Facebook language predicts depression in medical records.

Depression, the most prevalent mental illness, is underdiagnosed and undertreated, highlighting the need to extend the scope of current screening methods. Here, we use language from Facebook posts of consenting individuals to predict depression recorded in electronic medical records. We accessed the history of Facebook statuses posted by 683 patients visiting a large urban academic emergency department, 114 of whom had a diagnosis of depression in their medical records. Using only the language preceding their first documentation of a diagnosis of depression, we could identify depressed patients with fair accuracy [area under the curve (AUC) = 0.69], approximately matching the accuracy of screening surveys benchmarked against medical records. Restricting Facebook data to only the 6 months immediately preceding the first documented diagnosis of depression yielded a higher prediction accuracy (AUC = 0.72) for those users who had sufficient Facebook data. Significant prediction of future depression status was possible as far as 3 months before its first documentation. We found that language predictors of depression include emotional (sadness), interpersonal (loneliness, hostility), and cognitive (preoccupation with the self, rumination) processes. Unobtrusive depression assessment through social media of consenting individuals may become feasible as a scalable complement to existing screening and monitoring procedures.

Filling in the white space: Spatial interpolation with Gaussian processes and social media data.

Full national coverage below the state level is difficult to attain through survey-based data collection. Even the largest survey-based data collections, such as the CDC's Behavioral Risk Factor Surveillance System or the Gallup-Healthways Well-being Index (both with more than 300,000 responses p.a.) only allow for the estimation of annual averages for about 260 out of roughly U.S. 3,000 counties when a threshold of 300 responses per county is used. Using a relatively high threshold of 300 responses gives substantially higher convergent validity-higher correlations with health variables-than lower thresholds but covers a reduced and biased sample of the population. We present principled methods to interpolate spatial estimates and show that including large-scale geotagged social media data can increase interpolation accuracy. In this work, we focus on Gallup-reported life satisfaction, a widely-used measure of subjective well-being. We use Gaussian Processes (GP), a formal Bayesian model, to interpolate life satisfaction, which we optimally combine with estimates from low-count data. We interpolate over several spaces (geographic and socioeconomic) and extend these evaluations to the space created by variables encoding language frequencies of approximately 6 million geotagged Twitter users. We find that Twitter language use can serve as a rough aggregate measure of socioeconomic and cultural similarity, and improves upon estimates derived from a wide variety of socioeconomic, demographic, and geographic similarity measures. We show that applying Gaussian Processes to the limited Gallup data allows us to generate estimates for a much larger number of counties while maintaining the same level of convergent validity with external criteria (i.e., N = 1,133 vs. 2,954 counties). This work suggests that spatial coverage of psychological variables can be reliably extended through Bayesian techniques while maintaining out-of-sample prediction accuracy and that Twitter language adds important information about cultural similarity over and above traditional socio-demographic and geographic similarity measures. Finally, to facilitate the adoption of these methods, we have also open-sourced an online tool that researchers can freely use to interpolate their data across geographies.

Survival and complications following γ knife radiosurgery or enucleation for ocular melanoma: a 20-year experience.

BACKGROUND: We present our experience in treating ocular melanoma at the National Centre for Stereotactic Radiosurgery in Sheffield, UK over the last 20 years. METHOD: We analysed 170 patients treated with Gamma Knife radiosurgery, recorded the evolution of visual acuity and complication rates, and compared their survival with 620 patients treated with eye enucleation. Different peripheral doses (using the 50% therapeutic isodose) were employed: 50-70 Gy for 24 patients, 45 Gy for 71 patients, 35 Gy for 62 patients. FINDINGS: There was no significant difference in survival between the 35-Gy, 45-Gy and 50- to 70-Gy groups when compared between themselves (p = 0.168) and with the enucleation group (p = 0.454). The 5-year survival rates were: 64% for 35 Gy, 62.71% for 45 Gy, 63.6% for 50-70 Gy and 65.2% for enucleated patients. Clinical variables influencing survival for radiosurgery patients were tumour volume (p = 0.014) and location (median 66.4 vs 37.36 months for juxtapapillary vs peripheral tumours, respectively; p = 0.001), while age and gender did not prove significant. Regarding complications, using 35 Gy led to more than a 50% decrease, when compared with the 45-Gy dose, in the incidence of cataract, glaucoma and retinal detachment. Retinopathy, optic neuropathy and vitreous haemorrhage were not significantly influenced. Blindness decreased dramatically from 83.7% for 45 Gy to 31.4% for 35 Gy (p = 0.006), as well as post-radiosurgery enucleation: 23.9% for 45 Gy vs 6.45% for 35 Gy (p = 0.018). Visual acuity, recorded up to 5 years post-radiosurgery, was significantly better preserved for 35 Gy than for 45 Gy (p = 0.0003). CONCLUSIONS: Using 35 Gy led to a dramatic decrease in complications, vision loss and salvage enucleation, while not compromising patient survival.

Cross-platform and cross-interaction study of user personality based on images on Twitter and Flickr.

Assessing the predictive value of different social media platforms is important to understand the variation in how users reveal themselves across multiple platforms. Most social media platforms allow users to interact in multiple ways: by posting content to the platform, liking others' posts, or building a user profile. While prior studies offer insights into how language use differs across platforms, differences in image usage is less well understood. In this study, we analyzed variation in image content with user personality across three interaction types (posts, likes and profile images) and two platforms, using a unique data set of users who are active on both Twitter and Flickr. Usage patterns on these two social media platforms revealed different aspects of users' personality. Cross-platform data fusion is thus shown to improve personality prediction performance.

Studying User Income through Language, Behaviour and Affect in Social Media.

Automatically inferring user demographics from social media posts is useful for both social science research and a range of downstream applications in marketing and politics. We present the first extensive study where user behaviour on Twitter is used to build a predictive model of income. We apply non-linear methods for regression, i.e. Gaussian Processes, achieving strong correlation between predicted and actual user income. This allows us to shed light on the factors that characterise income on Twitter and analyse their interplay with user emotions and sentiment, perceived psycho-demographics and language use expressed through the topics of their posts. Our analysis uncovers correlations between different feature categories and income, some of which reflect common belief e.g. higher perceived education and intelligence indicates higher earnings, known differences e.g. gender and age differences, however, others show novel findings e.g. higher income users express more fear and anger, whereas lower income users express more of the time emotion and opinions.

Gamma knife surgery for pediatric arteriovenous malformations: a 25-year retrospective study.

OBJECT: The authors present their 25-year experience in treating pediatric arteriovenous malformations (AVMs) to allow comparisons with other historic studies and data in adults. METHODS: Data were collected from a prospectively maintained departmental database selected for age and supplemented by case note review and telephone interviews as appropriate. RESULTS: Three hundred sixty-three patients, ages 1-16 years (mean ± SD, 12 ± 3.2 years), underwent 410 treatments; 4 had planned 2-stage treatments and 43 were retreated subsequent to an initial partial response. Fifty-eight percent received general anesthesia for the procedure. Sixteen percent had previously undergone embolization. The most common presenting symptoms were as follows: hemorrhage (80.2%), epilepsy (8.3%; overall seizure prevalence 19.9%), and migrainous headaches (6.3%). Only 0.28% of the AVMs were incidental findings. The mean lesion volume was 3.75 ± 5.3 cm3 (range 0.01-32.8 cm3), with a median Spetzler-Martin grade of III (range I-V). The mean peripheral (therapeutic) dose was 22.7 ± 2.3 Gy (range 15-25 Gy), corresponding to a mean maximum dose of 43.6 ± 6 Gy (range 25-51.4 Gy). The obliteration rate was 71.3% in patients who received one treatment and 62.5% for retreated patients, with a mean obliteration time of 32.4 and 79.6 months, respectively. The overall obliteration rate was 82.7%. No follow-up data are as yet available for the 4 patients who underwent the staged treatments. Only 4 patients received peripheral doses below 20 Gy, and the AVM was obliterated in 3 of these patients. The other patients received 20, 22.5, or 25 Gy and had obliteration rates of 82.6%, 77.7%, and 86.3%, respectively. The bleeding rate postradiosurgery was 2.2%, and the cumulative complication rate was 3.6%, with radionecrosis being the most common complication (1.1%). CONCLUSIONS: Surprisingly, there was no correlation (p = 0.43) between outcome and radiosurgical dose when that dose was between 20 and 25 Gy, thus suggesting that the lower of these 2 doses may be effective. Radiosurgery for pediatric AVM is safe and effective.