While many studies have explored the use of social media and behavioral changes of individuals, few examined the utility of using social media for suicide detection and prevention. The study by... Show More

While many studies have explored the use of social media and behavioral changes of individuals, few examined the utility of using social media for suicide detection and prevention. The study by Jashinsky et al. identified specific language patterns associated with a set of twelve suicide risk factors. The authors extended these methods to assess the significance of the language used on Twitter for suicide detection. This article quantifies the use of Twitter to express suicide related language, and its potential to detect users at high risk of suicide. The authors searched Twitter for tweets indicative of 12 suicide risk factors. This paper divided Twitter users into two groups: “high risk” and “at risk” based on two of the risk factors (“self-harm” and “prior suicide attempts”) and examined language patterns by computing co-occurrences of terms in tweets which helped identify relationships between suicide risk factors in both groups.

Teaching files are widely used by radiologists in the diagnostic process and for student education. Most hospitals maintain an active collection of teaching files for internal purposes, but many... Show More

Teaching files are widely used by radiologists in the diagnostic process and for student education. Most hospitals maintain an active collection of teaching files for internal purposes, but many teaching files are also publicly available online, some linked to secondary sources. However, public sources offer very limited (and ad-hoc) search capabilities. Based on the previous work on data integration and text-based search, the authors extended their Integrated Radiology Image Search (IRIS 1.1) engine with a new medical ontology, SNOMED CT, and the ICD10 dictionary. IRIS 1.1 integrates public data sources and applies query expansion with exact and partial matches to find relevant teaching files. Using a set of 28 representative queries from multiple sources, the search engine finds more relevant teaching cases versus other publicly available search engines.

Health-care systems that rely on hospitalization for early patient treatment pose a financial concern for governments. In this article, the author suggests a hospitalization prevention program in... Show More

Health-care systems that rely on hospitalization for early patient treatment pose a financial concern for governments. In this article, the author suggests a hospitalization prevention program in which the decision of whether to intervene on a patient depends on a simple decision model and the prediction of the patient risk of an annual length-of-stay using machine learning techniques. These results show that the prevention program achieves significant cost savings relative to several base scenarios for program efficacies greater than or equal to 40% and intervention costs per patient of 100,000 to 700,000 Colombian pesos (i.e., approximately 14% to 100% of the average cost per patient in Colombia statuary health care system). This article also shows how tree-based methods outperform linear regressions when predicting an annual length-of-stay and the final model achieves a lower out-of-sample error compared to those of the Heritage Health Prize.