OptimCLM: Optimizing clinical language models for predicting patient outcomes via knowledge distillation, pruning and quantization.

Journal: International journal of medical informatics
Published Date: Dec 18, 2024

Abstract

BACKGROUND: Clinical Language Models (CLMs) possess the potential to reform traditional healthcare systems by aiding in clinical decision making and optimal resource utilization. They can enhance patient outcomes and help healthcare management through predictive clinical tasks. However, their real-world deployment is limited due to high computational cost at inference, in terms of both time and space complexity.

Authors

  • Mohammad Junayed Hasan
    Apurba NSU R&D Lab, Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh. Electronic address: mohammad.hasan5@northsouth.edu.
  • Fuad Rahman
    From the Biomedical Engineering, University of Arizona, Tucson, Arizona.
  • Nabeel Mohammed
    Apurba NSU R&D Lab, Department of Electrical and Computer Engineering North South University, Dhaka, Bangladesh.

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