COVID-19 outcome prediction by integrating clinical and metabolic data using machine learning algorithms

Villagrana-Bañuelos, Karen E.; Maeda-Gutiérrez, Valeria; Alcalá-Rmz, Vanessa; Oropeza-Valdez, Juan J; Oostdam, Ana Sofía Herrera-Van; Castañeda-Delgado, Julio Enrique; López, Jesús Adrián; Moreno, Juan C Borrego; Galván-Tejada, Carlos E.; Galván-Tejeda, Jorge I; Gamboa-Rosales, Hamurabi; Luna-García, Huizilopoztli; Celaya-Padilla, José M.; López‐Hernández, Yamilé

doi:10.24875/ric.22000182

Cited by 3 publications

(3 citation statements)

References 29 publications

(31 reference statements)

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“…Most of these studies were performed in developed countries, and the considered indicators generally included comorbidities, demographic factors, laboratory data and symptoms. Some models also predicted the severity or mortality by considering the genetic indicators or metabolomics (38,(45)(46)(47)(48). Image analysis approaches based on deep learning algorithms were also utilized in the field diagnosis and prognosis of COVID-19 patients using CT and radiographic images (11)(12)(13)(14)(15)(16).…”

Section: Discussionmentioning

confidence: 99%

Unraveling complex relationships between COVID-19 risk factors using machine learning based models for predicting mortality of hospitalized patients and identification of high-risk group: a large retrospective study

Banoei,

Rafiepoor,

Zendehdel

et al. 2023

Front. Med.

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BackgroundAt the end of 2019, the coronavirus disease 2019 (COVID-19) pandemic increased the hospital burden of COVID-19 caused by the SARS-Cov-2 and became the most significant health challenge for nations worldwide. The severity and high mortality of COVID-19 have been correlated with various demographic characteristics and clinical manifestations. Prediction of mortality rate, identification of risk factors, and classification of patients played a crucial role in managing COVID-19 patients. Our purpose was to develop machine learning (ML)-based models for the prediction of mortality and severity among patients with COVID-19. Identifying the most important predictors and unraveling their relationships by classification of patients to the low-, moderate- and high-risk groups might guide prioritizing treatment decisions and a better understanding of interactions between factors. A detailed evaluation of patient data is believed to be important since COVID-19 resurgence is underway in many countries.ResultsThe findings of this study revealed that the ML-based statistically inspired modification of the partial least square (SIMPLS) method could predict the in-hospital mortality among COVID-19 patients. The prediction model was developed using 19 predictors including clinical variables, comorbidities, and blood markers with moderate predictability (Q2 = 0.24) to separate survivors and non-survivors. Oxygen saturation level, loss of consciousness, and chronic kidney disease (CKD) were the top mortality predictors. Correlation analysis showed different correlation patterns among predictors for each non-survivor and survivor cohort separately. The main prediction model was verified using other ML-based analyses with a high area under the curve (AUC) (0.81−0.93) and specificity (0.94−0.99). The obtained data revealed that the mortality prediction model can be different for males and females with diverse predictors. Patients were classified into four clusters of mortality risk and identified the patients at the highest risk of mortality, which accentuated the most significant predictors correlating with mortality.ConclusionAn ML model for predicting mortality among hospitalized COVID-19 patients was developed considering the interactions between factors that may reduce the complexity of clinical decision-making processes. The most predictive factors related to patient mortality were identified by assessing and classifying patients into different groups based on their sex and mortality risk (low-, moderate-, and high-risk groups).

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Section: Discussionmentioning

confidence: 99%

Unraveling complex relationships between COVID-19 risk factors using machine learning based models for predicting mortality of hospitalized patients and identification of high-risk group: a large retrospective study

Banoei,

Rafiepoor,

Zendehdel

et al. 2023

Front. Med.

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“… 21 To support this premise, the following search string “ TS = (COVID-19 and Random Forest (RF)) and English (Languages) ” was used to extract related work from the Web of Science collections, focusing on the RF applications in addressing COVID-19. Only 3 of 8 publications 22 – 24 met our criteria. We also queried ScienceDirect using the following search string: “ TS = (COVID-19 and Random Forest) and TS = (prediction or forecast) and TS = (regression) ,” focusing on English as a language, and retrieved 1 of 33 publications (see Table 1 and Table 4 in the Appendices ).…”

Section: Related Literaturementioning

confidence: 99%

Informed Random Forest to Model Associations of Epidemiological Priors, Government Policies, and Public Mobility

Thapelo,

Mpoeleng,

Hillhouse

2023

MDM Policy & Practice

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Background. Infectious diseases constitute a significant concern worldwide due to their increasing prevalence, associated health risks, and the socioeconomic costs. Machine learning (ML) models and epidemic models formulated using deterministic differential equations are the most dominant tools for analyzing and modeling the transmission of infectious diseases. However, ML models can be inconsistent in extracting the dynamics of a disease in the presence of data drifts. Likewise, the capability of epidemic models is constrained to parameter dimensions and estimation. We aimed at creating a framework of informed ML that integrates a random forest (RF) with an adapted susceptible infectious recovered (SIR) model to account for accuracy and consistency in stochasticity within the dynamics of coronavirus disease 2019 (COVID-19). Methods. An adapted SIR model was used to inform a default RF on predicting new COVID-19 cases (NCCs) at given intervals. We validated the performance of the informed RF (IRF) using real data. We used Botswana’s pharmaceutical interventions (PIs) and non-PIs (NPIs) adopted between February 2020 and August 2022. The discrepancy between predictions and observations is modeled using loss functions, which are minimized, interpreted, and used to assess the IRF. Results. The findings on the real data have revealed the effectiveness of the default RF in modeling and predicting NCCs. The use of the effective reproductive rate to inform the RF yielded an excellent predictive power (84%) compared with 75% by the default RF. Conclusion. This research has potential to inform policy and decision makers in developing systems to evaluate interventions for infectious diseases. Highlights This framework is initiated by incorporating model outputs from an epidemic model to a machine learning model. An informed random forest (RF) is instantiated to model government and public responses to the COVID-19 pandemic. This framework does not require data transformations, and the epidemic model is shown to boost the RF’s performance. This is a baseline knowledge-informed learning framework for assessing public health interventions in Botswana.

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“…However, a shift occurred between 2010 and 2014, with computer-aided disease detection and prediction taking the lead [79][80][81][82]. Recently, there has been a surge in drug development and disease-drug response studies [83,84], with COVID-19-related research dominating the most recent publications [85].…”

Section: Planningmentioning

confidence: 99%

Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study

Shi,

Bendig,

Vollmar

et al. 2023

J Med Internet Res

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Background Artificial intelligence (AI), conceived in the 1950s, has permeated numerous industries, intensifying in tandem with advancements in computing power. Despite the widespread adoption of AI, its integration into medicine trails other sectors. However, medical AI research has experienced substantial growth, attracting considerable attention from researchers and practitioners. Objective In the absence of an existing framework, this study aims to outline the current landscape of medical AI research and provide insights into its future developments by examining all AI-related studies within PubMed over the past 2 decades. We also propose potential data acquisition and analysis methods, developed using Python (version 3.11) and to be executed in Spyder IDE (version 5.4.3), for future analogous research. Methods Our dual-pronged approach involved (1) retrieving publication metadata related to AI from PubMed (spanning 2000-2022) via Python, including titles, abstracts, authors, journals, country, and publishing years, followed by keyword frequency analysis and (2) classifying relevant topics using latent Dirichlet allocation, an unsupervised machine learning approach, and defining the research scope of AI in medicine. In the absence of a universal medical AI taxonomy, we used an AI dictionary based on the European Commission Joint Research Centre AI Watch report, which emphasizes 8 domains: reasoning, planning, learning, perception, communication, integration and interaction, service, and AI ethics and philosophy. Results From 2000 to 2022, a comprehensive analysis of 307,701 AI-related publications from PubMed highlighted a 36-fold increase. The United States emerged as a clear frontrunner, producing 68,502 of these articles. Despite its substantial contribution in terms of volume, China lagged in terms of citation impact. Diving into specific AI domains, as the Joint Research Centre AI Watch report categorized, the learning domain emerged dominant. Our classification analysis meticulously traced the nuanced research trajectories across each domain, revealing the multifaceted and evolving nature of AI’s application in the realm of medicine. Conclusions The research topics have evolved as the volume of AI studies increases annually. Machine learning remains central to medical AI research, with deep learning expected to maintain its fundamental role. Empowered by predictive algorithms, pattern recognition, and imaging analysis capabilities, the future of AI research in medicine is anticipated to concentrate on medical diagnosis, robotic intervention, and disease management. Our topic modeling outcomes provide a clear insight into the focus of AI research in medicine over the past decades and lay the groundwork for predicting future directions. The domains that have attracted considerable research attention, primarily the learning domain, will continue to shape the trajectory of AI in medicine. Given the observed growing interest, the domain of AI ethics and philosophy also stands out as a prospective area of increased focus.

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COVID-19 outcome prediction by integrating clinical and metabolic data using machine learning algorithms

Cited by 3 publications

References 29 publications

Unraveling complex relationships between COVID-19 risk factors using machine learning based models for predicting mortality of hospitalized patients and identification of high-risk group: a large retrospective study

Unraveling complex relationships between COVID-19 risk factors using machine learning based models for predicting mortality of hospitalized patients and identification of high-risk group: a large retrospective study

Informed Random Forest to Model Associations of Epidemiological Priors, Government Policies, and Public Mobility

Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study

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