Worldwide, the coronavirus has intensified the management problems of health services, significantly harming patients. Some of the most affected processes have been cancer patients’ prevention, diagnosis, and treatment. Breast cancer is the most affected, with more than 20 million cases and at least 10 million deaths by 2020. Various studies have been carried out to support the management of this disease globally. This paper presents a decision support strategy for health teams based on machine learning (ML) tools and explainability algorithms (XAI). The main methodological contributions are: first, the evaluation of different ML algorithms that allow classifying patients with and without cancer from the available dataset; and second, an ML methodology mixed with an XAI algorithm, which makes it possible to predict the disease and interpret the variables and how they affect the health of patients. The results show that first, the XGBoost Algorithm has a better predictive capacity, with an accuracy of 0.813 for the train data and 0.81 for the test data; and second, with the SHAP algorithm, it is possible to know the relevant variables and their level of significance in the prediction, and to quantify the impact on the clinical condition of the patients, which will allow health teams to offer early and personalized alerts for each patient.

Various care processes have been affected by COVID-19. One of the most dramatic has been the care of chronic patients under medical supervision. According to the World Health Organization (WHO), a chronic patient has one or more long-term illnesses, and must be permanently monitored by the health team.. In fact, and according to the Chilean Ministry of Health (MINSAL), 7 out of 10 chronic patients have suspended their medical check-ups, generating critical situations, such as a more significant number of visits to emergency units, expired prescriptions, and a higher incidence in hospitalization rates. For this problem, health services in Chile have had to reschedule their scarce medical resources to provide care in all health processes. One element that has been considered is caring through telemedicine and patient prioritization. In the latter case, the aim was to provide timely care to those critical patients with high severity and who require immediate clinical attention. For this reason, in this work, we present the following methodological contributions: first, an unsupervised algorithm that analyzes information from anonymous patients to classify them according to priority levels; and second, rules that allow health teams to understand which variable(s) determine the classification of patients. The results of the proposed methodology allow classifying new patients with 99.96% certainty using a three-level decision tree and five classification rules.

The coronavirus pandemic has intensified the strain on medical care processes, especially waiting lists for patients under medical management. In Chile, the pandemic has caused an increase of 52,000 people waiting for care. For this reason, a high-complexity hospital (HCH) in Chile devised a decision support system (DSS) based on multi-criteria decision-making (MCDM), which combines management criteria, such as critical events, with clinical variables that allow prioritizing the population of chronic patients on the waiting list. The tool includes four methodological contributions: (1) pattern recognition through the analysis of anonymous patient data that allows critical patients to be characterized; (2) a score of the critical events suffered by the patients; (3) a score based on clinical criteria; and (4) a dynamic–hybrid methodology for patient selection that links critical events with clinical criteria and with the risk levels of patients on the waiting list. The methodology allowed to (1) characterize the most critical patients and triple the evaluation of medical records; (2) save medical hours during the prioritization process; (3) reduce the risk levels of patients on the waiting list; and (4) reduce the critical events in the first month of implementation, which could have been caused by the DSS and medical decision-making. This strategy was effective (even during a pandemic period).

Stock market manipulation, defined as any attempt to artificially influence stock prices, poses significant challenges by causing financial losses and eroding investor trust. The prevalent reliance on supervised learning models for detecting such manipulations, while showing promise, faces notable hurdles due to the dearth of labeled data and the inability to recognize novel manipulation tactics beyond those explicitly labeled. This study ventures into addressing these gaps by proposing a novel detection framework aimed at identifying suspicious hourly manipulation blocks through an unsupervised learning approach, thereby circumventing the limitations of data labeling and enhancing the adaptability to emerging manipulation strategies. Our methodology involves the innovative creation of features reflecting the behavior of stocks across various time windows followed by the segmentation of the dataset into k subsets. This setup facilitates the identification of potential manipulation instances via a voting ensemble composed of k isolation forest models, which have been chosen for their efficiency in pinpointing anomalies and their linear computational complexity—attributes that are critical for analyzing vast datasets. Evaluated against eight real stocks known to have undergone manipulation, our approach demonstrated a remarkable capability to identify up to 89% of manipulated blocks, thus significantly outperforming previous methods that do not utilize a voting ensemble. This finding not only surpasses the detection rates reported in prior studies but also underscores the enhanced robustness and adaptability of our unsupervised model in uncovering varied manipulation schemes. Through this research, we contribute to the field by offering a scalable and efficient unsupervised learning strategy for stock manipulation detection, thereby marking a substantial advancement over traditional supervised methods and paving the way for more resilient financial markets.

Prolonged recovery from work following commuting accidents entails substantial personal, social, and economic costs; however, the social and health determi

Background: Prolonged work absence following an occupational injury has important personal, social, and economic consequences, including social isolation, depression, reduced income, and diminished quality of life. Despite its relevance, the demographic, clinical, and injury-related factors associated with prolonged work absence remain insufficiently understood. This study aimed to identify these factors using administrative data from Mutual de Seguridad de Chile (2011–2024). Methods: An explanatory full-cohort study was conducted using administrative records from 130,752 eligible occupational injury cases. Prolonged work absence was defined as time loss of at least 90 days (3 months). A probit model was estimated using the full cohort, and average marginal effects (AMEs) were calculated to quantify the adjusted association between each predictor and the probability of prolonged work absence. All explanatory variables were operationalized as dichotomous measures. Model performance was evaluated using goodness-of-fit statistics, including the area under the receiver operating characteristic (ROC) curve. Results: Prolonged work absence was observed in 6,228 cases (4.8% of the cohort). The probit model demonstrated excellent discriminative ability, with an area under the ROC curve of 0.91. A higher probability of prolonged work absence was associated with being male (0.6% points), having multiple diagnoses (3.0% points), sustaining a severe injury (11.3% points), having a disability of at least 15% (57.6% points), injuries affecting the nervous system (9.1% points) or genitourinary system (4.3% points), falls (0.9% points), machine-related injuries (0.2% points), and employment in small and medium-sized enterprises (0.3% points). Compared with workers aged 45 years or older, younger workers had significantly lower probabilities of prolonged work absence. Conclusion: Prolonged work absence following occupational injuries in Chile is associated with demographic, clinical, injury-related, and workplace characteristics, with injury severity and disability demonstrating the strongest associations. These findings may support the early identification of workers at increased risk of prolonged absence and inform targeted prevention, rehabilitation, and return-to-work strategies. Strengthening occupational health protection systems may further facilitate recovery and promote a timely and sustainable return to work.