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  "author": "Edward Choi",
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  "author": "Edward Choi",
            
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  "citation": [],
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  "defined_in": "https://doi.org/10.48550/arXiv.1511.05942",
            
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  "doi": "10.57702/pjynm767",
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  "doi": "10.57702/pjynm767",
            
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  "doi_date_published": null,
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  "doi_date_published": "2024-12-16",
            
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  "extra_authors": [
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Mohammad Taha Bahadori",
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      "extra_author": "Mohammad Taha Bahadori",
            
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    },
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    {
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      "extra_author": "Andy Schuetz",
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      "extra_author": "Andy Schuetz",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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      "extra_author": "Walter F. Stewart",
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      "extra_author": "Walter F. Stewart",
            
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    },
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    },
            
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    {
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      "extra_author": "Jimeng Sun",
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      "extra_author": "Jimeng Sun",
            
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      "orcid": ""
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  "name": "doctor-ai--predicting-clinical-events",
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  "notes": "The dataset is a longitudinal time-stamped EHR data from 
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  "notes": "The dataset is a longitudinal time-stamped EHR data from 
            
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260K patients over 8 years. Encounter records (e.g. diagnosis codes, 
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260K patients over 8 years. Encounter records (e.g. diagnosis codes, 
            
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medication codes or procedure codes) were input to RNN to predict 
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medication codes or procedure codes) were input to RNN to predict 
            
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(all) the diagnosis and medication categories for a subsequent 
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(all) the diagnosis and medication categories for a subsequent 
            
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