The era of big data has brought a big revolution that will transform the way we live, work, and think. In medical field, as the development of social economics and medicine since 21 century, the human disease spectrum has been changing, the disease type has been increasing, and the complexity of the etiology, diagnosis and treatment of disease have been gradually increasing. In order to improve the healthy level, and explore the law of disease occurrence and development, we should constantly research to find discipline in enormous knowledge by fully mining and using the big medical data. It will be helpful to improve the level medical information management. And it can be supportive to the diagnosis, treatment, clinical practice and decision-making. We did the review under the background of big data, and the mean contact of this review is about the origin, meaning, classification, features of big data as well as the research process, application and future development of data mining, especially clinical data mining.
ObjectiveTo explore the methods of data management and statistical analysis for longitudinal big data collected from mobile health management applications (APP). MethodsThe data management process and statistical analysis method were proposed by summarizing the characteristics of the data from mobile health management APPs. The methods would be clarified by a practical case: an APP recording female menstruation. ResultsThe data from health management APPs belong to longitudinal big data and the original record of the APP should be reprocessed or computed before conducting statistical analysis. A two-step data cleaning procedure was suggested for data management of the original records and reprocessed data, and longitudinal models such as mixed models was recommended for statistical analysis. ConclusionsThe data from health management APPs could be used for medical research via specific data management and statistical analysis after removing suspicious data. Cloud computing could be a viable method to improve efficiency of the big data analysis of health management APPs.
As a science which focuses on evidence, the decision making process of evidence medicine encounters an opportunity for development in the big data era. The starting point is shifting forward from evidence to data. The big data technology is playing an active role in evidence's collection, process and utilization. Evidence is more objective, righteous, authentic, transparent and easier to collect. Thus, to initiate evidence-based medicine research in the big data era and to structure an evidence-based medicine intelligent service platform, a full-scaled strategy should be developed in order to improve the quality of evidence. To promote the complete publicity of clinical research data, structuralized clinical data standard should be constructed. To provide a pathway to patients' follow-up data, portable and wearable monitoring devices should be popularized. To avoid risks from utilization of clinical research big data, regulations of clinical data usage should be implemented.
Big data technology is an inevitable result of the information age, which not only promotes the development of biomedical science, but also opens up new paths for the development of traditional Chinese medicine (TCM). This paper introduced the application status of big data technology in the field of TCM in recent years, and put forward some thinkings and prospects so as to provide new insights and methods for the future development direction of TCM.
Objective To analyze the data of external fixation instruments (including Ilizarov instruments) used by QIN Sihe orthopaedic surgical team in the treatment of limb deformities in the past 30 years, and to explore the indications for the application of modern external fixation techniques in the correction of limb deformities and individual device configuration selection strategy. Methods According to QIN Sihe orthopaedic surgical team, the use of external fixator between January 1988 and December 2017 was analyzed retrospectively. The total use of external fixation and the proportion of different external fixators were analyzed in gender, different operation time, different age, different parts, and different diseases. Results External fixators were used in 8 113 patients, 69 of them were used simultaneously in both lower extremity surgery, so 8 182 external fixators were used. Among them, there were 4 725 (57.74%) combined external fixators, 3 388 (41.41%) Ilizarov circle fixators, 64 (0.78%) single arm external fixators (including Orthofix), 5 (0.06%) Taylor space external fixators. There were 4 487 males (55.31%) and 3 626 females (44.69%). According to the analysis of different time periods, the number of external fixators increased year by year, and the number of applications increased after 2000. The main age of the patients was 11-30 years old, of which 1 819 sets (22.23%) were used at the age of 21-25 years. The use of the external fixator covered almost all parts of the limbs, with the ankle and toe areas being the most common, reaching 4 664 sets (57.00%), and the upper extremities the least, with 152 sets (1.86%). The 8 113 cases covered more than a dozen disciplines and more than 150 kinds of diseases. The top 5 diseases were poliomyelitis sequelae, cerebral palsy, deformity of lower extremity after spina bifida, traumatic sequelae, and congenital equinovarus foot. Conclusion Ilizarov technique has been widely used in extremity deformity, disability, and complicated orthopedic diseases caused by vascular, lymphoid, nerve, skin, endocrine, and other diseases. The indication of operation is far beyond the scope of orthopedics. The domestic external fixator and its mounting tools can basically meet the requirements of various treatments. The technique of external fixation has entered a new era of tension tissue regeneration under stress control, natural repair of tissue trauma and deformity, and reconstruction of limb function.
ObjectiveScreening the Database from Colorectal Cancer (DACCA) based on West China Hospial data by " Operation Date”, we purposed to analyze the population characteristics of colorectal cancer patients in regional medical center within recent Database Version.MethodsThe DACCA Version was updated in December 12th, 2018. Personal data (including sex, age, blood type, height, weight, and BMI), location data (including provinces, cities, and subordinate areas in Chengdu), occupation and education data, and main diagnosis data were included in the items. Characteristic analysis was performed on each selected data item.ResultsAccording to screening, 9 633 analytical data rows were obtained. Based on the database information, there were 24 consecutive years from 1995 to 2018 into every year. We set 2005 to 2006 as the time node for the database construction. The contribution to database before 2005 (including) was 1 358, while after 2005 (not including) were 8 275. The contribution rate (contribution numbers/years) after 2005 was higher than before 2005 ［1 358/11 vs. 8 275/13, 95% CI was (–625.337, –400.831), P<0.001］. According to gender distribution, total male data were 4 669, female were 3 340, non-checked were 1 624. According to age distribution, age were from 13 to 104 ［(59±13) years］. Linear prediction was used to predict the age distribution with the " year” as the time axis. The results showed the stable linear prediction (\begin{document}$\hat y$\end{document}=0.016 1x+26.54, R2=3.42×105, P=0.601 108). According to height, height were from 138 cm to 192 cm ［(161±7)cm］, linear prediction results showed that the linear variation with height changes by value (\begin{document}$\hat y$\end{document}=0.110 5 x–60.911, R2=0.002 6, P=0.000 272). According to weight, weight were from 27.5 kg to 80.5 kg ［(59.38±10.27) kg］, linear prediction results showed that the linear variation with height changes by value (\begin{document}$\hat y$\end{document}=0.296 5x–537.24, R2=0.010 625, P=2.37×1014). Available 6 884 data showed the difference between serving areas by West China Hospital and official definition of western region. A total of 9 209 data obtained by analyzing main diagnosis, showed that the main site of disease was rectum (68.64%). Sigmoid was the main location of colon cancer (68.64%), and anal-rectal cancer was main of rectal cancer (27.06%).ConclusionPopulation characteristics from DACCA database could initially reflect the trend of increasing weight and BMI of colorectal cancer patients, and also reflect the regional distribution characteristics based on geographic information. They would be the clues for further database research.
ObjectiveBased on recently update Database from Colorectal Cancer (DACCA), we aimed to analyze the characteristics of in-hospital process management from reginal medical center’s colorectal cancer patients.MethodsWe used Version January 29th, 2019 of DACAA being the analyzing source. The items were included date of first out-patient meeting, admitted date, operative date, discharged date, waiting-time, preoperative staying days, postoperative staying days, hospital staying days, and manage protocol, whose characteristics would be analyzed.ResultsWe left 8 913 lines to be analyzed by filtering DACCA. Useful data lines of first out-patient meeting had 3 915, admitted date had 8 144, operative date had 8 049, and discharged date had 7 958. The average of waiting-time were (9.41±0.43) days, and based on timeline trend for line prediction analyzing, which showed R2=0.101 257, P<0.001. The average of preoperative staying days were (5.41±0.04) days, and based on timeline trend for line prediction analyzing, which showed R2=0.023 671, P<0.001. The average of postoperative staying days were (8.99±0.07) days, and based on timeline trend for line prediction analyzing, which showed R2=0.086 177, P<0.001. The average of hospital staying days were (14.43±0.08) days, and based one timeline trend of line prediction analyzing, which showed R2=0.098 44, P<0.001. Analyzable ERAS data were 2 368 lines in DACCA. Total EARS data in 2 368 lines, there were 108 lines (5%) completed and 2 260 lines (95%) incomplete. Pre/post ERAS data in 2 260 lines, there were 150 lines (7%) completed and 2 110 lines (93%) incomplete. Post ERAS data in 2 110 lines, there were 170 lines (8%) completed and 1 940 lines (92%) incomplete.ConclusionsIn recent 20 years, the regional medical center served in-hospital colorectal cancer patients with decreased preoperative staying days, postoperative staying days, and in-hospital staying days from DACCA analyzing, which could prove the service ability had been in improved. Utilization rate of EARS was increased, and also being the main in-hospital process management.