As the generation and accumulation of massive electronic health records (EHR), how to effectively extract the valuable medical information from EHR has been a popular research topic. During the medical information extraction, named entity recognition (NER) is an essential natural language processing (NLP) task. This paper presents our efforts using neural network approaches for this task. Based on the Chinese EHR offered by CCKS 2019 and the Second Affiliated Hospital of Soochow University (SAHSU), several neural models for NER, including BiLSTM, have been compared, along with two pretrained language models, word2vec and BERT. We have found that the BERT-BiLSTM-CRF model can achieve approximately 75% F1 score, which outperformed all other models during the tests.

The role of the stock market across the overall financial market is indispensable. The way to acquire practical trading signals in the transaction process to maximize the benefits is a problem that has been studied for a long time. This paper put forward a theory of Deep Reinforcement Learning in the stock trading decisions and stock price prediction, the reliability and availability of the model are proved by experimental data, and the model is compared with the traditional model to prove its advantages. From the point of view of stock market forecasting and intelligent decision-making mechanism, this paper proves the feasibility of Deep Reinforcement Learning in financial markets and the credibility and advantages of strategic decision-making.

Biomedical text data, such as electronic medical records (EMR), enable the creation of high-quality and centralized records to keep track of patients' health, as they contain lots of information from which experience can be derived. How to efficiently process and use these large amounts of medical text data to improve clinics' efficiency is an important area of research today.This study conducted a systematic literature review of the research progress of biomedical language models, and deep learning-based biomedical natural language processing (NLP) tasks, and downstream models. By exploring related paradigms in this emerging field and comparing some Chinese and English language models, we found some key points that have not yet been developed or have practical applicability difficulties in Chinese biomedicine.We propose a Chinese biomedical language model series (CMed-LMs) with a detailed downstream task evaluation. By using large-scale transfer learning, language models are introduced with prior knowledge to improve the performance of downstream tasks and solve specific NLP tasks related to the Chinese biomedical field. In addition, a free-form text EMR-based Disease Diagnosis

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