Month: November 2025

Machine Learning Techniques Used for the Identification of Sociodemographic Factors Associated with Cancer: Systematic Literature Review

Background: Cancer remains one of the foremost global causes of mortality, with nearly 10 million deaths recorded by 2020. As incidence rates rise, there is a growing interest in leveraging machine learning (ML) to enhance prediction, diagnosis, and treatment strategies. Despite these advancements, insufficient attention has been directed towards the integration of sociodemographic variables, which are crucial determinants of health equity, into ML models in oncology. This review, investigates how machine learning techniques have been used to identify patterns of predictive association between sociodemographic factors and cancer-related outcomes. Specifically, it seeks to map current research endeavours by detailing the types of algorithms employed, the sociodemographic variables examined, and the validation methodologies utilized. We conducted a systematic literature review in accordance with the PRISMA guidelines. Searches were executed across seven databases, focusing on primary studies employing machine learning to investigate the relationship between sociodemographic characteristics and cancer-related outcomes. The search strategy was informed by the PICO framework, and a set of predefined inclusion criteria was utilized to screen the studies. The methodological quality of each included paper was assessed. Out of the 328 records examined, 19 satisfied the inclusion criteria. The majority of studies employed supervised machine learning techniques, with Random Forest and XGBoost being the most commonly utilized. Frequently analysed variables include age, sex, education level, income, and geographic location. Cross-validation is the predominant method for evaluating model performance. Nevertheless, the integration of clinical and sociodemographic data is limited, and efforts toward external validation are infrequent. Machine learning (ML) holds significant potential for discerning patterns associated with the social determinants of cancer. Nevertheless, research in this domain remains fragmented and inconsistent. Future investigations should prioritize the integration of contextual factors, enhance model transparency, and bolster external validation. These measures are crucial for the development of more
equitable, generalizable, and actionable ML applications in cancer care.

This study will be published in https://www.jmir.org

The state of practice about security in telemedicine systems in Chile: An exploratory study

Information security within telemedicine systems is essential to advancing the digital transformation of healthcare. Telemedicine encompasses diverse modalities, including teleconsultation, telehealth, and remote patient monitoring, all of which depend on digital platforms, secure communication networks, and internet-connected devices. Although these systems have progressed in aligning with information security standards and regulations, there remains a shortage of comprehensive, practice-oriented studies evaluating which aspects of security are effectively addressed and which remain insufficiently managed, particularly within the Chilean context. This study aims to examine how effectively telemedicine systems in Chile address the core security attributes of confidentiality, availability, and integrity. Data were analysed from an evaluation tool designed to assess the quality of telemedicine systems in Chile. Over a six-year period, 25 telemedicine systems from different providers were assessed, and an in-depth examination of how companies manage key information security sub-characteristics within their systems was undertaken. The findings indicate that 52% of telemedicine systems optimally implement cryptographic techniques to protect confidentiality. In contrast, 44% lack robust strategies for adapting to, recovering from, and mitigating security-related incidents. Fault tolerance mechanisms are frequently integrated to minimise service disruption caused by system failures. However, the prioritisation of data integrity varies: while some companies treat it as a critical requirement, others assign it limited importance. This study offers an understanding of the security priorities and practices adopted by telemedicine providers. It highlights a prevailing tendency to prioritise security measures over usability, underscoring the need for a balanced approach that safeguards patient information while supporting efficient clinical workflows.

This study will published in https://medinform.jmir.org