The Electronic Health Record (EHR) contains information useful for clinical, epidemiological and genetic studies. This information of patient symptoms, history, medication and treatment is not completely captured in the structured part of the EHR but is often found in the form of freetext narrative. A major obstacle for clinical studies is finding patients that fit the eligibility criteria of the study. Using EHR in order to automatically identify relevant cohorts can help speed up both clinical trials and retrospective studies (Restificar, Korkontzelos et al. 2013). While the clinical criteria for inclusion and exclusion from the study are explicitly stated in most studies, automating the process using the EHR database of the hospital is often impossible as the structured part of the database (age, gender, ICD9/10 medical codes, etc.’) rarely covers all of the criteria. Many resources such as UMLS (Bodenreider 2004), cTakes (Savova, Masanz et al. 2010), MetaMap (Aronson and Lang 2010) and recently richly annotated corpora and treebanks (Albright, Lanfranchi et al. 2013) are available for processing and representing medical texts in English. Resource poor languages, however, suffer from lack in NLP tools and medical resources. Dictionaries exhaustively mapping medical terms to the UMLS medical meta-thesaurus are only available in a limited number of languages besides English. NLP annotation tools, when they exist for resource poor languages, suffer from heavy loss of accuracy when used outside the domain on which they were trained, as is well documented for English (Tsuruoka, Tateishi et al. 2005; Tateisi, Tsuruoka et al. 2006). In this work we focus on the problem of classifying patient eligibility for inclusion in retrospective study of the epidemiology of epilepsy in Southern Israel. Israel has a centralized structure of medical services which include advanced EHR systems. However, the free text sections of these EHR are written in Hebrew, a resource poor language in both NLP tools and handcrafted medical vocabularies. Epilepsy is a common chronic neurologic disorder characterized by seizures. These seizures are transient signs and/or symptoms of abnormal, excessive, or hyper synchronous neuronal activity in the brain. Epilepsy is one of the most common of the serious neurological disorders (Hirtz, Thurman et al. 2007).