A Pilot Study of the Application of Natural Language Interpretation to Identify Insights on Acute Lymphocytic Leukemia and Its Treatment from Social Media Data

Social media (SM) data is emerging as an important source of information on patient disease and treatment experiences. SM data provide researchers access to unsolicited information shared by patients/caregivers (CGs)/healthcare providers (HCPs), without burdens associated with traditional research methods or influence of interviewer bias. SM facilitates open communication on serious illness in an unstructured forum; offering snap shots of information that highlight important issues prioritized by patients/CGs/HCPs. SM data is growing exponentially, manual review of this noisy “Big Data” is time consuming and impractical. Natural language understanding (NLU) provides an optimal approach to extracting structured data elements from unstructured text. This pilot study aimed to use NLU to aggregate, analyze, and better understand various aspects of patients/CGs/HCPs experiences of Acute lymphocytic leukemia (ALL). ALL is a blood and bone marrow cancer with a significant patient symptom burden and detrimental impact on patient/CG health-related quality of life (HRQOL).