Hippokratia
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Detecting liver dysfunction/failure in the intensive care unit poses a challenge as individuals afflicted with these conditions often appear symptom-free, thereby complicating early diagnoses and contributing to unfavorable patient outcomes. The objective of this endeavor was to improve the chances of early diagnosis of liver dysfunction/failure by creating a predictive model for the critical care setting. This model has been designed to produce an index that reflects the probability of severe liver dysfunction/failure for patients in intensive care units, utilizing machine learning techniques. ⋯ Our machine learning approach facilitates early and timely intervention in the hepatic function of critically ill patients by their healthcare providers to prevent or minimize associated morbidity and mortality. HIPPOKRATIA 2024, 28 (1):1-10.
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Background: Patient-reported outcome measures (PROMs) assess how individuals perceive the disease and its impact on quality of life, representing an important complement to the metabolic evaluation in type 1 diabetes mellitus (T1DM). This study aimed to assess the PROMs and their association with metabolic control. ⋯ Including PROMs alongside detailed metabolic evaluation allows for individualized decision-making and active patient participation in diabetes management. These results underscore the importance of preventing depression, promoting well-being, and enhancing diabetes psychological adjustment in these patients, aiming to improve their quality of life. HIPPOKRATIA 2024, 28 (1):17-21.