The American journal of medicine
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Alpha-1 antitrypsin deficiency is an under-recognized genetic cause of chronic lung and liver disease; it remains unclear what the testing frequency and disparities are for alpha-1 antitrypsin deficiency. ⋯ Although slowly improving, testing for AAT deficiency continues to have a low uptake in the clinical setting despite guidelines recommending broader testing. Individuals of White race and those with concomitant chronic obstructive pulmonary disease and liver disease are more likely to be tested, while older subjects, individuals of non-White race, current tobacco use, and men with chronic obstructive pulmonary disease are less favored to be tested.
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Machine learning has emerged as a significant tool to augment the medical decision-making process. Studies have steadily accrued detailing algorithms and models designed using machine learning to predict and anticipate pathologic states. The cardiac intensive care unit is an area where anticipation is crucial in the division between life and death. In this paper, we aim to review important studies describing the utility of machine learning algorithms to describe the future of artificial intelligence in the cardiac intensive care unit, especially in regards to the prediction of successful ventilatory weaning, acute respiratory distress syndrome, arrhythmia, and acute kidney injury.