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

Patterns, 2020-08, Vol.1 (5), p.100074-100074, Article 100074 [Peer Reviewed Journal]

2020 The Authors ;2020. Not withstanding the ProQuest Terms and Conditions, you may use this content in accordance with the associated terms available at https://www.elsevier.com/legal/elsevier-website-terms-and-conditions ;2020 The Authors 2020 ;ISSN: 2666-3899 ;EISSN: 2666-3899 ;DOI: 10.1016/j.patter.2020.100074 ;PMID: 32835314

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  • Title:
    Machine-Learning Approaches in COVID-19 Survival Analysis and Discharge-Time Likelihood Prediction Using Clinical Data
  • Author: Nemati, Mohammadreza ; Ansary, Jamal ; Nemati, Nazafarin
  • Subjects: artificial intelligence ; biostatistic ; coronavirus ; COVID-19 ; machine learning ; pandemic ; statistical analysis ; survival analysis
  • Is Part Of: Patterns, 2020-08, Vol.1 (5), p.100074-100074, Article 100074
  • Description: As a highly contagious respiratory disease, COVID-19 has yielded high mortality rates since its emergence in December 2019. As the number of COVID-19 cases soars in epicenters, health officials are warning about the possibility of the designated treatment centers being overwhelmed by coronavirus patients. In this study, several computational techniques are implemented to analyze the survival characteristics of 1,182 patients. The computational results agree with the outcome reported in early clinical reports released for a group of patients from China that confirmed a higher mortality rate in men compared with women and in older age groups. The discharge-time prediction of COVID-19 patients was also evaluated using different machine-learning and statistical analysis methods. The results indicate that the Gradient Boosting survival model outperforms other models for patient survival prediction in this study. This research study is aimed to help health officials make more educated decisions during the outbreak. [Display omitted] •1,182 hospitalized patients were studied in this research•Survival analysis can be applied to predict patient length of stay in the hospital•We used seven machine-learning and statistical analysis techniques•The impact of clinical covariates on survival times was studied A record-breaking pressure has been placed on healthcare systems by the COVID-19 pandemic. As a result of fast-growing requests for medical care in hospitals, with limited space and number of intensive care units, estimation of the length of stay of patients with COVID-19 in hospitals can provide insightful information to decision makers for efficient allocation of equipment and managing hospital overload in different countries. This work introduces statistical models and machine-learning-based approaches that can be directly applied to real-world COVID-19 data to predict the patient discharge time from hospital and evaluate how the patient clinical information could have an impact on the length of stay in hospital. While considerable insights have been achieved about the patient recovery times in this paper, applications of these data-driven approaches are expected to gather substantial interest in the near future once more detailed clinical data are available. COVID-19 has spread to many countries in a short period, and overwhelmed hospitals can be a direct consequence of rapidly increasing coronavirus cases. In this study, by choosing patient discharge time as the event of interest, survival analysis techniques including statistical analysis and machine-learning approaches are used to build predictive models capable of predicting patients’ period of stay in hospital. This time is crucial because it allows decision makers to be prepared for hospital overloads.
  • Publisher: Amsterdam: Elsevier Inc
  • Language: English
  • Identifier: ISSN: 2666-3899
    EISSN: 2666-3899
    DOI: 10.1016/j.patter.2020.100074
    PMID: 32835314
  • Source: Coronavirus Research Database
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