Redesigning COVID-19 Care With Network Medicine and Machine Learning

John Halamka; Paul Cerrato; Adam Perlman

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Redesigning COVID-19 Care With Network Medicine and Machine Learning

机译：用网络医学和机器学习重新设计Covid-19护理

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Emerging evidence regarding COVID-19 highlights the role of individual resistance and immune function in both susceptibility to infection and severity of disease. Multiple factors influence the response of the human host on exposure to viral pathogens. Influencing an individual’s susceptibility to infection are such factors as nutritional status, physical and psychosocial stressors, obesity, protein-calorie malnutrition, emotional resilience, single-nucleotide polymorphisms, environmental toxins including air pollution and firsthand and secondhand tobacco smoke, sleep habits, sedentary lifestyle, drug-induced nutritional deficiencies and drug-induced immunomodulatory effects, and availability of nutrient-dense food and empty calories. This review examines the network of interacting cofactors that influence the host-pathogen relationship, which in turn determines one’s susceptibility to viral infections like COVID-19. It then evaluates the role of machine learning, including predictive analytics and random forest modeling, to help clinicians assess patients’ risk for development of active infection and to devise a comprehensive approach to prevention and treatment.

机译：关于Covid-19的新兴证据强调了个体抗性和免疫功能在易感染和疾病严重程度中的作用。多种因素影响人宿主对病毒病原体暴露的反应。影响个人对感染的易感性是营养状况，身体和心理社会压力源，肥胖，蛋白质 - 卡路里营养不良，情绪韧性，单核苷酸多态性，环境毒素，包括空气污染和第一手和二手烟草烟雾，睡眠习惯，休眠习惯，休眠习惯，药物诱导的营养缺陷和药物诱导的免疫调节效果，以及营养密集食物和空卡路里的可用性。本综述审查了影响宿主病原源关系的交互辅因子的网络，这反过来决定了一个对Covid-19等病毒感染的易感性。然后，它评估机器学习的作用，包括预测性分析和随机森林建模，帮助临床医生评估患者的发育患者的积极感染，并设计综合性的预防和治疗方法。

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《Mayo Clinic Proceedings: Innovations, Quality & Outcomes》 |2020年第6期|共8页
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John Halamka; Paul Cerrato; Adam Perlman;
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AI artificial intelligenceHbAltsubgt1clt/subgthemoglobin Altsubgt1clt/subgtHIV human immunodeficiency virusML machine learningSARS-CoV-2 severe acute respiratory syndrome coronavirus 2;

机译：AI人工智能智慧＆ltsub＆gt1c＆lt / sub＆gthemoglobin a＆ltsub＆gt1c＆gt1c＆lt / sub＆gthiv人类免疫缺陷病毒机器学习ars-cov-2严重急性呼吸综合征coronavirus 2;

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1. A.Martin1,N.Boyle2,J.Cooke3,S.P.Kennelly4,R.Martin5,M.Mulroy6,M.O’Connor7,S.O'Keeffe8,D.O’Neill4OnbehalfoftheIrishSocietyofPhysiciansinGeriatricMedicine.1.BeaumontHospital,Dublin.2.St.Vincent’sUniversityHospital,Dublin.3.UniversityHospitalWaterford.4.TallaghtUniversityHospital,Dublin.5.ConnollyHospital,Dublin.6.OurLadyofLourdesHospital,Drogheda.7.CorkUniversityHospital.8.GalwayUniversityHospital.AbstractResidents in nursing home care have borne a disproprtionate morbidity and mortality in the COVID-19 pandemic in comparison to the general population. Although the high rate of infection, morbidity and mortality in older people living in nursing homes may be attributable to increased levels of frailty and comorbidity in residents, the physical infrastructure and governance structures within nursing homes is also likely to be highly significant. The authors present, on behalf of Irish Society of Physicians in Geriatric Medicine, a position paper on changes that should be implemented to enhance the safety and quality of care for nursing home residents in Ireland. IntroductionResidentsinnursinghomesrepresentagroupwithhighlevelsofdisabilityandco-morbiditywhichrenderthemparticularlyvulnerabletoexternalandinternalstressors1,asexemplifiedbythehighdeathrateinIrishnursinghomesduringtheCOVID-19pandemic.Atthelastcensusin2016were22,762peopleover65yearsofage(3.7%)listedasresidentinnursinghomesinIreland2.Althoughthiswasadecreaseintheproportionoftheolderpopulationinresidentialcarefrom2011itreflectsanactualincreaseof9.4%inpeoplelivinginnursinghomesinthiscountry.Asoursocietyagesitwillbeincreasinglyimportanttoprovidehighqualitycareforthisvulnerablegroup.TheIrishSocietyofPhysiciansinGeriatricMedicine(ISPGM)haspromotedthedevelopmentofimprovedstandardsofcareinresidentialcareforseveraldecadesandwasthefirstIrishprofessionalgrouptoformulateimprovedstandardsofcarein2001,publishedintheIrishMedicalJournal3. [J] . Irish medical journal. . 2020,第9期

机译：A.Martin1，N.Boyle2，J.Cooke3，SPKennelly4，R.Martin5，M.Mulroy6，M.O'Connor7，S.O'Keeffe8，D.O'Neill4OnbehalfoftheIrishSocietyofPhysiciansinGeriatricMedicine.1.BeaumontHospital，Dublin.2.St .Vincent'sUniversityHospital，Dublin.3.UniversityHospitalWaterford.4.TallaghtUniversityHospital，Dublin.5.ConnollyHospital，Dublin.6.OurLadyofLourdesHospital，在家庭护理Drogheda.7.CorkUniversityHospital.8.GalwayUniversityHospital.AbstractResidents已经结出一个disproprtionate发病率和死亡率在COVID-19大流行相比于普通人群。虽然感染，发病率和死亡率在生活在养老院的老年人率高可能是由于增加了居民的脆弱性和合并症的水平，养老院内的物理基础设施和治理结构，也可能是非常显著。作者提出，对应该实施，加强安全和医疗质量养老院在爱尔兰的变化代表在老年医学，意见书医生的爱尔兰社会。 IntroductionResidentsinnursinghomesrepresentagroupwithhighlevelsofdisabilityandco-morbiditywhichrenderthemparticularlyvulnerabletoexternalandinternalstressors1，asexemplifiedbythehighdeathrateinIrishnursinghomesduringtheCOVID-19pandemic.Atthelastcensusin2016were22,762peopleover65yearsofage（3.7％）listedasresidentinnursinghomesinIreland2.Althoughthiswasadecreaseintheproportionoftheolderpopulationinresidentialcarefrom2011itreflectsanactualincreaseof9.4％inpeoplelivinginnursinghomesinthiscountry.Asoursocietyagesitwillbeincreasinglyimportanttoprovidehighqualitycareforthisvulnerablegroup.TheIrishSocietyofPhysiciansinGeriatricMedicine（ISPGM）haspromotedthedevelopmentofimprovedstandardsofcareinresidentialcareforseveraldecadesandwasthefirstIrishprofessionalgrouptoformulateimprovedstandardsofcarein2001，publishedintheIrishMedicalJournal3。
2. Machine learning-based Analysis of COVID-19 Pandemic Impact on US Research Networks [J] . Mariam Kiran, Scott Campbell, Fatema Bannat Wala, Computer communication review . 2021,第4期

机译：基于机器学习的Covid-19大流行对美国研究网络的影响分析
3. Identifying propaganda from online social networks during COVID-19 using machine learning techniques [J] . Akib Mohi Ud Din Khanday, Qamar Rayees Khan, Syed Tanzeel Rabani International Journal of Information Technology . 2021,第1期

机译：使用机器学习技术在Covid-19期间识别来自在线社交网络的宣传
4. Indian Health Care System is Ready to Fight Against COVID-19 A Machine Learning Tool for Forecast the Number of Beds [C] . Shakti Nagpal, Vijay Anant Athavale, Ashwini Kumar Saini, International Conference on Parallel, Distributed and Grid Computing . 2020

机译：印度医疗保健系统准备打击Covid-19机器学习工具，用于预测床位
5. Design, development and evaluation of asynchronous learning networks in primary care medicine: An application of biomedical informatics engineering principles in medical education. [D] . Alrajeh, Nabil Ali. 2001

机译：初级保健医学中异步学习网络的设计，开发和评估：生物医学信息工程原理在医学教育中的应用。
6. Redesigning COVID-19 Care With Network Medicine and Machine Learning [O] . John Halamka, Paul Cerrato, Adam Perlman 2020

机译：用网络医学和机器学习重新设计Covid-19护理
7. Redesigning COVID-19 Care With Network Medicine and Machine Learning [O] . John Halamka, Paul Cerrato, Adam Perlman 2020

机译：用网络医学和机器学习重新设计Covid-19护理

Redesigning COVID-19 Care With Network Medicine and Machine Learning

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