Background: Named entity recognition (NER) on Chinese electronic medical/healthcare records has attracted significantly attentions as it can be applied to building applications to understand these records. Most previous methods have been purely data-driven, requiring high-quality and large-scale labeled medical data. However, labeled data is expensive to obtain, and these data-driven methods are difficult to handle rare and unseen entities. Methods: To tackle these problems, this study presents a novel multi-task deep neural network model for Chinese NER in the medical domain. We incorporate dictionary features into neural networks, and a general secondary named entity segmentation is used as auxiliary task to improve the performance of the primary task of named entity recognition. Results: In order to evaluate the proposed method, we compare it with other currently popular methods, on three benchmark datasets. Two of the datasets are publicly available, and the other one is constructed by us. Experimental results show that the proposed model achieves 91.07% average f-measure on the two public datasets and 87.05% f-measure on private dataset. Conclusions: The comparison results of different models demonstrated the effectiveness of our model. The proposed model outperformed traditional statistical models.

Blood cultures are often performed to detect patients who has a serious illness without infections and patients with bloodstream infections. Early positive blood culture prediction is important, as bloodstream infections may cause inflammation of the body, even organ failure or death. However, existing work mainly adopts statistical models with laboratory indicators, and fails to make full use of textual description information from EHRs. We study the problem of positive blood culture prediction by using neural network model. Specifically, we first construct dataset from raw EHRs. Then we propose a hybrid neural network which incorporates attention based Bi-directional Long Short-Term Memory and Autoencoder networks to fully capture the information in EHRs. In order to evaluate the proposed method, we constructe a dataset which consists of totally 5963 patients who had one or more blood cultures tests during hospitalization. Experimental results show that the proposed neural model gets 91.23% F-measure for this task. The comparison results of different models demonstrated the effectiveness of our model. The proposed model outperformed traditional statistical models.

Background: Mature cystic teratoma (MCT) with meningioma of the ovary is a very rare benign tumor. There is only 3 reports of this disease until June 2019. The aim of the present study was to describe a ovarian mature cystic teratoma containing meningioma and nests of neuroblasts in a 15-year-old girl. Methods: The method used in the present study consists of description of the clinical history, image lab features, and pathological result. Results: The patient complained of a 2-month history of irregular vaginal bleeding. Abdominal computed tomography (CT) showed a large oval cystic-solid mass with septations and fat density shadow, in abdomen pelvic cavity. The cystic part was the main component in the mass. The tumoral solid parts and its internal division could be seen intensified from slight to moderate on contrast-enhanced CT images compared with those on precontrast images, and the solid parts showed heterogeneous enhancement. Neighbouring intestinal tract and the uterus displaced by compression. The pathological examination confirmed the diagnosis. Conclusions: The clinical feature of ovarian mature cystic teratoma with meningioma includes a lack of specificity. Only meticulous recording of the gross features, histopathological examination including immunohistochemistry and supportive clinical and radiological findings to arrive at a correct diagnosis in case of unconventional tumours. If necessary, preoperative puncture can be performed.