From Data Mining of Public Sources to Clinical Decision Support in Personalized Medicine

Personalized medicine enables precise tumor treatment for a patient's molecular genetic profile. To devise optimal targeted treatment plans for patients in a molecular tumor board, physicians must consider alterations on gene- and proteins levels but also cancer cell phenotypes. Machine learning can uncover buried patterns, extract pivotal information, and unveil corresponding insights from available data. Publicly available datasets provide the amounts of data necessary. This work outlines the efficacy of various machine learning algorithms which could eventually serve as clinical decision support in a precision oncology setting. Leveraging algorithms including Random Forest, Decision tree, XGBoost, Logistic regression, Gaussian Naive Bayes, k nearest neighbor, and AdaBoost, we conducted two experiments for the breast invasive carcinoma dataset. Incorporated data includes patient-, molecular- and treatment data. The aim of the investigation was to predict medication treatment or type of treatment based on genetic profile. After preprocessing and application of ML algorithms, the first results were promising. Multiple factors challenge application in clinical care settings without carefully considering the limitations.