While the use of machine learning methods in clinical decision support has great potential for improving patient care, acquiring standardized, complete, and sufficient training data presents a major challenge for methods relying exclusively on machine learning techniques. Domain experts possess knowledge that can address these challenges and guide model development. We present Advice-Based-Learning (ABLe), a framework for incorporating expert clinical knowledge into machine learning models, and show results for an example task: estimating the probability of malignancy following a non-definitive breast core needle biopsy. By applying ABLe to this task, we demonstrate a statistically significant improvement in specificity (24.0% with p=0.004) without missing a single malignancy. Collaborations between medical domain experts (MDE) and computer science experts (CSE) often involve the use of machine learning to develop predictive models aimed at improving patient care. Unfortunately, standardized, complete, and sufficient training data for machine-learning algorithms is rarely available for a variety of reasons including variability of practice between physicians as well as institutions, low disease prevalence on a population level, and confidentiality issues. The difficulty inherent in collecting large, high quality datasets represents a major challenge in the development of machine learned models for decision support. One of the solutions to this challenge is to incorporate the clinical experience and intuition of MDEs, that may help compensate for a lack of large training datasets.
Besides classical Machine Learning methods, there are also approaches that incorporates practical knowledge into machine learning models REF .
Leveraging Expert Knowledge to Improve Machine-Learned Decision Support Systems
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Computer Science Medicine