Language Models are Few-shot Learners for Prognostic Prediction

By Zekai Chen, Mariann Micsinai Balan, Kevin Brown. 2023

111 citations recorded by the source corpus.

Clinical prediction is an essential task in the healthcare industry. However, the recent success of transformers, on which large language models are built, has not been extended to this domain. In this research, we explore the use of transformers and language models in prognostic prediction for immunotherapy using real-world patients' clinical data and molecular profiles. This paper investigates the potential of transformers to improve clinical prediction compared to conventional machine learning approaches and addresses the challenge of few-shot learning in predicting rare disease areas. The study benchmarks the efficacy of baselines and language models on prognostic prediction across multiple cancer types and investigates the impact of different pretrained language models under few-shot regimes. The results demonstrate significant improvements in accuracy and highlight the potential of NLP in clinical research to improve early detection and intervention for different diseases.

Subfields: Cancer Research, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging, Large Language Models, Medical & Health AI

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Radiology, Nuclear Medicine and Imaging

1. Xiangliang Zhang

In Large Language Models

1. Kun Zhou
2. Ji-rong Wen
3. Xifeng Yan
4. Zekun Li
5. Wei Peng
6. Wayne Xin Zhao
7. Zabir Al Nazi
8. Hong Wang

Citations in the corpus

• Large language models in healthcare and medical domain: A review (2023, background): “The medicine and healthcare industries largely acknowledge the potential of artificial intelligence (AI) to drive substantial progress in the delivery of healthcare.”
• Evaluating Object Hallucination in Large Vision-Language Models (2023, background): “Considering their impressive performance on complex vision-language tasks (Chen et al., 2023; Bai et al., 2023; Li et al., 2023a), it is counter-intuitive that the hallucination problem of LVLMs is so severe.”
• MWP-BERT: A Strong Baseline for Math Word Problems (2021, methodology): “…has been formulated as a pipeline of translating language descriptions to mathematical equations with neural network based encoder-decoder framework (Wang et al., 2018, 2019; Chiang and Chen, 2018; Liu et al., 2019; Li et al., 2019; Xie and Sun, 2019; Zhang et al., 2020b; Shen and Jin, 2020).”
• Limitations of Language Models in Arithmetic and Symbolic Induction (2022, background): “Although these models exhibit surprisingly impressive capabilities in complex arithmetic reasoning tasks, such as MultiArith (Roy and Roth, 2015) and GSM8k (Cobbe et al., 2021), it has also been pointed out that they tend to make certain calculation errors and perform significantly worse when the num”