The Development of CanPrompt Strategy in Large Language Models for Cancer Care
Bibliographic record
Abstract
Background: The recent revolution in Large Language Models (LLMs) is transforming industries, enhancing communication, and reshaping research methodologies. LLMs have found significant applications across various sectors, notably in finance for stock market predictions, and in healthcare, where complex medical data is analyzed for diagnosis at an early stage, improving diagnostic procedures, and personalized treatment planning. In healthcare, where complex medical data is analyzed for diagnosis at an early stage. Despite the immense potential, challenges such as overwhelming Big Data, model hallucinations, and ethical concerns about patient privacy and bias persist. Method: We implemented novel strategies like CanPrompt to mitigate the accuracy and hallucination concerns to ensure responsible deployment. The CanPrompt strategy utilizes prompt engineering combined with few-shot and in-context learning to significantly enhance model accuracy by generating more relevant answers. The models were tested against a specialized dataset from MedQuAD, focusing on cancer, and evaluated using metrics like ROUGE and BERTScore to assess the semantic and syntactic accuracy of generated responses against validated "Gold Answers". Through this approach, the study seeks to outline the potential and limitations of LLMs in improving cancer care. Result: After applying CanPrompt with models Mistral 7x8b, Falcon 40b, and Llama 3-8b, BERTScore results showed Mistral leading with an accuracy around 84%, Falcon slightly lower, and Llama the least, with respective precision scores also reflecting a similar trend. Conclusion: The study demonstrates the promise of LLMs in cancer care through the introduction of CanPrompt.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".