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  "author": "Chirag Nagpal",
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    {
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      "extra_author": "Willa Potosnak",
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    },
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    {
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      "extra_author": "Artur Dubrawski",
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      "description": "",
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      "description": "",
            
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      "name": "survival-analysis",
            
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      "title": "Survival Analysis"
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  "name": "auton-survival",
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  "name": "auton-survival",
            
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  "notes": "Applications of machine learning in healthcare often 
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  "notes": "Applications of machine learning in healthcare often 
            
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require working with time-to-event prediction tasks including 
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require working with time-to-event prediction tasks including 
            
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prognostication of an adverse event, re-hospitalization, and 
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prognostication of an adverse event, re-hospitalization, and 
            
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mortality. Such outcomes are typically subject to censoring due to 
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mortality. Such outcomes are typically subject to censoring due to 
            
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loss of follow up. Standard machine learning methods cannot be applied 
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loss of follow up. Standard machine learning methods cannot be applied 
            
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in a straightforward manner to datasets with censored outcomes.",
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in a straightforward manner to datasets with censored outcomes.",
            
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      "display_name": "survival analysis",
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      "display_name": "survival analysis",
            
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      "id": "4088193f-c3f2-4690-ac3c-a7b1f7fb3b45",
            
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      "name": "survival analysis",
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      "name": "survival analysis",
            
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  "title": "auton-survival",
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  "title": "auton-survival",
            
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  "type": "dataset",
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  "type": "dataset",
            
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  "version": ""
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  "version": ""
            
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}
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}