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Exploring Breast Cancer Patterns for Different Outcomes using Artificial Intelligence.

, , , , and . HealthCom, page 1-6. IEEE, (2018)

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VisualMLTCGA: An Easy-to-Use Web Tool for the Visualization, Processing and Classification of Clinical and Genomic TCGA Data., , , and . HEALTHINF, page 413-420. SCITEPRESS, (2020)The Impact of Environmental Factors on Heart Failure Decompensations., , , , , , and . HEALTHINF, page 51-58. SciTePress, (2019)978-989-758-353-7.Exploring Breast Cancer Patterns for Different Outcomes using Artificial Intelligence., , , , and . HealthCom, page 1-6. IEEE, (2018)Artificial Intelligence to Prevent Mobile Heart Failure Patients Decompensation in Real Time: Monitoring-Based Predictive Model., , , , and . Mob. Inf. Syst., (2018)Predicting 30-day Readmission in Heart Failure using Machine Learning Techniques., , , , and . HEALTHINF, page 308-315. SciTePress, (2018)DESIREE - a web-based software ecosystem for the personalized, collaborative and multidisciplinary management of primary breast cancer., , , , and . HealthCom, page 1-2. IEEE, (2018)Hospital Readmission Prediction via Keyword Extraction and Sentiment Analysis on Clinical Notes., , , , , , , and . ICIMTH, volume 295 of Studies in Health Technology and Informatics, page 339-342. IOS Press, (2022)Adaptative Clinical Decision Support System using Machine Learning and Authoring Tools., , , , and . HEALTHINF, page 95-105. SCITEPRESS, (2020)Multicenter Prospective Blind External Validation of a Machine Learning Model for Predicting Heart Failure Decompensation: A 3-Hospital Validation Study., , , , , , , , , and 5 other author(s). IWINAC (2), volume 14675 of Lecture Notes in Computer Science, page 368-377. Springer, (2024)Breast Cancer Digital Patient Model to Capture and Visualize Real World Data., , , , and . CBMS, page 640-645. IEEE, (2019)