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Language Models are Few-shot Learners for Prognostic Prediction

Zekai Chen, Mariann Micsinai Balan, Kevin Brown

arXiv:2302.12692v4cs.CLcs.AIcs.LGq-bio.QM

TL;DR

Clinical immunotherapy prediction needs to identify patients likely to respond, while the benefit of transformers for clinical prediction and language models in few-shot prognosis remains an open question. The paper benchmarks transformers and pretrained language models across cancer types and reports improvements across prognostic outcomes, including few-shot gains.

  • Problem

    The paper asks whether transformers improve clinical prediction over conventional approaches and whether language models can support cancer prognostic prediction when labeled samples are scarce.

    Source: §1 · p. 2

  • Method

    The authors benchmark clinical prediction models on MSK-IMPACT across cancer types and compare pretrained language models from different knowledge sources in few-shot settings.

    Source: §1 · p. 2, §2 · p. 3

  • Results

    Across cancer types, the study reports improvements for OS, PFS, and BOR; for pan-cancer OS, ClinTaT’s C-index was 0.724 versus 0.688 for XgBoost.

    Source: §1 · p. 2, §3 · p. 5

  • Takeaways & Limitations

    The reported findings support the potential of transformers and language models for immunotherapy prognostic prediction across multiple cancer types and outcomes.

    Source: §1 · p. 2

    The study uses one 1,479-patient clinical cohort, and does not provide solid evidence that its success extends to other relevant trials.

    Source: §4 · p. 7

Abstract

from arXiv · show

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.

1 Introduction

The paper asks whether transformers can improve clinical prediction and how language models can help when labeled data are scarce. It benchmarks these approaches for immunotherapy prognosis across cancer types and reports improvements across several outcomes.

1 Introduction

  • The motivating clinical need is to identify which patients will benefit from immunotherapy and forecast treatment response and disease progress from clinical features and molecular profiles.Not all patients derive clinical benefit from PD-1/PD-L1 treatment strategies.Source: §1 · p. 1
  • The pilot evaluates response-probability AUC across cancer types with small training samples on a public clinical dataset.The caption identifies the dataset as Chowell et al. (2021).Source: Figure 1 · p. 1
  • The paper asks whether transformers can improve clinical prediction over conventional machine-learning approaches.Clinical data are commonly tabular, and tree-based ensembles are described as mainstream approaches to cancer prognosis.Source: §1 · pp. 1–2
  • Few labeled records can hamper transformer performance, a concern for rare diseases; the paper asks how language models can boost clinical prediction in few-shot settings.The authors note that prior LLM success in clinical information extraction had not yet extended to higher-precision cancer prognostic prediction.Source: §1 · p. 2
  • The study benchmarks baselines and language models on real-world MSK-IMPACT data for prognostic prediction across multiple cancer types and reports accuracy improvements for OS, PFS, and BOR.The benchmark spans melanoma, NSCLC, bladder, and other cancer types.Source: §1 · p. 2

2 LLMs for Few-Shot Clinical Prediction

The paper adapts pretrained language models to clinical tabular prediction by serializing patient features, comparing models trained on different corpora, and jointly optimizing multiple prognostic endpoints.

2 LLMs for Few-Shot Clinical Prediction

  • LLMs are presented as a route to use knowledge from other resources for downstream prediction with minimal tuning when training samples are limited.This motivates applying pretrained models where purely supervised transformer encoders may be hampered by limited samples.Source: §2 · p. 2
  • Clinical columns are serialized as natural-language attribute–value sequences before being encoded by a language model.The paper uses the template “The {attribute} is {value}.”Source: §2 · pp. 2–3
  • The study compares few-shot downstream performance across language models pretrained on domain-specific and domain-agnostic corpora.The authors motivate this comparison by noting that clinical language differs from standard NLP text and that pretraining-source relevance can affect knowledge transfer.Source: §2 · p. 3
  • Multiple endpoints, including OS, PFS, and BOR, are merged into a unified loss for joint training.The experiments use CoxPH loss for OS and PFS and CrossEntropy loss for BOR.Source: §2 · p. 3
  • ClinTaT adds a continuous-feature embedding layer to TabTransformer and concatenates its outputs with categorical-feature embeddings before the transformer backbone.The added layer uses independent linear layers for continuous features.Source: Figure 3 · p. 3

3 Experiments and Results

The experiments evaluate ClinTaT and pretrained language models for immunotherapy response and survival prediction across cancer types, including few-shot settings. ClinTaT performs strongly with full training data, while few-shot results vary by task and language-model pretraining source.

3 Experiments and Results

  • The MSK-IMPACT cohort included 1,479 immune checkpoint blockade-treated patients across 16 cancer types, with up to 16 biological features each; response, overall survival, and progression-free survival were evaluated on a 295-patient test set.Source: §3 · p. 3
  • ClinTaT achieved higher test AUC than conventional baselines across cancer types, with pan-cancer OS C-index 0.724 versus 0.688 for XgBoost and 0.682 for Random Forest, and PFS C-index 0.684 versus 0.671 and 0.666.Both survival comparisons were reported at p < 0.05; Table 3's caption limits the PFS advantage to the full-training setting.Source: Figure 4 · p. 4, §3 · pp. 4–5, Table 2 · p. 5
  • With only 6 samples, ClinTaT scored 0.583 versus Random Forest's 0.637 for OS and 0.585 versus 0.611 for PFS, showing weak generalization in these settings.The paper attributes this to transformers' data-hungry nature and low inductive bias.Source: §3 · p. 5
  • PubMedBERT outperformed the other tested language-model variants across k-shot settings, averaging over 5% improvement; at 4 samples, language-model finetuning improved 9.4% over the baseline.The comparisons concern treatment-response AUC on MSK-IMPACT.Source: §3 · p. 5, Table 4 · p. 6
  • Adding a six-layer transformer encoder on top of language models performed better than using a single linear layer for treatment-response finetuning.The authors present the extra encoder as evidence that added compute complexity can improve clinical-feature representations.Source: Table 5 · p. 6, §3 · p. 6
  • SciBERT performed worse than BioBERT and ClinicalBERT, while vanilla BERT ranked second-best, so the results do not establish that domain-specific pretraining is always necessary.The paper also reports that pretraining from scratch on a domain-specific corpus can outperform mixed-domain pretraining.Source: §3 · p. 6

4 Limitations

The study’s evidence is limited by its single 1,479-patient cohort, narrow cancer-immunotherapy focus, and limited model comparisons; the models’ effect on patient outcomes also depends on clinical integration.

4 Limitations

  • The study uses one 1,479-patient clinical cohort, which may limit generalizability because its demographic distributions may not represent the broader population.The authors note potential bias from differences in gender, age, and race.Source: §4 · p. 7
  • The results concern cancer-immunotherapy prognostic prediction and do not establish that the approach succeeds in other relevant trials.Source: §4 · p. 7
  • The study compares only a limited set of transformers and language models, so other models may perform better on the evaluated tasks.Source: §4 · p. 7
  • Whether high-accuracy predictions improve patient outcomes depends on their integration into clinical decision-making and their impact on care.Source: §4 · p. 7

5 Ethical Considerations

The authors describe safeguards for patient data and ethical concerns around bias, equitable access, and responsible use of predictive technology. They also report that fine-tuning requires relatively modest computation.

5 Ethical Considerations

  • Patient data are de-identified and protected from unauthorized access or use, and the public data received MSKCC approval.Source: §5 · p. 7
  • The authors identify possible cohort-selection and feature-choice bias, and emphasize equitable access when predictive models inform care.Source: §5 · p. 7
  • The paper says its use of NLP and machine learning is guided by transparency, accountability, and patient-centered care.Source: §5 · p. 7
  • Fine-tuning requires limited computation: a single linear layer takes about two hours on one Nvidia A10 GPU, and a six-layer encoder takes under five hours.The pretrained language-model weights are publicly available.Source: §5 · p. 7

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