The social phenotype: Extracting a patient-centered perspective of diabetes from health-related blogs.

MOTIVATIONS: It has recently been argued [1] that the effectiveness of a cure depends on the doctor-patient shared understanding of an illness and its treatment. Although a better communication between doctor and patient can be pursued through dedicated training programs, or by collecting patients' experiences and symptoms by means of questionnaires, the impact of these actions is limited by time and resources. In this paper we suggest that a patient-centered view of a disease - as well as potential misalignment between patient and doctor focuses - can be inferred at a larger scale through automated textual analysis of health-related forums. People are generating an enormous amount of social data to describe their health care experiences, and continuously search information about diseases, symptoms, diagnoses, doctors, treatment options and medicines. By automatically collecting, analyzing and exploiting this information, it is possible to obtain a more detailed and nuanced vision of patients' experience, that we call the "social phenotype" of diseases. MATERIALS AND METHODS: As a use-case for our analysis, we consider diabetes, a widespread disease in most industrialized countries. We create a high quality data sample of diabetic patients' messages in Italy, extracted from popular medical forums during more than 10 years. Next, we use a state-of-the-art topic extraction technique based on generative statistical models improved with word embeddings, to identify the main complications, the frequently reported symptoms and the common concerns of these patients. Finally, in order to detect differences in focus, we compare the results of our analysis with available quality of life (QoL) assessments obtained with standard methodologies, such as questionnaires and survey studies. RESULTS: We show that patients with diabetes, when accessing on-line forums, express a perception of their disease in a way that might be noticeably different from what is inferred from published QoL assessments on diabetes. In our study, we found that issues reported to have a daily impact on these patients are diet, glycemic control, drugs and clinical tests. These problems are not commonly considered in QoL assessments, since they are not perceived by doctors as representing severe limitations. Although limited to the case of Italian diabetic patients, we suggest that the methodology described in this paper, which is language and disease agnostic, could be applied to other diseases and countries, since misalignment between doctor and patients, and the importance of collecting unbiased patient perceptions, has been emphasized in many studies ([2,3]inter alia). Extracting the social phenotype of a disease might help acquiring patient-centered information on health care experiences on a much wider scale.

Can Twitter Be a Source of Information on Allergy? Correlation of Pollen Counts with Tweets Reporting Symptoms of Allergic Rhinoconjunctivitis and Names of Antihistamine Drugs.

Pollen forecasts are in use everywhere to inform therapeutic decisions for patients with allergic rhinoconjunctivitis (ARC). We exploited data derived from Twitter in order to identify tweets reporting a combination of symptoms consistent with a case definition of ARC and those reporting the name of an antihistamine drug. In order to increase the sensitivity of the system, we applied an algorithm aimed at automatically identifying jargon expressions related to medical terms. We compared weekly Twitter trends with National Allergy Bureau weekly pollen counts derived from US stations, and found a high correlation of the sum of the total pollen counts from each stations with tweets reporting ARC symptoms (Pearson's correlation coefficient: 0.95) and with tweets reporting antihistamine drug names (Pearson's correlation coefficient: 0.93). Longitude and latitude of the pollen stations affected the strength of the correlation. Twitter and other social networks may play a role in allergic disease surveillance and in signaling drug consumptions trends.

Twitter mining for fine-grained syndromic surveillance.

BACKGROUND: Digital traces left on the Internet by web users, if properly aggregated and analyzed, can represent a huge information dataset able to inform syndromic surveillance systems in real time with data collected directly from individuals. Since people use everyday language rather than medical jargon (e.g. runny nose vs. respiratory distress), knowledge of patients' terminology is essential for the mining of health related conversations on social networks. OBJECTIVES: In this paper we present a methodology for early detection and analysis of epidemics based on mining Twitter messages. In order to reliably trace messages of patients that actually complain of a disease, first, we learn a model of naïve medical language, second, we adopt a symptom-driven, rather than disease-driven, keyword analysis. This approach represents a major innovation compared to previous published work in the field. METHOD: We first developed an algorithm to automatically learn a variety of expressions that people use to describe their health conditions, thus improving our ability to detect health-related "concepts" expressed in non-medical terms and, in the end, producing a larger body of evidence. We then implemented a Twitter monitoring instrument to finely analyze the presence and combinations of symptoms in tweets. RESULTS: We first evaluate the algorithm's performance on an available dataset of diverse medical condition synonyms, then, we assess its utility in a case study of five common syndromes for surveillance purposes. We show that, by exploiting physicians' knowledge on symptoms positively or negatively related to a given disease, as well as the correspondence between patients' "naïve" terminology and medical jargon, not only can we analyze large volumes of Twitter messages related to that disease, but we can also mine micro-blogs with complex queries, performing fine-grained tweets classification (e.g. those reporting influenza-like illness (ILI) symptoms vs. common cold or allergy). CONCLUSIONS: Our approach yields a very high level of correlation with flu trends derived from traditional surveillance systems. Compared with Google Flu, another popular tool based on query search volumes, our method is more flexible and less sensitive to changes in web search behaviors.

Influenza-like illness surveillance on Twitter through automated learning of naïve language.

Twitter has the potential to be a timely and cost-effective source of data for syndromic surveillance. When speaking of an illness, Twitter users often report a combination of symptoms, rather than a suspected or final diagnosis, using naïve, everyday language. We developed a minimally trained algorithm that exploits the abundance of health-related web pages to identify all jargon expressions related to a specific technical term. We then translated an influenza case definition into a Boolean query, each symptom being described by a technical term and all related jargon expressions, as identified by the algorithm. Subsequently, we monitored all tweets that reported a combination of symptoms satisfying the case definition query. In order to geolocalize messages, we defined 3 localization strategies based on codes associated with each tweet. We found a high correlation coefficient between the trend of our influenza-positive tweets and ILI trends identified by US traditional surveillance systems.