Content Recommendation by Analyzing User Behavior in Online Health Communities

Online health communities (OHCs) are the platforms for patients and their care-givers to search and share health-related information, and have attracted a vast amount of users in recent years. However, health consumers are easily overwhelmed by the overloaded information in OHCs, which makes it inefficient for users to find contents of their interest. This study proposes a framework for content recommendation by analyzing user activities in OHCs that utilizes social network analysis and text mining technology. We model users’ activities by constructing user behavior networks that capture implicit interactions of users, based on which closely related users are detected and user similarities are calculated. Text analysis are performed using topic model to select the threads for final content recommendation. Based on the data collected from a famous Chinese OHCs, we expect that our model could achieve promising results

Toward Sustainable Virtualized Healthcare: Extracting Medical Entities from Chinese Online Health Consultations Using Deep Neural Networks

Increasingly popular virtualized healthcare services such as online health consultations have significantly changed the way in which health information is sought, and can alleviate geographic barriers, time constraints, and medical resource shortage problems. These online patient–doctor communications have been generating abundant amounts of healthcare-related data. Medical entity extraction from these data is the foundation of medical knowledge discovery, including disease surveillance and adverse drug reaction detection, which can potentially enhance the sustainability of healthcare. Previous studies that focus on health-related entity extraction have certain limitations such as demanding tough handcrafted feature engineering, failing to extract out-of-vocabulary entities, and being unsuitable for the Chinese social media context. Motivated by these observations, this study proposes a novel model named CNMER (Chinese Medical Entity Recognition) using deep neural networks for medical entity recognition in Chinese online health consultations. The designed model utilizes Bidirectional Long Short-Term Memory and Conditional Random Fields as the basic architecture, and uses character embedding and context word embedding to automatically learn effective features to recognize and classify medical-related entities. Exploiting the consultation text collected from a prevalent online health community in China, the evaluation results indicate that the proposed method significantly outperforms the related state-of-the-art models that focus on the Chinese medical entity recognition task. We expect that our model can contribute to the sustainable development of the virtualized healthcare industry

Mining medical related temporal information from patients’ self-description

Purpose – The purpose of this paper is to develop a new method to extract medical temporal information from online health communities. Design/methodology/approach – The authors trained a conditional random-filed model for the extraction of temporal expressions. The temporal relation identification is considered as a classification task and several support vector machine classifiers are built in the proposed method. For the model training, the authors extracted some high-level semantic features including co-reference relationship of medical concepts and the semantic similarity among words. Findings – For the extraction of TIMEX, the authors find that well-formatted expressions are easy to recognize, and the main challenge is the relative TIMEX such as “three days after onset”. It also shows the same difficulty for normalization of absolute date or well-formatted duration, whereas frequency is easier to be normalized. For the identification of DocTimeRel, the result is fairly well, and the relation is difficult to identify when it involves a relative TIMEX or a hypothetical concept. Originality/value – The authors proposed a new method to extract temporal information from the online clinical data and evaluated the usefulness of different level of syntactic features in this task

Four-port Silicon Multi-wavelength Optical Router for Photonic networks-on-chip

We design and fabricate a four-port wavelength-selective optical router on silicon-on-insulator wafer for photonic networks-on-chip. The router consists of four basic operation blocks. Each is constructed by one microring resonator (MRR) based add-drop filter rather than the traditional two microrings based 2 × 2 optical crossbar. It can provide multiwavelength routing for each path to increase the aggregate data transmission bandwidth. The possible 12 I/O routing paths are experimentally observed in the transmission spectra and each of them has the ability to route four group wavelengths simultaneously in the measured spectral range. The device has a small footprint ( ~ 78×100 μm2) and low power consumption (6.58 mW)

A Thermally Annealed Mach-Zehnder Interferometer for High Temperature Measurement

An in-fiber Mach-Zehnder interferometer (MZI) for high temperature measurement is proposed and experimentally demonstrated. The device is constructed of a piece of thin-core fiber (TCF) sandwiched between two short sections of multimode fiber (MMF), i.e., a MMF-TCF-MMF structure. A well-defined interference spectrum is obtained owing to the core-mismatch, and the interference dips are sensitive to the ambient temperature. The experimental results show that the proposed interferometer is capable of high temperature measurement up to 875 °C with a sensitivity of 92 pm/°C over repeated measurements. The explored wavelength drop point may limit the measurement range, which can be improved by repeated thermal annealing

Sensitivity-Improved Strain Sensor over a Large Range of Temperatures Using an Etched and Regenerated Fiber Bragg Grating

A sensitivity-improved fiber-optic strain sensor using an etched and regenerated fiber Bragg grating (ER-FBG) suitable for a large range of temperature measurements has been proposed and experimentally demonstrated. The process of chemical etching (from 125 µm to 60 µm) provides regenerated gratings (at a temperature of 680 °C) with a stronger reflective intensity (from 43.7% to 69.8%), together with an improved and linear strain sensitivity (from 0.9 pm/με to 4.5 pm/με) over a large temperature range (from room temperature to 800 °C), making it a useful strain sensor for high temperature environments

Temperature-Independent Fiber Inclinometer Based on Orthogonally Polarized Modes Coupling Using a Polarization-Maintaining Fiber Bragg Grating

A reflection fiber inclinometer is proposed and experimentally demonstrated based on two linearly polarized (LP) modes coupling. The configuration consists of a section of polarization-maintaining fiber (PMF) containing a fiber Bragg grating (FBG) splicing with single mode fiber (SMF). Bending the PMF in the upstream of FBG can induce an additional birefringence of PMF, which results in the intensity changes of two LP modes owing to orthogonal polarization coupling. The experimental results represent that the device shows different bending responses at the angle range from 0° to 40°and from 64° to 88°, respectively. Moreover, the temperature change just shifts the wavelengths of LP modes reflected and does not influence their intensities, which effectively avoid the temperature cross-sensitivity and make it a good candidate for measuring inclinometer and temperature simultaneously