# Improving drug sensitivity predictions in precision medicine through active expert knowledge elicitation

## TL;DR

This paper introduces a probabilistic model and intelligent feedback collection methods to incorporate expert knowledge into drug sensitivity prediction, significantly improving accuracy and reducing expert workload in precision medicine.

## Contribution

The paper presents a novel probabilistic framework and two active learning strategies for efficiently integrating expert insights into genomic-based drug response models.

## Key findings

- Expert knowledge reduced prediction error by 8%.
- Intelligent feedback collection decreased expert workload by over 70%.
- Methods are effective on multiple myeloma dataset.

## Abstract

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.