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Oxford Handbook Of Critical Care 3rd Edition (2...


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Oxford Handbook Of Critical Care 3rd Edition (2...


The LITFL Critical Care Compendium is a comprehensive collection of pages concisely covering the core topics and controversies of critical care. Currently there are over 1,500 entries with more in the works, and all the pages are being constantly revised and improved.


The Oxford Handbook Of Critical Care contains all the important concepts of clinical guidance, compiled in one place. It is designed with the aim of providing the most apt advice quickly when needed in times of emergency in the hospital wards or intensive care units. This third edition of this book covers topics ranging right from critical care organization and management to death and the dying patient, and every critical situation imaginable in between.


All critical care professionals, paramedical staff, nurses, senior medical students or trainee doctors working in intensive care and emergency medicine will find this book to be a valuable supplement for the fact that it is patient-oriented, extremely handy, and functional. Published by Oxford University Press in 2009, it is available in paperback.


Michael Donahoe, MD, specializes in pulmonology and is certified in critical care medicine, internal medicine, and pulmonary disease by the American Board of Internal Medicine. He practices at UPMC Horizon, UPMC Presbyterian, UPMC Shadyside, UPMC Mercy, UPMC East, UPMC McKeesport, and UPMC Magee-Womens Hospital. Dr. Donahoe is affiliated with University of Pittsburgh Physicians, Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine. He completed his medical degree at Hahnemann/Drexel University College.Dr. Donahoe specializes in hospital quality improvement and treating patients with acute respiratory distress syndrome.


Advances in artificial intelligence (AI) and machine learning offer the potential to provide personalized care by taking into account granular patient differences. Machine learning using images, clinical notes, and other electronic health record (EHR) data has been successful in several clinical tasks such as detection of diabetic retinopathy10 and distinguishing between malignant and nonmalignant skin lesions in dermatoscopic images.11 Prior research has established that machine learning using clinical notes to supplement lab tests and other structured data is more accurate than an algorithm using structured data alone in classifying patients with rheumatoid arthritis12 and in predicting mortality13 and the onset of critical care interventions14 in intensive care settings.


This same ability to discern among patients brings with it the risk of amplifying existing biases, which can be especially concerning in sensitive areas like health care.15,16 Because machine learning models are powered by data, bias can be encoded by modeling choices or even within the data itself.17 Ideally, algorithms would have access to exhaustive sources of population EHR data to create representative models for diagnosing diseases, predicting adverse effects, and recommending ongoing treatments.18 However, such comprehensive data sources are not often available, and recent work has demonstrated bias in critical care interventions. For example, recent Canadian immigrants are more likely to receive aggressive care in the ICU than other Canadian residents.19


In contrast to critical care, psychiatry relies more heavily on analysis of clinical notes for patient assessment and treatment. Text is a rich source of unstructured information for machine learning models, but the subjective and expressive nature of the data also makes text a strong potential source of bias.20,21 Racism has established impacts on chronic and acute health,22 which would affect EHR data. In addition, mental health problems of racial groups often depend heavily on the larger social context in which the group is embedded,22 which would also influence clinical prediction based on EHR data. 59ce067264






https://www.cissbigdata.org/group/trial-group/discussion/76bbcb19-bc54-4f62-97ad-312101f4e264

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