{"title":"Investigating Critical Risk Factors of Liver Cancer with Deep Neural Networks","authors":"Jinpeng Li, Yaling Tao, Zhunan Li, Ting Cai","doi":"10.3233/atde210238","DOIUrl":"https://doi.org/10.3233/atde210238","url":null,"abstract":"The crude incidence of liver cancer ranks top five among all cancers in China, and the death rate ranks the top two. Identifying critical risk factors of liver cancer helps people adjust their lifestyles to reduce cancer risk. Launched in 2012, Early Diagnosis and Treatment of Urban Cancer project has been carried out in major cities of China, which collected a broad range of epidemiological risk factors including definite, probable and possible causes of cancer. We retrieved data from 2014 to the present and obtained 184 liver cancer cases among 55 thousand people. We explored 84 risk factors and implemented liver cancer prediction model with machine learning algorithms, where deep neural network achieved the best performance using non-clinical information (mean AUC=0.73). We analyzed model parameters to investigate critical risk factors that contribute the most to prediction. Using 50% top-ranking risk factors to train a model, the performance showed no significant difference from that using all risk factors. Using top 10% risk factors induced a sensitivity drop and a lower false positive rate. These phenomena prove that the identified risk factors are critical in liver cancer prediction. This work is a reference in public health research, and provides a scientific lifestyle guideline for individuals to prevent liver cancer based on machine learning technology.","PeriodicalId":386877,"journal":{"name":"Computer Methods in Medicine and Health Care","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116588857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Application of Big Data Technology in the Fight Against New Coronary Pneumonia Epidemic in China","authors":"Xie Yan","doi":"10.3233/atde210245","DOIUrl":"https://doi.org/10.3233/atde210245","url":null,"abstract":"In the fight against New Coronary Pneumonia Epidemic, Chinese Ministry of Health put forward the inevitable requirements of precise policy implementation and scientific epidemic prevention. Accordingly, the big data technology has been applied in the analysis of epidemic dynamic, information inquiry, disease prevention and treatment, and prediction of epidemic trend. And, great success has been achieved in the fight, where the big data technology has played a vital role. This article outlines the main applications of big data technology in the prevention and control of New Coronary Pneumonia Epidemic, and proposes suggestions based on the problems in the application of big data during the epidemic prevention and control period. In the later stage, the integration of big data technology in various fields should be accelerated, information should be further shared and the utility value of data should be maximized.","PeriodicalId":386877,"journal":{"name":"Computer Methods in Medicine and Health Care","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115169683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Continuous Blood Pressure Monitoring Method Based on Multiple Photoplethysmography Features","authors":"Yang Xu, Zhipei Huang, Jiankang Wu, Zhongdi Liu","doi":"10.3233/atde210246","DOIUrl":"https://doi.org/10.3233/atde210246","url":null,"abstract":"Continuous blood pressure monitoring is of great significance for the prevention and early diagnosis of cardiovascular diseases. However, the existing continuous blood pressure monitoring methods, especially the sleeveless blood pressure monitoring methods, are complex and computationally heavy. In this paper, we propose a method, using plethysmography (PPG) signals alone, to estimate continuous blood pressure by extracting multiple PPG features related to intravascular blood flow characteristics. The performance of our method was evaluated using ten minutes synchronized PPG signals and continuous blood pressure from 21 healthy volunteers and 19 patients with hypertension and diabetes. The test results have shown that the absolute mean errors and standard deviation errors between the estimated and referenced blood pressure are 3.22±0.66 mmHg for systolic blood pressure and 2.11±1.11 mmHg for diastolic blood pressure, which meet AAMI (the association for the advancement of medical instrumentation) error acceptance.","PeriodicalId":386877,"journal":{"name":"Computer Methods in Medicine and Health Care","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124741427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Cartography Theory and Map Service Model Based on Constructivism","authors":"Tian Duo, Peng Zhang","doi":"10.3233/atde210251","DOIUrl":"https://doi.org/10.3233/atde210251","url":null,"abstract":"Map is not only the result of geospatial environment cognition, but also a tool for geospatial environment cognition. The new concept advocated by Constructivist cognitive theory is highly consistent with the concept of map service in the era of Internet plus space-time big data. This paper analyzes the geographic information transmission process from the perspective of constructivism, and constructs the geographic information transmission process model. Based on the traditional map cognitive process model, a map cognitive process model based on constructivism is constructed. According to the four elements of “situation, cooperation, communication and meaning construction” advocated by Constructivist cognitive theory, a map service function model based on constructivism is constructed.","PeriodicalId":386877,"journal":{"name":"Computer Methods in Medicine and Health Care","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129623130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}