The purpose of this paper is to discuss the development of medical informatization in the era of big data.Through literature review and theoretical analysis,the development of medical informatization in the era of big...The purpose of this paper is to discuss the development of medical informatization in the era of big data.Through literature review and theoretical analysis,the development of medical informatization in the era of big data is deeply discussed.The results show that medical informatization has developed rapidly in the era of big data,and its role in clinical decision-making,scientific research,teaching,and management has become increasingly prominent.The development of medical informatization in the era of big data has important purposes and methods,which can produce important results and conclusions and provide strong support for the development of the medical field.展开更多
This study offers a framework for a breast cancer computer-aided treat-ment prediction(CATP)system.The rising death rate among women due to breast cancer is a worldwide health concern that can only be addressed by ear...This study offers a framework for a breast cancer computer-aided treat-ment prediction(CATP)system.The rising death rate among women due to breast cancer is a worldwide health concern that can only be addressed by early diagno-sis and frequent screening.Mammography has been the most utilized breast ima-ging technique to date.Radiologists have begun to use computer-aided detection and diagnosis(CAD)systems to improve the accuracy of breast cancer diagnosis by minimizing human errors.Despite the progress of artificial intelligence(AI)in the medical field,this study indicates that systems that can anticipate a treatment plan once a patient has been diagnosed with cancer are few and not widely used.Having such a system will assist clinicians in determining the optimal treatment plan and avoid exposing a patient to unnecessary hazardous treatment that wastes a significant amount of money.To develop the prediction model,data from 336,525 patients from the SEER dataset were split into training(80%),and testing(20%)sets.Decision Trees,Random Forest,XGBoost,and CatBoost are utilized with feature importance to build the treatment prediction model.The best overall Area Under the Curve(AUC)achieved was 0.91 using Random Forest on the SEER dataset.展开更多
Chronic obstructive pulmonary disease(COPD)is a serious chronic respiratory disease.Improving the ability to identify patients with COPD in primary medical institutions is important to prevent and treat the disease.Wi...Chronic obstructive pulmonary disease(COPD)is a serious chronic respiratory disease.Improving the ability to identify patients with COPD in primary medical institutions is important to prevent and treat the disease.With the continuous development of medical digitization,the application of big data informatization in the medical and health fields has become possible.Recently,applying innovative technologies such as big data analysis,machine learning,and artificial intelligence-assisted decision-making in the medical field has become an interdisciplinary research hotspot.Based on the identification and diagnosis of COPD in the high-risk population,this study proposes a convenient and effective clinical decision support system to help identify patients with COPD in primary health institutions.The results of the preliminary experiments show that the proposed method is convenient and effective compared with the existing methods.展开更多
文摘The purpose of this paper is to discuss the development of medical informatization in the era of big data.Through literature review and theoretical analysis,the development of medical informatization in the era of big data is deeply discussed.The results show that medical informatization has developed rapidly in the era of big data,and its role in clinical decision-making,scientific research,teaching,and management has become increasingly prominent.The development of medical informatization in the era of big data has important purposes and methods,which can produce important results and conclusions and provide strong support for the development of the medical field.
基金N.I.R.R.and K.I.M.have received a grant from the Malaysian Ministry of Higher Education.Grant number:203/PKOMP/6712025,http://portal.mygrants.gov.my/main.php.
文摘This study offers a framework for a breast cancer computer-aided treat-ment prediction(CATP)system.The rising death rate among women due to breast cancer is a worldwide health concern that can only be addressed by early diagno-sis and frequent screening.Mammography has been the most utilized breast ima-ging technique to date.Radiologists have begun to use computer-aided detection and diagnosis(CAD)systems to improve the accuracy of breast cancer diagnosis by minimizing human errors.Despite the progress of artificial intelligence(AI)in the medical field,this study indicates that systems that can anticipate a treatment plan once a patient has been diagnosed with cancer are few and not widely used.Having such a system will assist clinicians in determining the optimal treatment plan and avoid exposing a patient to unnecessary hazardous treatment that wastes a significant amount of money.To develop the prediction model,data from 336,525 patients from the SEER dataset were split into training(80%),and testing(20%)sets.Decision Trees,Random Forest,XGBoost,and CatBoost are utilized with feature importance to build the treatment prediction model.The best overall Area Under the Curve(AUC)achieved was 0.91 using Random Forest on the SEER dataset.
基金This work was supported by the Major Research Program of the National Natural Science Foundation of China(No.91843302).
文摘Chronic obstructive pulmonary disease(COPD)is a serious chronic respiratory disease.Improving the ability to identify patients with COPD in primary medical institutions is important to prevent and treat the disease.With the continuous development of medical digitization,the application of big data informatization in the medical and health fields has become possible.Recently,applying innovative technologies such as big data analysis,machine learning,and artificial intelligence-assisted decision-making in the medical field has become an interdisciplinary research hotspot.Based on the identification and diagnosis of COPD in the high-risk population,this study proposes a convenient and effective clinical decision support system to help identify patients with COPD in primary health institutions.The results of the preliminary experiments show that the proposed method is convenient and effective compared with the existing methods.
