BackgroundBiomedical named entity recognition(BNER) is a crucial initial step of information extraction in biomedical domain. The task is typically modeled as a sequence labeling problem. Various machine learning algorithms, such as Conditional Random Fields (CRFs), have been successfully used for this task. However, these state-of-the-art BNER systems largely depend on hand-crafted features.ResultsWe present a recurrent neural network (RNN) framework based on word embeddings and character representation. On top of the neural network architecture, we use a CRF layer to jointly decode labels for the whole sentence. In our approach, contextual information from both directions and long-range dependencies in the sequence, which is useful for this task, can be well modeled by bidirectional variation and long short-term memory (LSTM) unit, respectively. Although our models use word embeddings and character embeddings as the only features, the bidirectional LSTM-RNN (BLSTM-RNN) model achieves state-of-the-art performance — 86.55% F1 on BioCreative II gene mention (GM) corpus and 73.79% F1 on JNLPBA 2004 corpus.ConclusionsOur neural network architecture can be successfully used for BNER without any manual feature engineering. Experimental results show that domain-specific pre-trained word embeddings and character-level representation can improve the performance of the LSTM-RNN models. On the GM corpus, we achieve comparable performance compared with other systems using complex hand-crafted features. Considering the JNLPBA corpus, our model achieves the best results, outperforming the previously top performing systems. The source code of our method is freely available under GPL at https://github.com/lvchen1989/BNER.
Code summarization has long been viewed as a challenge in software engineering because of the difficulties of understanding source code and generating natural language. Some mainstream methods combine abstract syntax trees with language models to capture the structural information of the source code and generate relatively satisfactory comments. However, these methods are still deficient in code understanding and limited by the long dependency problem. In this paper, we propose a novel model called Fret, which stands for Functional REinforced Transformer with BERT. The model provides a new way to generate code comments by learning code functionalities and deepening code understanding while alleviating the problem of long dependency. For this purpose, a novel reinforcer is proposed for learning the functional contents of code so that more accurate summaries to describe the code functionalities can be generated. In addition, a more efficient algorithm is newly designed to capture the source code structure. The experimental results show that the effectiveness of our model is remarkable. Fret significantly outperforms all the state-of-the-art methods we examine. It pushes the BLEU-4 score to 24.32 for Java code summarization (14.23% absolute improvement) and the ROUGE-L score to 40.12 for Python. An ablation test is also conducted to further explore the impact of each component of our method.
BACKGROUND: Although growing, the prevalence of the use of health information technology (HIT) by patients to communicate with their providers is not well understood on the population level, nor whether patients are communicating with their providers about their use of HIT. OBJECTIVE: To understand whether patients are communicating with their providers about HIT use and the patient characteristics associated with the communication. DESIGN: Cross-sectional, self-administered survey of a sample of patients across the state of Indiana. PARTICIPANTS: Nine hundred seventy adult participants from across Indiana, 54% female and 79.5% white. MAIN MEASURES: The survey included sections assessing health information-seeking behavior, use of health information technology, and discussions with doctors about the use of HIT. KEY RESULTS: The survey had a 12% response rate. Sixty-three percent of respondent reported going to the Internet as the first source when seeking health information, while only 19% of respondent reported their doctor was their first source. When communicating with doctors electronically, 31% reported using an electronic health record messaging system, 24% used email, and 18% used text messaging. Only 39% of respondents reported having had any conversation about HIT use with their providers. CONCLUSIONS: There remain many unmet opportunities for patients and providers to communicate about HIT use. More guidance for patients and care teams may both help facilitate these conversations and promote optimal use, such as recommendations to ask simple clarification questions and minimize inefficient, synchronous communication when unnecessary.
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