We seek to describe the barriers that people with eating disorders (EDs) face when undertaking a decision about whether to be admitted for inpatient treatment. Data were retrieved from a moderated ED Internet community website. A descriptive phenomenological approach was used to explore the shared experiences of community members who posted information about their viewpoints on inpatient admission. Three themes emerged: (i) 'Can I let others help me?' addressed the question of participants' ability to cooperate with hospital staff; (ii) 'Can I give up my daily routine?' focused on participants' meaningful lives outside of their ED (school, work, family, friends); and (iii) 'Can inpatient treatment work?' revealed a general lack of faith in the ability of inpatient treatment to make a substantial positive contribution. Our findings highlight the difficulties associated with making a decision about inpatient admission and suggest implications for mental-health professionals.

Objective In Hebrew online health communities, participants commonly write medical terms that appear as transliterated forms of a source term in English. Such transliterations introduce high variability in text and challenge text-analytics methods. To reduce their variability, medical terms must be normalized, such as linking them to Unified Medical Language System (UMLS) concepts. We present a method to identify both transliterated and translated Hebrew medical terms and link them with UMLS entities. Materials and Methods We investigate the effect of linking terms in Camoni, a popular Israeli online health community in Hebrew. Our method, MDTEL (Medical Deep Transliteration Entity Linking), includes (1) an attention-based recurrent neural network encoder-decoder to transliterate words and mapping UMLS from English to Hebrew, (2) an unsupervised method for creating a transliteration dataset in any language without manually labeled data, and (3) an efficient way to identify and link medical entities in the Hebrew corpus to UMLS concepts, by producing a high-recall list of candidate medical terms in the corpus, and then filtering the candidates to relevant medical terms. Results We carry out experiments on 3 disease-specific communities: diabetes, multiple sclerosis, and depression. MDTEL tagging and normalizing on Camoni posts achieved 99% accuracy, 92% recall, and 87% precision. When tagging and normalizing terms in queries from the Camoni search logs, UMLS-normalized queries improved search results in 46% of the cases. Conclusions Cross-lingual UMLS entity linking from Hebrew is possible and improves search performance across communities. Annotated datasets, annotation guidelines, and code are made available online (https://github.com/yonatanbitton/mdtel).