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Related Experiment Video

Updated: Jan 11, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Predicting hospice eligibility among dementia patients using language models.

Thomas H McCoy1,2, Roy H Perlis1,2

  • 1Center for Quantitative Health, Massachusetts General Hospital, Boston, Massachusetts, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|November 11, 2025
PubMed
Summary

Large language models (LLMs) like GPT-4o can estimate 6-month mortality risk in patients with Alzheimer's disease and related dementias (ADRD). This technology may help improve access to hospice care by identifying patients needing supportive services.

Keywords:
Alzheimer's diseaseclinical decision supportdementia careelectronic health records (EHRs)end‐of‐life carehospice eligibilitylarge language models (LLMs)mortality predictionnatural language processing (NLP)prediction modeling

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Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Informatics
  • Geriatric Medicine

Background:

  • Alzheimer's disease and related dementias (ADRD) prevalence is rising, yet hospice care access remains a challenge.
  • Electronic health records (EHRs) contain valuable data for clinical decision support.
  • Large language models (LLMs) show potential for extracting insights from unstructured EHR data.

Purpose of the Study:

  • To evaluate the ability of GPT-4o, a large language model, to estimate 6-month mortality risk in patients with ADRD.
  • To determine if LLM-generated mortality risk predictions can aid in hospice referral decisions.

Main Methods:

  • Analysis of 9872 patients diagnosed with ADRD from two academic medical centers.
  • Utilized GPT-4o to estimate 6-month mortality risk directly from discharge summaries without retraining.
  • Employed Cox regression to assess the association between GPT-4o predictions and actual mortality.

Main Results:

  • GPT-4o predictions effectively stratified 6-month mortality risk (log-rank p < 0.001, AUC = 0.79).
  • Predictions showed a strong association with mortality (adjusted hazard ratio = 31.02, p < 0.001).
  • Performance was consistent across both participating medical centers.

Conclusions:

  • GPT-4o can accurately stratify mortality risk in dementia patients using routine clinical documentation.
  • These LLM-driven risk estimates show promise for facilitating timely hospice referral decisions.
  • Further prospective research is warranted to validate these findings in clinical practice.