Background
In the face of the current COVID-19 pandemic, the timely prediction of upcoming medical needs for infected individuals enables better and quicker care provision when necessary and management decisions within health care systems.
Objective
This work aims to predict the medical needs (hospitalizations, intensive care unit admissions, and respiratory assistance) and survivability of individuals testing positive for SARS-CoV-2 infection in Portugal.
Methods
A retrospective cohort of 38,545 infected individuals during 2020 was used. Predictions of medical needs were performed using state-of-the-art machine learning approaches at various stages of a patient’s cycle, namely, at testing (prehospitalization), at posthospitalization, and during postintensive care. A thorough optimization of state-of-the-art predictors was undertaken to assess the ability to anticipate medical needs and infection outcomes using demographic and comorbidity variables, as well as dates associated with symptom onset, testing, and hospitalization.
Results
For the target cohort, 75% of hospitalization needs could be identified at the time of testing for SARS-CoV-2 infection. Over 60% of respiratory needs could be identified at the time of hospitalization. Both predictions had >50% precision.
Conclusions
The conducted study pinpoints the relevance of the proposed predictive models as good candidates to support medical decisions in the Portuguese population, including both monitoring and in-hospital care decisions. A clinical decision support system is further provided to this end.
The current SARS-COV-2 epidemic is associated with nearly 1 million estimated deaths and responsible
for multiple disturbances around the world, including the overload of health care systems. The timely prediction of the medical needs of infected individuals enables a better and quicker care provision for the necessary cases, supporting the management of available resources.
This work ascertains the predictability of medical needs (as hospitalization, respiratory support, and admission to intensive care units) and the survivability of individuals testing SARS-CoV-2 positive considering a cohort with all infected individuals in Portugal as per June 30, 2020. Predictions are performed at the various stages of a patients cycle, namely: pre-hospitalization (testing time), pos-hospitalization, and pos-intensive care. A thorough optimization of state-of-the-art predictors is undertaken to assess the ability to anticipate medical needs and infection outcomes using demographic and comorbidity variables, as well as onset date of symptoms, test and hospitalization.
MotivationThe increasing prevalence of omics data sources is pushing the study of regulatory mechanisms underlying complex diseases such as cancer. However, the vast quantities of features produced and the inherent interplay between them lead to a level of complexity that hampers both descriptive and predictive tasks, requiring custom-built algorithms that can extract relevant information from these sources of data.ResultsWe propose a transformation that moves data centered on molecules (e.g. transcripts and proteins) to a new data space focused on putative regulatory modules given by statistically relevant patterns of coexpression. The proposed transformation extracts patterns from the data through biclustering and uses them to create new variables with guarantees of interpretability and discriminative power. The transformation is shown to achieve dimensionality reductions of up to 99% and to increase the predictive performance of various classifiers across multiple omics layers. Our results suggest that a transformation of omics data from gene-centric to pattern-centric data provides benefits to both prediction tasks and human interpretation. The proposed approach is expected to greatly support further bioinformatic analyses for precision medicine applications.AvailabilitySoftware code and the raw results generated are available atgithub.com/Andrempp/Pattern-Centric-Transformation.Contactandremppatricio@tecnico.ulisboa.ptSupplementary informationSupplementary data are available atJournal Nameonline.
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