An Interpretable Machine Learning Framework for Accurate Severe vs Non-Severe COVID-19 Clinical Type Classification

Chen, Yuanfang; Ouyang, Liu; Bao, Sheng; Li, Qian; Han, Lei; Zhang, Hengdong; Zhu, Baoli; Xu, Mengyi; Liu, Jie; Ge, Yaorong; Chen, Shi

doi:10.2139/ssrn.3638427

Cited by 20 publications

(11 citation statements)

References 32 publications

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“…Besides, the research project regarding this study was approved by Koc University Ethics Committee (2020.269.IRB1.092) as well. Despite of existing many parameters regarding the hematological measurements regarding COVID-19 and disease histories of patients, the features used in analysis were collected in accordance with the literature and expert knowledge [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] . The short description of these features are given in Table 1 .…”

Section: Discussionmentioning

confidence: 99%

“…The coronavirus that firstly emerged in Wuhan China in 2019 and has influenced human beings on a global level, defined as a worldwide epidemic by the World Health Organization in March 2020 [2] . For this reason, many studies related to the COVID-19 pandemic have focused on identifying some risk factors and assessing of disease severity and prognosis of infected patients [3] , [4] , [5] . For instances, Chen et al.…”

Section: Introductionmentioning

confidence: 99%

“…For instances, Chen et al. [3] classified the COVID-19 clinical types (severe vs. non-severe) to reduce the risk of overloading the healthcare system. They used totally 52 features related to comorbidity, symptoms and laboratory testing results, and the proposed model were estimated by Random Forest (RF).…”

Section: Introductionmentioning

confidence: 99%

“…Top ten features were listed as age, hypertension, cardiovascular disease, gender, diabetes; d -Dimer, hsTNI, absolute neutrophil count, IL-6, and LDH, in descending order and the RF's accuracy is found above 99%. They emphasized that comorbidities and symptoms can be used as an initial screening tool to detect the disease's severity [3] .…”

Section: Introductionmentioning

confidence: 99%

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Clinical prognosis evaluation of COVID-19 patients: An interpretable hybrid machine learning approach

Kocadağlı

Baygül

Gökmen

et al. 2022

Current Research in Translational Medicine

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Clinical prognosis evaluation of COVID-19 patients: An interpretable hybrid machine learning approach

Kocadağlı

Baygül

Gökmen

et al. 2022

Current Research in Translational Medicine

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“…These approaches make predictions relying on basic patient information, clinical symptoms [31][32][33], as well as travel history [34] and discharge time of hospitalized patients [16]. Some other efforts focus on identifying patients requiring specialized care, namely hospitalization and/or specialized care units [35][36][37], or patients at a higher fatality risk [38,39].…”

Section: Introductionmentioning

confidence: 99%

Identification of high-risk COVID-19 patients using machine learning

Quiroz-Juárez

Gómez

Hoyo-Ulloa

et al. 2021

Preprint

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The current COVID-19 public health crisis, caused by SARSCoV- 2 (severe acute respiratory syndrome coronavirus 2), has produced a devastating toll both in terms of human life loss and economic disruption. In this paper we present a machine-learning algorithm capable of identifying whether a given patient (actually infected or suspected to be infected) is more likely to survive than to die, or vice-versa. We train this algorithm with historical data, including medical history, demographic data, as well as COVID-19-related information. This is extracted from a database of confirmed and suspected COVID-19 infections in Mexico, constituting the official COVID-19 data compiled and made publicly available by the Mexican Federal Government. We demonstrate that the proposed method can detect high-risk patients with high accuracy, in each of four identified treatment stages, thus improving hospital capacity planning and timely treatment. Furthermore, we show that our method can be extended to provide optimal estimators for hypothesis-testing techniques commonly-used in biological and medical statistics. We believe that our work could be of use in the context of the current pandemic in assisting medical professionals with real-time assessments so as to determine health care priorities.

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Artificial intelligence for COVID-19: battling the pandemic with computational intelligence

Xiao

et al. 2022

Intelligent Medicine

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The new coronavirus disease 2019 (COVID-19) has become a global pandemic leading to over 180 million confirmed cases and nearly 4 million deaths until June, 2021, according to the World Health Organization. Since the initial reported from December 2019 in Wuhan, China, COVID-19 has demonstrated a high transmission rate (with a R0 > 2), a diverse set of clinical characteristics (e.g., high rate of hospital and intensive care unit admission rates, multi-organ dysfunction for critically ill patients due to hyperinflammation, thrombosis, etc.), and tremendous burden on health care systems around the world. To understand the serious and complex disease and develop effective control, treatment, and prevention strategies, researchers from different disciplines have been making significant efforts from different aspects including epidemiology and public health, biology and genomic medicine, as well as clinical care and patient management. In recent years, artificial intelligence (AI) has been introduced into the healthcare field to aid clinical decision-making for disease diagnosis and treatment such as detecting cancer based on medical images and has achieved superior performance in multiple data-rich application scenarios. In the COVID-19 pandemic, AI techniques have also been used as a powerful tool to overcome the complex disease. In this context, the goal of this study is to review existing studies on applications of AI techniques in combating the COVID-19 pandemic. Specifically, these efforts can be grouped into the fields of epidemiology, therapeutics, clinical research, social and behavioral studies and are summarized. Potential challenges, directions, and open questions are discussed accordingly, which may provide new insights into addressing COVID-19 pandemic and would be helpful for researchers to explore more related topics in the post-pandemic era.

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An Interpretable Machine Learning Framework for Accurate Severe vs Non-Severe COVID-19 Clinical Type Classification

Cited by 20 publications

References 32 publications

Clinical prognosis evaluation of COVID-19 patients: An interpretable hybrid machine learning approach

Clinical prognosis evaluation of COVID-19 patients: An interpretable hybrid machine learning approach

Identification of high-risk COVID-19 patients using machine learning

Artificial intelligence for COVID-19: battling the pandemic with computational intelligence

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