Chest
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Case Reports
A 44-Year-Old Man With Right Limb Convulsion and Cavitary Lung Lesion With Diffuse Interstitial Abnormalities.
A 44-year-old man with a history of asthma presented with intermittent convulsion of the right limb, fever in the late afternoon, and decreased exercise tolerance over 2 months. Occasional productive cough, no hemoptysis, and weight loss of nearly 6 kg were observed during this period. Neither chemotherapy nor oral immunosuppressive drugs had been administered, and no exposure to toxic substances was known. ⋯ Bronchoscopy with BAL and transbronchial biopsy were nondiagnostic. While preparing for another diagnostic procedure, the patient gradually developed increasing dyspnea and more frequent convulsions with the progression of lesions on the follow-up chest CT scan. The patient was transferred to our hospital.
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A 79-year-old woman was admitted to the hospital for progressive dyspnea and severe hypoxemia, requiring oxygen supplementation. The dyspnea started approximately 3 to 4 weeks before presentation and was slowly progressive throughout the following weeks. ⋯ In the months leading up to her current presentation there were no changes in medication and no use of antibiotics. She had no known exposure to toxic fumes or substances, she was a nonsmoker, and her family history was unremarkable for autoimmune disorders or interstitial lung disease (ILD).
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Predictive analytic models leveraging machine learning methods increasingly have become vital to health care organizations hoping to improve clinical outcomes and the efficiency of care delivery for all patients. Unfortunately, predictive models could harm populations that have experienced interpersonal, institutional, and structural biases. Models learn from historically collected data that could be biased. ⋯ This strategy follows the lifecycle of machine learning models in health care, namely, identifying the clinical problem, model design, data collection, model training, model validation, model deployment, and monitoring after deployment. To illustrate this approach, we use a hypothetical case of a health system developing and deploying a machine learning model to predict the risk of mortality in 6 months for patients admitted to the hospital to target a hospital's delivery of palliative care services to those with the highest mortality risk. The core ethical concepts of equity and transparency guide our proposed framework to help ensure the safe and effective use of predictive algorithms in health care to help everyone achieve their best possible health.