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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Developing Artificial Intelligence Models for Extracting Oncologic Outcomes from Japanese Electronic Health Records.

Kenji Araki1, Nobuhiro Matsumoto2, Kanae Togo3

  • 1Patient Advocacy Center, University of Miyazaki Hospital, Miyazaki, Japan.

Advances in Therapy
|December 22, 2022
PubMed
Summary

Artificial intelligence models were developed to extract lung cancer treatment responses from electronic health records. The Bidirectional Encoder Representations from Transformers (BERT) model showed promising accuracy, aiding in predicting time to disease progression.

Keywords:
Artificial intelligenceBERTElectronic health records databaseLung cancerReal-world dataRetrospective study

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Area of Science:

  • Oncology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Establishing AI frameworks for extracting oncological outcomes from large databases is challenging.
  • Electronic Health Records (EHRs) contain valuable unstructured data for cancer research.

Purpose of the Study:

  • To develop and evaluate AI models for extracting treatment responses in lung cancer patients.
  • To utilize unstructured EHR data for predicting oncological outcomes.

Main Methods:

  • Developed and compared AI models including Bidirectional Encoder Representations from Transformers (BERT), Naïve Bayes, and Longformer.
  • Trained models on University of Miyazaki Hospital (UMH) data and validated on the Life Data Initiative (LDI) dataset.
  • Extracted treatment responses to calculate time to progression (TTP) and compared AI predictions with manual curation.

Main Results:

  • The BERT model demonstrated superior precision, recall, and F1 scores compared to Naïve Bayes and Longformer on the UMH dataset.
  • BERT model's prediction accuracy remained consistent when applied to the multi-hospital LDI dataset.
  • Kaplan-Meier analysis showed similar time to progression trends between AI-predicted and manually curated data across treatment lines.

Conclusions:

  • AI models, particularly BERT, can effectively extract treatment responses from unstructured EHR data for lung cancer patients.
  • The developed AI framework shows potential for large-scale oncological outcome analysis.
  • Further model refinement is necessary to enhance performance in clinical applications.