{"id":1788,"date":"2022-06-28T14:04:36","date_gmt":"2022-06-28T06:04:36","guid":{"rendered":"https:\/\/www.bebi.ntu.edu.tw\/?p=1788"},"modified":"2022-06-28T14:05:32","modified_gmt":"2022-06-28T06:05:32","slug":"%e7%94%9f%e9%86%ab%e9%9b%bb%e8%b3%87%e6%89%80%e6%95%99%e5%b8%ab%e7%a0%94%e7%a9%b6%e4%ba%ae%e9%bb%9e-111%e5%b9%b46%e6%9c%88%e4%bb%bd%e3%80%8c%e6%ad%90%e9%99%bd%e5%bd%a5%e6%ad%a3%e6%95%99%e6%8e%88","status":"publish","type":"post","link":"https:\/\/www.bebi.ntu.edu.tw\/?p=1788","title":{"rendered":"\u751f\u91ab\u96fb\u8cc7\u6240\u6559\u5e2b\u7814\u7a76\u4eae\u9ede-111\u5e746\u6708\u4efd\u300c\u6b50\u967d\u5f65\u6b63\u6559\u6388\u300d"},"content":{"rendered":"
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\u64b0\u5beb\uff1a\u5b78\u751f\u738b\u99a8<\/div>\n<\/div>\n
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\u65b0\u8208\u50b3\u67d3\u75c5 (EID)\uff0c\u5305\u62ec\u56b4\u91cd\u6025\u6027\u547c\u5438\u7d9c\u5408\u5fb5 (SARS) (2003)\u3001H1N1\u6d41\u611f\u75c5\u6bd2 (2009)\u3001\u4e2d\u6771\u547c\u5438\u7d9c\u5408\u5fb5\u51a0\u72c0\u75c5\u6bd2 (MERS-CoV) (2012) \u548c2019\u5e74\u51a0\u72c0\u75c5\u6bd2\u75c5 (COVID -19) \u5927\u6d41\u884c\uff0c\u8fd1\u5e7e\u5341\u5e74\u51fa\u73fe\u4e26\u5f15\u767c\u4e86\u5168\u7403\u516c\u5171\u885b\u751f\u5371\u6a5f\u3002\u5982\u679c\u6c92\u6709\u73fe\u6709\u7684\u4fdd\u8b77\u6027\u514d\u75ab\uff0cEID\u53ef\u80fd\u6703\u8fc5\u901f\u50b3\u64ad\u4e26\u5728\u5f88\u77ed\u7684\u6642\u9593\u5167\u9020\u6210\u5927\u91cf\u4eba\u54e1\u50b7\u4ea1\u3002\u7576\u91ab\u7642\u6a5f\u69cb\u56e0\u5927\u91cf\u60a3\u8005\u800c\u4e0d\u582a\u91cd\u8ca0\u6642\uff0c\u8b58\u5225\u5177\u6709\u75be\u75c5\u9032\u5c55\u98a8\u96aa\u7684\u75c5\u4f8b\u4ee5\u512a\u5316\u91ab\u7642\u8cc7\u6e90\u914d\u7f6e\u662f\u76f8\u7576\u5fc5\u8981\u7684\u3002<\/div>\n<\/div>\n
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Emerging infectious diseases (EIDs), including the severe acute respiratory syndrome (SARS) (2003), H1N1 influenza virus (2009), Middle East respiratory syndrome coronavirus (MERS-CoV) (2012), and coronavirus disease 2019 (COVID-19) pandemic, emerged and raised global public health crises in recent decades. Without existing protective immunity, an EID may spread rapidly and cause mass casualties in a very short time. It is imperative to identify cases with risk of disease progression for the optimized allocation of medical resources in case medical facilities are overwhelmed with a flood of patients.<\/div>\n<\/div>\n
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Prof. Yen-Jen Oyang and his team are committed to developing machine learning techniques to address this challenge in preventive medicine. In their research, they incorporated only age, sex, and comorbidities as features to build the ML based prediction models for identifying the population at risk of severe diseases before infection. The study has been based on 83,227 hospital admissions with influenza-like illness and analysed the risk effects of 19 comorbidities along with age and gender for severe illness or mortality risk. To solve the problem, the team have developed three types of prediction models, namely, the DT models, the state-of-the-art DNN models, as well as the conventional logistic regression-based prediction models.<\/div>\n<\/div>\n
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In the research, Prof. Oyang and his team have conducted a comprehensive analysis on how to exploit machine learning algorithms to stratify the risk of severe illness or death among hospitalized ILI patients. There were three major findings in this study. Firstly, the three different types of prediction models investigated in this study, namely the DNN models, the LR models, and the proposed DT based models, delivered comparable performance in predicting severe ILI after hospitalization. Secondly, the tree structures of the DT models explicitly illustrated how predictions were made and provide valuable guidelines for clinicians to develop effective strategies for risk stratification of ILI patients. Thirdly, the clinicians can employ the DT models with an appropriate sensitivity level to cope with the availability of medical resources and public health needs in different epidemic stages of an EID disaster. The proposed ML models are of significant merit when health policymakers need to identify high risk populations and then develop a prioritized vaccination strategy accordingly. Moreover, frontline physicians could incorporate the proposed prediction models to triage patients without laboratory tests, in order to discharge patients with minimal risk. The machine learning techniques developed by Prof. Oyang and his team\u2019s research institute will have considerable contributions to the response of both health policymakers and medical personnel during the EID outbreak.<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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