. RESULTS: According to the XGBoost algorithm, the best model reached a mean absolute error (MAE) of 10.28 and root-mean-square error (RMSE) of 12.91 in the testing portion. The variables frailty (39.4%), mobility disability (25.4%), depression (21.9%), and female sex (13.3%) had the highest importance. CONCLUSION: The model identified risk factors through ML algorithms that could be used to predict LSM restriction; these risk factors could be used by practitioners to identify older adults with an increased risk of LSM reduction in the future. The XGBoost model offers benefits as a complementary method of traditional statistical approaches to understand the complexity of mobility.

Unveiling educational patterns at a regional level in Colombia: data from elementary and public high school institutions.

Even though the field of Learning Analytics (LA) has experienced an expressive growth in the last few years. The vast majority of the works found in literature are usually focusing on experimentation of techniques and methods over datasets restricted to a given discipline, course, or institution and are still few works manipulating region and countrywide datasets. This may be since the implementation of LA in national or regional scope and using data from governments and institutions poses many challenges that may threaten the success of such initiatives, including the same availability of data. The present article describes the experience of LA in Latin America using governmental data from Elementary and Middle Schools of the State of Norte de Santander - Colombia. This study is focusing on students' performance. Data from 2013 to 2018 was collected, containing information related to 1) students' enrollment in school disciplines provided by Regional Education Secretary, 2) students qualifications provided by educational institutions, and 3) students qualifications provided by the national agency for education evaluation. The methodology followed includes a process of cleaning and integration of the data, subsequently a descriptive and visualization analysis is made and some educational data mining techniques are used (decision trees and clustering) for the modeling and extraction of some educational patterns. A total of eight patterns of interest are extracted. In addition to the decision trees, a feature ranking analysis was performed using xgboost and to facilitate the visual representation of the clusters, t-SNE and self-organized maps (SOM) were applied as result projection techniques. Finally, this paper compares the main challenges mentioned by the literature according to the Colombian experience and proposes an up-to-date list of challenges and solutions that can be used as a baseline for future works in this area and aligned with the Latin American context and reality.