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نتیجه جستجو - MIMIC-III

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU
ISeeU: یادگیری عمیق قابل تفسیر برای پیش بینی مرگ و میر در بخش مراقبت های ویژه-2019
To improve the performance of Intensive Care Units (ICUs), the field of bio-statistics has developed scores which try to predict the likelihood of negative outcomes. These help evaluate the effectiveness of treatments and clinical practice, and also help to identify patients with unexpected outcomes. However, they have been shown by several studies to offer sub-optimal performance. Alternatively, Deep Learning offers state of the art capabilities in certain prediction tasks and research suggests deep neural networks are able to outperform traditional techniques. Nevertheless, a main impediment for the adoption of Deep Learning in healthcare is its reduced interpretability, for in this field it is crucial to gain insight into the why of predictions, to assure that models are actually learning relevant features instead of spurious correlations. To address this, we propose a deep multiscale convolutional architecture trained on the Medical Information Mart for Intensive Care III (MIMIC-III) for mortality prediction, and the use of concepts from coalitional game theory to construct visual explanations aimed to show how important these inputs are deemed by the network. Results show our model attains a ROC AUC of 0.8735 (± 0.0025) which is competitive with the state of the art of Deep Learning mortality models trained on MIMIC-III data, while remaining interpretable. Supporting code can be found at https://github.com/ williamcaicedo/ISeeU.
Keywords: Deep learning | MIMIC-III | ICU | Shapley Values
مقاله انگلیسی
2 An empirical evaluation of deep learning for ICD-9 code assignment using MIMIC-III clinical notes
یک ارزیابی تجربی از یادگیری عمیق برای اختصاص کد ICD-9 با استفاده از یادداشتهای بالینی MIMIC-III-2019
Background and Objective: Code assignment is of paramount importance in many levels in modern hos- pitals, from ensuring accurate billing process to creating a valid record of patient care history. However, the coding process is tedious and subjective, and it requires medical coders with extensive training. This study aims to evaluate the performance of deep-learning-based systems to automatically map clinical notes to ICD-9 medical codes. Methods: The evaluations of this research are focused on end-to-end learning methods without manually defined rules. Traditional machine learning algorithms, as well as state-of-the-art deep learning methods such as Recurrent Neural Networks and Convolution Neural Networks, were applied to the Medical Infor- mation Mart for Intensive Care (MIMIC-III) dataset. An extensive number of experiments was applied to different settings of the tested algorithm. Results: Findings showed that the deep learning-based methods outperformed other conventional ma- chine learning methods. From our assessment, the best models could predict the top 10 ICD-9 codes with 0.6957 F 1 and 0.8967 accuracy and could estimate the top 10 ICD-9 categories with 0.7233 F 1 and 0.8588 accuracy. Our implementation also outperformed existing work under certain evaluation metrics. Conclusion: A set of standard metrics was utilized in assessing the performance of ICD-9 code assignment on MIMIC-III dataset. All the developed evaluation tools and resources are available online, which can be used as a baseline for further research
Keywords: Deep learning | Clinical notes | Machine learning | ICD-9 | Medical codes RNNs CNNs MIMIC-III Code assignment
مقاله انگلیسی
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