auto: 2026-04-12T03:55:57Z
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note: Preprint auto-formatted for levineuwirth.org
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note: Preprint auto-formatted for levineuwirth.org
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::: {.annotation .annotation--collapsible}
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::: {.annotation .annotation--static}
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**KEY POINTS**
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<div class="annotation-header">
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<span class="annotation-label">Summary</span>
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<span class="annotation-name">Key points</span>
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</div>
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<div class="annotation-body">
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**Question.** Among adult hospitalizations in a national claims database, does a deep learning model using ICD-10-CM diagnosis codes improve prediction of 30-day unplanned readmission and 30-day postdischarge in-hospital mortality compared with benchmark models based on Charlson and Elixhauser comorbidity indices?
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**Question.** Among adult hospitalizations in a national claims database, does a deep learning model using ICD-10-CM diagnosis codes improve prediction of 30-day unplanned readmission and 30-day postdischarge in-hospital mortality compared with benchmark models based on Charlson and Elixhauser comorbidity indices?
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**Findings.** In this cohort study of 3,226,831 temporally held-out discharges, the ICD-10-CM--based model showed better discrimination than benchmark comorbidity-index models for both outcomes.
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**Findings.** In this cohort study of 3,226,831 temporally held-out discharges, the ICD-10-CM--based model showed better discrimination than benchmark comorbidity-index models for both outcomes.
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**Meaning.** Using the full set of discharge diagnosis codes may improve short-term claims-based outcome prediction beyond summary comorbidity indices.
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**Meaning.** Using the full set of discharge diagnosis codes may improve short-term claims-based outcome prediction beyond summary comorbidity indices.
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</div>
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:::
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:::
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## Introduction (Background and Significance)
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## Introduction (Background and Significance)
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