A unified framework of medical information annotation and extraction for Chinese clinical text.

Medical information extraction consists of a group of natural language processing (NLP) tasks, which collaboratively convert clinical text to pre-defined structured formats. This is a critical step to exploit electronic medical records (EMRs). Given the recent thriving NLP technologies, model implementation and performance seem no longer an obstacle, whereas the bottleneck locates on a high-quality annotated corpus and the whole engineering workflow. This study presents an engineering framework consisting of three tasks, i.e., medical entity recognition, relation extraction and attribute extraction. Within this framework, the whole workflow is demonstrated from EMR data collection through model performance evaluation. Our annotation scheme is designed to be comprehensive and compatible between the multiple tasks. With the EMRs from a general hospital in Ningbo, China, and the manual annotation by experienced physicians, our corpus is of large scale and high quality. Built upon this Chinese clinical corpus, the medical information extraction system show performance that approaches human annotation. The annotation scheme, (a subset of) the annotated corpus, and the code are all publicly released, to facilitate further research.

Exploring the Spatial Distribution of Rheumatic Diseases and Its Correlation With Temperature and Humidity Among Middle-Aged and Elderly Adults in China.

Objectives: This study aimed to analyze the prevalence of rheumatic diseases and its correlation with temperature and humidity among middle-aged and elderly adults in China from a spatial perspective. Methods: Data on rheumatic diseases among middle-aged and elderly adults were sourced from the 2018 China Health and Retirement Longitudinal Study (CHARLS). Moran's I was applied to explore the spatial autocorrelation of rheumatic diseases. Spatial lag model (SLM) was established to probe the correlation between rheumatic diseases and temperature and humidity. Results: The age-standardized prevalence of rheumatic diseases was 33.2% for middle-aged and elderly adults in China, varying from 12.0% to 51.4% depending on regions. The Global Moran's I was 0.506 (p = 0.001). Average temperature had negative correlation while average relative humidity had positive correlation with age-standardized prevalence of rheumatic diseases in the SLM. Conclusion: The age-standardized prevalence of rheumatic diseases of middle-aged and elderly adults showed spatial autocorrelation in China. We recommend taking measures to prevent rheumatic diseases for the middle-aged and elderly adults, especially for those living in cold and humid regions.

Spatial Characteristics of Life Expectancy and Geographical Detection of Its Influencing Factors in China.

Life expectancy (LE) is a comprehensive and important index for measuring population health. Research on LE and its influencing factors is helpful for health improvement. Previous studies have neither considered the spatial stratified heterogeneity of LE nor explored the interactions between its influencing factors. Our study was based on the latest available LE and social and environmental factors data of 31 provinces in 2010 in China. Descriptive and spatial autocorrelation analyses were performed to explore the spatial characteristics of LE. Furthermore, the Geographical Detector (GeoDetector) technique was used to reveal the impact of social and environmental factors and their interactions on LE as well as their optimal range for the maximum LE level. The results show that there existed obvious spatial stratified heterogeneity of LE, and LE mainly presented two clustering types (high-high and low-low) with positive autocorrelation. The results of GeoDetector showed that the number of college students per 100,000 persons (NOCS) could mainly explained the spatial stratified heterogeneity of LE (Power of Determinant (PD) = 0.89, p < 0.001). With the discretization of social and environmental factors, we found that LE reached the highest level with birth rate, total dependency ratio, number of residents per household and water resource per capita at their minimum range; conversely, LE reached the highest level with consumption level, GDP per capita, number of college students per 100,000 persons, medical care expenditure and urbanization rate at their maximum range. In addition, the interaction of any two factors on LE was stronger than the effect of a single factor. Our study suggests that there existed obvious spatial stratified heterogeneity of LE in China, which could mainly be explained by NOCS.

Geriatric nutritional risk index is associated with mortality in peritoneal dialysis patients.

BACKGROUND: Malnutrition is one of the most common complications among dialysis patients. The Geriatric Nutritional Risk Index (GNRI) is rarely used in dialysis patients, especially peritoneal dialysis (PD). AIM: To use the GNRI to evaluate the initial nutritional state of PD patients and to examine the association between the GNRI and mortality in chronic PD patients. METHODS: We retrospectively examined the medical records at our centre to identify all adults (≥18 years) who had undergone PD for over 3 months before recruitment from January 2005 to December 2017. The correlation between the GNRI and mortality was examined by Kaplan-Meier and Cox proportional hazards analyses. RESULTS: A total of 1804 patients was enrolled in the study. Significant correlations were noted between the initial GNRI and Charlson index, uric acid, blood calcium, potassium, triglycerides, low-density lipoprotein cholesterol, haemoglobin and so on. Multivariate Cox proportional hazards analyses demonstrated that the GNRI was associated with all-cause mortality (hazard ratio = 0.96, P < 0.001, 95% confidence interval: 0.95-0.98) after adjustment. Compared with the lowest GNRI group, all-cause mortality decreased significantly for each level of GNRI after adjusting for various influencing factors, and the mortality risk of the highest GNRI grade decreased by 66%. The Kaplan-Meier analysis survival rate was significantly different among the four groups in terms of all-cause mortality and cardiovascular and cerebrovascular mortality (log-rank test, P < 0.05). CONCLUSIONS: These results demonstrated that the GNRI is significantly associated with mortality and can be a simple, clinically useful marker for the assessment of nutritional status in PD patients.