Joint Extraction of Biomedical Events Based on Dynamic Path Planning Strategy and Hybrid Neural Network.

Biomedical event detection is a pivotal information extraction task in molecular biology and biomedical research, which provides inspiration for the medical search, disease prevention, and new drug development. The existing methods usually detect simple biomedical events and complex events with the same model, and the performance of the complex biomedical event extraction is relatively low. In this paper, we build different neural networks for simple and complex events respectively, which helps to promote the performance of complex event extraction.

Predicting anticancer drug sensitivity on distributed data sources using federated deep learning.

Drug sensitivity prediction plays a crucial role in precision cancer therapy. Collaboration among medical institutions can lead to better performance in drug sensitivity prediction. However, patient privacy and data protection regulation remain a severe impediment to centralized prediction studies.

DriverGenePathway: Identifying driver genes and driver pathways in cancer based on MutSigCV and statistical methods.

Although computational methods for driver gene identification have progressed rapidly, it is far from the goal of obtaining widely recognized driver genes for all cancer types. The driver gene lists predicted by these methods often lack consistency and stability across different studies or datasets. In addition to analytical performance, some tools may require further improvement regarding operability and system compatibility.

Double enhanced residual network for biological image denoising.

With the achievements of deep learning, applications of deep convolutional neural networks for the image denoising problem have been widely studied. However, these methods are typically limited by GPU in terms of network layers and other aspects. This paper proposes a multi-level network that can efficiently utilize GPU memory, named Double Enhanced Residual Network (DERNet), for biological-image denoising.

A biomedical event extraction method based on fine-grained and attention mechanism.

Background: Biomedical event extraction is a fundamental task in biomedical text mining, which provides inspiration for medicine research and disease prevention. Biomedical events include simple events and complex events. Existing biomedical event extraction methods usually deal with simple events and complex events uniformly, and the performance of complex event extraction is relatively low.

A blind medical image denoising method with noise generation network.

Background: In the process of medical images acquisition, the unknown mixed noise will affect image quality. However, the existing denoising methods usually focus on the known noise distribution.

Objective: In order to remove the unknown real noise in low-dose CT images (LDCT), a two-step deep learning framework is proposed in this study, which is called Noisy Generation-Removal Network (NGRNet).

Clinical characteristics of remote Zeus robot-assisted laparoscopic cholecystectomy: a report of 40 cases.

Aim: To summarize the performing essentials and analyze the characteristics of remote Zeus robot-assisted laparoscopic cholecystectomy.

Methods: Robot-assisted laparoscopic cholecystectomy was performed in 40 patients between May 2004 and July 2005. The operating procedures and a variety of clinical parameters were recorded and analyzed.

Zeus robot-assisted laparoscopic cholecystectomy in comparison with conventional laparoscopic cholecystectomy.

Background: The robotic surgical system overcomes many technological obstacles of conventional laparoscopic surgery, and possesses enormous clinical applied potential. The aim of this study was to compare the efficacy of Zeus robot-assisted laparoscopic cholecystectomy with conventional laparoscopic cholecystectomy.

Methods: Forty patients undergoing elective cholecystectomy were randomly divided into two groups.

[Laparoscopic cholecystectomy with remote zeus surgical robotic system: report of 16 cases].

Objective: To explore the methodology and operative essentials of laparoscopic cholecystectomy with remote Zeus surgical robotic system.

Methods: Based on strict training and successful experiment in animal model of swine, laparoscopic cholecystectomy using Zeus robotic system was performed on 16 patients with biliary diseases, including choledocholithiasis, cholelithiasis, polyposis of gallbladder, and chronic cholecystitis, 10 males and 16 females, aged 33 (14 approximately 27), 26 April to 31 August 2004. The general data, preoperative preparation time, operation time, amount of bleeding, complications, and hospitalization time were analyzed.

Clinical practice of video-assisted thoracoscopic surgery in children.

OBJECTIVE To estimate the value of video-assisted thoracoscopic surgery (VATS) in diagnosis and treatment of children with chest diseases. METHODS From May 1997 to October 2001, forty-one children (25 boys and 16 girls) with chest diseases received VATS under general anesthesia in our hospital. Their average age was 6.