The aim of this study was to determine whether volume and localization of intracerebral hematoma affects the six-month prognosis of patients with intracerebral hemorrhage (ICH). Patients and Methods. The study included 75 patients with ICH of both sex and all age groups. ICH, based on CT scan findings, was divided in the following groups: lobar, subcortical, infratentorial, intraventricular haemorrhage and multiple hematomas. Volume of intracerebral hematoma was calculated according to formula V = 0.5 × a × b × c. Intracerebral hematomas, according to the volume, are divided in three groups (0–29 mL, 30–60 mL, and >60 mL). Results. The highest mortality rate was recorded in the group with multiple hematomas (41%), while the lowest in infratentorial (12.8%). The best six-month survival was in patients with a volume up to 29 mL, 30 of them (64%) survived. The highest mortality rate was recorded in patients with the hematoma volume >60 mL (85%). Kaplan-Meier's analysis showed that there was statistical significance between the size of the hematoma and the six-month survival (P < 0.0001). More than half of patients (61.1%) who survived 6 months after ICH were functionally independent (Rankin scale ≤2). Conclusion The volume of hematoma significantly affects six-month prognosis in patients with intracerebral hemorrhage, while localization does not.

Based upon findings and results from our recent research (De Vocht et al., 2011) we propose a generic framework concept for researcher profiling with appliance to the areas of ”Science 2.0” and ”Research 2.0”. Intensive growth of users in social networks, such as Twitter generated a vast amount of information. It has been shown in many previous works that social networks users produce valuable content for profiling and recommendations (Reinhardt et al., 2009; Java et al., 2007; De Vocht et al., 2011). Our research focuses on identifying and locating experts for specific research area or topic. In our approach we apply semantic technologies like (RDFb, SPARQLc), common vocabularies (SIOCd, FOAFe, MOATf, Tag Ontologyg) and Linked Datah (GeoNamesi, COLINDAj) (Berners-Lee, 2006; Bizer et al., 2012) .