Improving drug sensitivity predictions in precision medicine through active expert knowledge elicitation
This work addresses the problem of small sample sizes in precision medicine for cancer treatment by efficiently integrating expert knowledge, though it is incremental as it builds on existing probabilistic and bandit models.
The paper tackled the challenge of predicting drug sensitivity in precision medicine by incorporating expert knowledge to improve models, reducing prediction error by 8% on a multiple myeloma dataset and cutting expert workload by over 70% using intelligent feedback collection methods.
Predicting the efficacy of a drug for a given individual, using high-dimensional genomic measurements, is at the core of precision medicine. However, identifying features on which to base the predictions remains a challenge, especially when the sample size is small. Incorporating expert knowledge offers a promising alternative to improve a prediction model, but collecting such knowledge is laborious to the expert if the number of candidate features is very large. We introduce a probabilistic model that can incorporate expert feedback about the impact of genomic measurements on the sensitivity of a cancer cell for a given drug. We also present two methods to intelligently collect this feedback from the expert, using experimental design and multi-armed bandit models. In a multiple myeloma blood cancer data set (n=51), expert knowledge decreased the prediction error by 8%. Furthermore, the intelligent approaches can be used to reduce the workload of feedback collection to less than 30% on average compared to a naive approach.