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BackgroundWhile a large number of well-known knowledge bases (KBs) in life science have been published as Linked Open Data, there are few KBs in Chinese. However, KBs in Chinese are necessary when we want to automatically process and analyze electronic medical records (EMRs) in Chinese. Of all, the symptom KB in Chinese is the most seriously in need, since symptoms are the starting point of clinical diagnosis.ResultsWe publish a public KB of symptoms in Chinese, including symptoms, departments, diseases, medicines, and examinations as well as relations between symptoms and the above related entities. To the best of our knowledge, there is no such KB focusing on symptoms in Chinese, and the KB is an important supplement to existing medical resources. Our KB is constructed by fusing data automatically extracted from eight mainstream healthcare websites, three Chinese encyclopedia sites, and symptoms extracted from a larger number of EMRs as supplements.MethodsFirstly, we design data schema manually by reference to the Unified Medical Language System (UMLS). Secondly, we extract entities from eight mainstream healthcare websites, which are fed as seeds to train a multi-class classifier and classify entities from encyclopedia sites and train a Conditional Random Field (CRF) model to extract symptoms from EMRs. Thirdly, we fuse data to solve the large-scale duplication between different data sources according to entity type alignment, entity mapping, and attribute mapping. Finally, we link our KB to UMLS to investigate similarities and differences between symptoms in Chinese and English.ConclusionsAs a result, the KB has more than 26,000 distinct symptoms in Chinese including 3968 symptoms in traditional Chinese medicine and 1029 synonym pairs for symptoms. The KB also includes concepts such as diseases and medicines as well as relations between symptoms and the above related entities. We also link our KB to the Unified Medical Language System and analyze the differences between symptoms in the two KBs. We released the KB as Linked Open Data and a demo at https://datahub.io/dataset/symptoms-in-chinese.

Background Accumulating evidence supports an association between an unhealthy mental state and low back pain (LBP). However, the degree of the association between mental health and chronic low back pain (CLBP) in the general population is poorly understood. The objective of this study was to analyze the incidence of CLBP in Chinese college students and to examine the association between students’ unhealthy mental states and the prevalence of CLBP. Methods This is a cross-sectional study. A total of 10,000 questionnaires were distributed in the second semester of the 2017–2018 academic year by the School of Medicine, Shanghai JiaoTong University. Eligible participants were students aged ≥ 18 years from randomly selected Chinese colleges. Participants completed a questionnaire survey that included items from the Symptom Checklist-90 (SCL-90) and items on demographic factors, LBP prevalence, quality of life at their university, study-related stress and interpersonal relationships. The evaluation of students’ mental states in the survey was divided into two major parts: direct and indirect indicators. A multivariate logistic regression model was mainly used to explore the relationship between CLBP and the students’ mental health. Results There was a high incidence of CLBP in the college students. Multiple logistic regression analysis indicated that the risk of CLBP increased with increasing scores on the SCL-90, and a clinically unhealthy mental state (scores greater than 3) was significantly associated with CLBP (adjusted odds ratios for depression, anxiety, coercion, paranoia, and interpersonal sensitivity were 7.209, 6.593, 3.959, 4.465, and 4.283, respectively; p < 0.001). Participants who had poor living habits or uncomfortable campus lives and those who experienced heavy academic pressure also showed a higher positive association with CLBP compared with the full sample. Conclusions Unhealthy psychological conditions, which may be attributed to unsatisfying school lives, excessive learning pressure, and uncomfortable interpersonal relationships, represent a risk factor for CLBP in college students. Electronic supplementary material The online version of this article (10.1186/s13034-019-0283-2) contains supplementary material, which is available to authorized users.

A new compact mobile lower limb robotic exoskeleton (MLLRE) has been developed for gait rehabilitation for neurologically impaired patients. This robotic exoskeleton is composed of two exoskeletal orthoses, an active body weight support (BWS) system attached to a motorized mobile base, allowing over-ground walking. The exoskeletal orthosis is optimized to implement the extension and flexion of human hip and knee joints in the sagittal plane. The motor-driven BWS system can actively unload human body weight and track the vertical displacement of the center of mass (COM). This system is compact and easy for therapist to help patient with different weight (up to 100 kg) and height (150–190 cm). Experiments were conducted to evaluate the performance of the robot with a healthy subject. The results show that MLLRE is a useful device for patient to achieve normal over-ground gait patterns.