3篇
您的检索式：作者名="Brian Pickering"
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| 1 | Under specification and Aspectual Coercion显示文摘 | MARTIN J PICKERING BRIAN MCELREE STEVEN FRISSON | 2006 | Discourse Processes2006，，2： | 1 |
| 2 | Artificial intelligence and computer simulation models in critical illness显示文摘Widespread implementation of electronic health records has led to the increased use of artificial intelligence(AI)and computer modeling in clinical medicine.The early recognition and treatment of critical illness are central to good outcomes but are made difficult by,among other things,the complexity of the environment and the often non-specific nature of the clinical presentation.Increasingly,AI applications are being proposed as decision supports for busy or distracted clinicians,to address this challenge.Data driven“associative”AI models are built from retrospective data registries with missing data and imprecise timing.Associative AI models lack transparency,often ignore causal mechanisms,and,while potentially useful in improved prognostication,have thus far had limited clinical applicability.To be clinically useful,AI tools need to provide bedside clinicians with actionable knowledge.Explicitly addressing causal mechanisms not only increases validity and replicability of the model,but also adds transparency and helps gain trust from the bedside clinicians for real world use of AI models in teaching and patient care. | Amos Lal Yuliya Pinevich Ognjen Gajic Vitaly Herasevich Brian Pickering | 2020 | World Journal of Critical Care Medicine2020，9，2： | 1 |
| 3 | Automatic quality improvement reports in the intensive care unit: One step closer toward meaningful use显示文摘AIM: To examine the feasibility and validity of electronic generation of quality metrics in the intensive care unit(ICU).METHODS: This minimal risk observational study was performed at an academic tertiary hospital. The Critical Care Independent Multidisciplinary Program at Mayo Clinic identified and defined 11 key quality metrics. These metrics were automatically calculated using ICU Data Mart, a near-real time copy of all ICU electronic medical record(EMR) data. The automatic report was compared with data from a comprehensive EMR review by a trained investigator. Data was collected for 93 randomly selected patients admitted to the ICU during April 2012(10% of admitted adult population). This study was approved by the Mayo Clinic Institution Review Board.RESULTS: All types of variables needed for metric calculations were found to be available for manual and electronic abstraction, except information for availability of free beds for patient-specific time-frames. There was 100% agreement between electronic and manual data abstraction for ICU admission source, admission service, and discharge disposition. The agreement between electronic and manual data abstraction of the time of ICU admission and discharge were 99% and 89%. The time of hospital admission and discharge were similar for both the electronically and manually abstracted datasets. The specificity of the electronically-generated report was 93% and 94% for invasive and non-invasive ventilation use in the ICU. One false-positive result for each type of ventilation was present. The specificity for ICU and in-hospital mortality was 100%. Sensitivity was 100% for all metrics.CONCLUSION: Our study demonstrates excellent accuracy of electronically-generated key ICU quality metrics. This validates the feasibility of automatic metric generation. | Mikhail A Dziadzko Charat Thongprayoon Adil Ahmed Ing C Tiong Man Li Daniel R Brown Brian W Pickering Vitaly Herasevich | 2016 | World Journal of Critical Care Medicine2016，5，2： | 0 |
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