blog_posts: 5fdf6c52c3
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id | createdDate | title | link | postExcerpt | featuredImageUrl | hash | contributors | modifiedDate | displayDate |
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blog-posts#18-17571 | 2022-11-09 19:14:13 | Predicting diabetic patient readmission using multi-model training on Amazon SageMaker Pipelines | https://aws.amazon.com/blogs/publicsector/predict-diabetic-patient-readmission-using-multi-model-training-amazon-sagemaker-pipelines/ | Diabetes is a major chronic disease that often results in hospital readmissions due to multiple factors. An estimated $25 billion is spent on preventable hospital readmissions that result from medical errors and complications, poor discharge procedures, and lack of integrated follow-up care. If hospitals can predict diabetic patient readmission, medical practitioners can provide additional and personalized care to their patients to pre-empt this possible readmission, thus possibly saving cost, time, and human life. In this blog post, learn how to use machine learning (ML) from AWS to create a solution that can predict hospital readmission – in this case, of diabetic patients – based on multiple data inputs. | https://d2908q01vomqb2.cloudfront.net/9e6a55b6b4563e652a23be9d623ca5055c356940/2022/11/09/hospital-readmission-machine-learning-predictions-1200x600-1.jpg | 5fdf6c52c3 | Abigail Yacat, Shyam Namavaram | 2022-11-09 19:14:13 | 09 Nov 2022 |
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