Machine-Learning Approaches in COVID-19 Survival Analysis and Discharge-Time Likelihood Prediction Using Clinical Data

Nemati, Mohammadreza; Ansary, Jamal; Nemati, Nazafarin

doi:10.1016/j.patter.2020.100074

Cited by 114 publications

(97 citation statements)

References 31 publications

(28 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Logistic regression and separately LASSO regression were used to determine 10 variables that served as a significant predictor of a severe and critical COVID-19 infection. Nemati et al analyze survival characteristics of a group of 1,182 patients to test different variables of a patient and their overall survival to aid public health officials in their decisions regarding COVID-19 outbreaks (15). They discovered that gender and age were the two largest contributing factors to fatality, e.g., men had a higher fatality rate than women, agreeing with an original sample found in China early on (16).…”

Section: Related Workmentioning

confidence: 77%

Individual-Level Fatality Prediction of COVID-19 Patients Using AI Methods

Horowitz

Liu

et al. 2020

Front. Public Health

View full text Add to dashboard Cite

The global covid-19 pandemic puts great pressure on medical resources worldwide and leads healthcare professionals to question which individuals are in imminent need of care. With appropriate data of each patient, hospitals can heuristically predict whether or not a patient requires immediate care. We adopted a deep learning model to predict fatality of individuals tested positive given the patient's underlying health conditions, age, sex, and other factors. As the allocation of resources toward a vulnerable patient could mean the difference between life and death, a fatality prediction model serves as a valuable tool to healthcare workers in prioritizing resources and hospital space. The models adopted were evaluated and refined using the metrics of accuracy, specificity, and sensitivity. After data preprocessing and training, our model is able to predict whether a covid-19 confirmed patient is likely to be dead or not, given their information and disposition. The metrics between the different models are compared. Results indicate that the deep learning model outperforms other machine learning models to solve this rare event prediction problem.

show abstract

Section: Related Workmentioning

confidence: 77%

Individual-Level Fatality Prediction of COVID-19 Patients Using AI Methods

Horowitz

Liu

et al. 2020

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…Few studies have also reported that being old or male, the probability of hospital discharge is lower. 22,23 Cox proportional hazards model has been used to study the mortality and recovery of COVID-19 patients. 10,11 The bias in estimating the hazard ratio of death in the presence of competing events have been investigated even in the context of COVID-19.…”

Section: Discussionmentioning

confidence: 99%

Competing risk between in-hospital mortality and recovery: An application of DeepHit on COVID-19 clinical data

M.K.

Kamath

2020

Preprint

View full text Add to dashboard Cite

Unexplained pneumonia appeared in Wuhan was soon determined to be a novel coronavirus disease, referred to as COVID-19. On March 11, 2020, WHO characterized COVID-19 as a pandemic and the virus has been recognized as a global threat. A plethora of studies is being carried out using various statistical and mathematical models to predict the probable evolution of this pandemic. Though most of them are focusing on building predictive models to assess mortality rates and risk, concentrating on the length of hospital stay can improve decision making and treatment plans. While modeling the length of stay, possible outcomes observed are either discharge or mortality. Modeling the duration of recovery and death provides valuable information for health officials to design proper strategies to reduce the burden on the health system during the outbreak. In this study, we are exploring this competing event aspect of the survival data obtained from COVID-19 patients using the state-of-the-art model DeepHit, a discrete survival model.

show abstract

“…In order to evaluate the early risk assessment for patients, in [114] demographic data, physiological clinical variables and laboratory results from electronic healthcare records are extracted and used with applied multivariate logistic regression, random forest and extreme gradient boosted trees. In order to predict survival analysis and discharge time based on clinical data, some machine learning algorithms are used in [115] . The data include various features including gender, symptoms, chronic disease history and travel history.…”

Section: Clinical Applicationsmentioning

confidence: 99%

Applications of artificial intelligence in battling against covid-19: A literature review

2021

Chaos, Solitons & Fractals

156

View full text Add to dashboard Cite

show abstract

Machine-Learning Approaches in COVID-19 Survival Analysis and Discharge-Time Likelihood Prediction Using Clinical Data

Abstract: Highlights d 1,182 hospitalized patients were studied in this research d Survival analysis can be applied to predict patient length of stay in the hospital d We used seven machine-learning and statistical analysis techniques d The impact of clinical covariates on survival times was studied

Cited by 114 publications

References 31 publications

Individual-Level Fatality Prediction of COVID-19 Patients Using AI Methods

Individual-Level Fatality Prediction of COVID-19 Patients Using AI Methods

Competing risk between in-hospital mortality and recovery: An application of DeepHit on COVID-19 clinical data

Applications of artificial intelligence in battling against covid-19: A literature review

Contact Info

Product

Resources

About