Prediction of adverse drug reactions of anti-breast cancer drugs using machine learning

Abstract

Chemotherapy is a representative treatment option for cancers, but one of the challenges during the course of cancer treatment is the possible occurrence of adverse drug reactions (ADRs) caused by an anticancer drug. For example, ADRs such as encephalopathy, radiation recall dermatitis, acute cardiogenic shock and febrile neutropenia have been reported for 5-fluorouracil, an anticancer drug used to treat several cancer types. These ADRs may be caused by a single drug or by interactions with other drugs. Unfortunately, it is extremely difficult to predict potential ADRs a priori because the occurrence of ADR depends on the patient’s genetics and health condition as well as a combination of drugs taken together. To address this problem, we use electric health record (EHR) data of breast cancer patients as a proof-of-concept demonstration to develop a machine learning model that predicts ADRs. For this, administrations of breast cancer patients, diagnostic records, and additional personal information (e.g., age and gender) were used as input data. To deal with high sparsity of EHR data, which has categorical information, factorization machine was applied. Factorization machine can effectively capture key information in an sparse environment. The model classifies whether a patient shows one of pre-defined ADRs as an output. The machine learning model developed in this study can be useful for predicting potential ADRs of various drugs by taking into account a patient’s health condition.