Biblio

Natural language processing (NLP) have been recently used to extract clinical information from free text in Electronic Health Record (EHR). In clinical NLP one challenge is that the meaning of clinical entities is heavily affected by assertion modifiers such as negation, uncertain, hypothetical, experiencer and so on. Incorrect assertion assignment could cause inaccurate diagnosis of patients' condition or negatively influence following study like disease modeling. Thus, clinical NLP systems which can detect assertion status of given target medical findings (e.g. disease, symptom) in clinical context are highly demanded. Here in this work, we propose a deep-learning system based on word embedding, RNN and attention mechanism (more specifically: Attention-based Bidirectional Long Short-Term Memory networks) for assertion detection in clinical notes. Unlike previous state-of-art methods which require knowledge input or feature engineering, our system is a knowledge poor machine learning system and can be easily extended or transferred to other domains. The evaluation of our system on public benchmarking corpora demonstrates that a knowledge poor deep-learning system can also achieve high performance for detecting negation and assertions comparing to state-of-the-art systems.

Mobile Healthcare Networks (MHN) continuouslycollect the patients' health data sensed by wearable devices, andanalyze the collected data pre-processed by servers combinedwith medical histories, such that disease diagnosis and treatmentare improved, and the heavy burden on the existing healthservices is released. However, the network is vulnerable to Sybilattacks, which would degrade network performance, disruptproceedings, manipulate data or cheat others maliciously. What'smore, the user is reluctant to leak identity privacy, so the identityprivacy preserving makes Sybil defenses more difficult. One ofthe best choices is mutually authenticating each other with noidentity information involved. Thus, we propose a fine-grainedauthentication scheme based on Attribute-Based Signature (ABS)using lattice assumption, where a signer is authorized by an at-tribute set instead of single identity string. This ABS scheme usesFiat-Shamir framework and supports flexible threshold signaturepredicates. Moreover, to anonymously guarantee integrity andavailability of health data in MHN, we design an anonymousanti-Sybil attack protocol based on our ABS scheme, so thatSybil attacks are prevented. As there is no linkability betweenidentities and services, the users' identity privacy is protected. Finally, we have analyzed the security and simulated the runningtime for our proposed ABS scheme.