Text Mining Patient-Doctor Online Forum Data from the Largest Online Health Community in China

The present study uses the data from the largest online health community in China, www.haodf.com, to examine what are the salient topics that Chinese health consumers discussed with their doctors online. The preliminary research found that there are 146,915 posts by patients and 123,059 posts by doctors from Aug. 2006 to Apr. 2014 on this open online forum. In total, there are 10,685 doctors have participated online forum discussion during this time period. The text mining results on topic modeling are still pending. But we already found the promising and unique quality of this data. We are also looking forward to more inspiring research questions to motivate us for this research

What Can Online Doctor Reviews Tell Us? A Deep Learning Assisted Study of Telehealth Service

The present study develops a novel deep learning method which assists text mining of online doctor reviews to extract underlying sentiment scores. Those scores can be used to estimate a healthcare service quality model to investigate how the online doctor reviews impact the online doctor consultation demand. Based on the data from the largest online health platforms in China, our model results show that the underlying sentiment scores have statistically significant impacts on the demand of online doctor consultation. Theoretically, the present study constructs an innovative deep learning algorithm with a better performance than four widely used text mining methods, which can be applied to text mining of many online forums or social media texts. Empirically, our model results show what factors impact the health service quality and online doctor consultation demand, and following those factors, healthcare professionals can improve their service

Analysis of Demographic Characteristics and Psychological Factors of Opioid Addicts in Zhengzhou Area

Objective: To explore the demographic characteristics and psychological factors of patients with opioid addiction. Methods: A random number method was used to select 200 opioid-addicted patients admitted to the 7th People’s Hospital of Zhengzhou from January 2019 to February 2020. Demographic characteristics and psychosocial factors were analyzed. Result: The proportion of opioid addicts aged 21 ~ 30 was the highest; the proportion of men was significantly higher; the proportion of people who is between jobs/unemployed is the highest; the proportion of junior middle school was the highest, and the proportion of unmarried was relatively high; the proportion of opioid addicts with ignorance/curiosity for the cause of first addiction was the highest; the use of suction is snorting, accounting for the highest proportion. According to the analysis of relevant social and psychological factors, the proportion of single parent family group is the highest, the proportion of parent tension is the highest, and the proportion of bad life coping style is relatively high. At the same time, dependent psychology occupies the highest proportion in psychological factors of relapse patients. Conclusion: By analyzing the demographic characteristics of opioid addicts and the psychosocial factors of their addiction, we can strengthen prevention and management for specific groups to reduce the new addition and relapse of opioid addicts

Effects of Material Parameters on Stress Distribution in Casing-cement -formation (CCF) Multilayer Composite System

This work focus on the stress distribution of the casing-cement -formation (CCF) multilayer composite system, which is a borehole system with multiple casings and cement sheathes. Most of the previous relevant studies are based on the traditional CCF system with the single casing and cement sheath, but these results are not adaptive to the CCF system multiple composite system. In this paper, the FEM numerical model of CCF multilayer composite system was constructed. Numerical simulations were calculated and compared with the system which consists of the single casing and cement sheath. Results show that the multilayer composite system possesses better performance. On this basis, the sensitivity analysis of main influence mechanical parameters such as in-situ stress, the elastic of cement sheathes and the elastic of formation are conducted. The cement sheath on the inside, namely cement sheath-1, is sensitive to its elastic modulus; meanwhile, the cement sheath on the outside, namely cement sheath-2, is not so sensitive to the elastic modulus of cement sheath-1. Cement sheath-1 and cement sheath-2 are all sensitive to the elastic modulus of cement sheath-2, and the mises stress of them has opposite trend to the elastic modulus of cement sheath-2. The proper values of elastic modulus of cement sheath-1 and cement sheath-2 are 5GPa and 5GPa to 30GPa, respectively. Under the in-situ stress ratio σh / σH = 0.7, the maximum mises stress of cement sheath-1 and cement sheath-2 increase as the increase of σh, and they are nearly equal when σh=15GPa. This research can be helpful for the design and analysis of CCF multilayer composite system

Point-cloud Transformer for Three-dimensional Electrical Impedance Tomography

Electrical impedance tomography (EIT) is an emerging medical imaging modality that offers nonintrusive, label-free, fast, and portable features. However, the three-dimensional (3-D) EIT image reconstruction problem is thwarted by its high dimensionality and nonlinearity, thus suffering from low image quality. This article proposes a novel algorithm named point-cloud transformer for 3-D EIT image reconstruction (ptEIT) to tackle the challenges of 3-D EIT image reconstruction. ptEIT leverages the nonlinear representation ability of deep learning and effectively addresses the computational cost issue by using irregular-grid representation of the 3-D conductivity distribution in point clouds. The permutation invariant property rooted in the self-attention operator makes ptEIT particularly suitable for processing this type of data, and the objectwise chamfer distance (OWCD) effectively solves the mean-shaped behavior problem encountered in reconstructing multiple objects. Our experimental results demonstrate that ptEIT can simultaneously achieve high accuracy, spatial resolution, and visual quality, outperforming the state-of-the-art 3-D EIT image reconstruction approaches. ptEIT also offers the unique feature of variable resolution and demonstrates strong generalization ability toward different noise levels, showing evident superiority over voxel-based 3-D EIT approaches

Genetic Characteristics Of An Ancient Nomadic Group In Northern China

Nomadic populations have played a significant role in the history of not only China but also in many nations worldwide. Because they had no written language, an important aspect in the study of these people is the discovery of their tombs. It has been generally accepted that Xiongnu was the first empire created by nomadic tribe in the 3rd century B.C. However, little population genetic information is available concerning the Donghu, another flourishing nomadic tribe at the same period because of the restriction of materials until Jinggouzi site was excavated. In order to test the genetic characteristics of ancient people in this site and explore the relationship between Jinggouzis and Donghus, two uniparentally inherited markers were analyzed from 42 human remains in this site, which located in northern China, dated approximately 2,500 years ago. With ancient DNA technology, four mtDNA haplogroups (D, G, C and M10) and one Y chromosome haplogroup (C) were identified using mitochondrial DNA and Y-chromosome single nucleotide polymorphisms (Y-SNPs). Those haplogroups are common in North Asia and East Asia. And the Jinggouzi people were genetically closest to the Xianbeis in ancient populations and to the Oroqens among extant populations, who were all pastoralists. This might indicate that ancient Jinggouzi people were nomads. Meanwhile, according to the genetic data and the evidences in archaeology, we inferred that Jinggouzi people were associated with Donghu. It is of much value to trace the history of Donghu tribe and might show some insight into the ancient nomadic societ

Activation detection in functional near-infrared spectroscopy by wavelet coherence

Functional near-infrared spectroscopy (fNIRS) detects hemodynamic responses in the cerebral cortex by transcranial spectroscopy. However, measurements recorded by fNIRS not only consist of the desired hemodynamic response but also consist of a number of physiological noises. Because of these noises, accurately detecting the regions that have an activated hemodynamic response while performing a task is a challenge when analyzing functional activity by fNIRS. In order to better detect the activation, we designed a multiscale analysis based on wavelet coherence. In this method, the experimental paradigm was expressed as a binary signal obtained while either performing or not performing a task. We convolved the signal with the canonical hemodynamic response function to predict a possible response. The wavelet coherence was used to investigate the relationship between the response and the data obtained by fNIRS at each channel. Subsequently, the coherence within a region of interest in the time-frequency domain was summed to evaluate the activation level at each channel. Experiments on both simulated and experimental data demonstrated that the method was effective for detecting activated channels hidden in fNIRS data