Detecting Chemotherapeutic Skin Adverse Reactions in Social Health Networks Using Deep Learning

Abstract

Adverse drug reactions (ADRs) occur in nearly all patients undergoing anticancer therapy, contributing to morbidity, therapy disruptions, and rising health care costs.¹ Their identification and characterization are hampered by clinical trials that are underpowered to detect rare events, the division of patients across institutions, patient exclusion from trials, publication editorial delays, and lack of participation and planning in oncology clinical trials of medical disciplines outside of oncology. Postmarket drug surveillance platforms, such as US Food and Drug Administration (FDA) monitoring rely on voluntary, spontaneous reporting and lack temporal advantage over literature. Early recognition of ADRs could substantially improve health outcomes and decrease societal costs. Internet community health forums provide a mechanism for several hundred million individuals to discuss current health concerns and may serve as a resource for computational detection of ADRs. However, the language in social media is highly informal, and expressed medical concepts are often nontechnical, descriptive, and challenging to extract using dictionary-based methods.