Quality of Life Assessment of Diabetic patients from health-related blogs.

Motivations: People are generating an enormous amount of data to describe their health care experiences, and continuously search information about diseases, symptoms, diagnoses, doctors, treatment options and medicines. The increasing availability of these traces presents an interesting opportunity to enhance timeliness and efficiency of care. By collecting, analyzing and exploiting this information, it is possible to modify or in any case significantly improve our knowledge on the manifestation of a pathology and obtain a more detailed and nuanced vision of patientsu0027 experience, that we call the social of diseases. Materials and methods: In this paper we present a data analytic framework to represent, extract and analyze the phenotype of diseases. To show the effectiveness of our methodology we presents a detailed case study on diabetes. First, we create a high quality data sample of diabetic patientsu0027 messages, extracted from popular medical forums during more than 10 years. Next, we use a topic extraction techniques based on latent analysis and word embeddings, to identify the main complications, the frequently reported symptoms and the common concerns of these patients. Results: We show that a freely manifested perception of a disease can be noticeably different from what is inferred from questionnaires, surveys and other common methodologies used to measure the impact of a disease on the patientsu0027 quality of life. In our case study on diabetes, we found that issues reported to have a daily impact on diabetic patients are diet, glycemic control, drugs and clinical tests. These problems are not commonly considered in Quality of Life assessments, since they are not perceived by doctors as representing severe limitations.