Three Papers Detail New Computational Models to Predict Severe Illness from COVID-19
Data Science and AI Methods Offer Predictive Systems to Aid Healthcare Policy Management and Level-of-Care Requirements
Data science and AI methods have evolved to become a powerful tool in developing strong predictive models to help improve our understanding and treatment of COVID-19. “Given ample data, and assuming that the future is not completely random (like flipping a coin), we can learn what are the specific factors influencing future outcomes and predict them with high confidence,” says CISE Director, Yannis Paschalidis, (ECE, SE, BME, CDS) .
Prof. Paschalidis brings his expertise in data science to develop models with the ability to predict the course of disease for patients who have contracted COVID-19. His work with collaborators at Boston University, Massachusetts General Hospital, Harvard Medical School, and Pontifícia Universidade Católica do Rio de Janeiro has resulted in three distinct papers published this month in PLOS One, eLife, and IJMI, and discoveries in the physiological and socioeconomic divide of COVID (specifically in Latin America), as well as more specific ways to predict level-of-care requirements for hospitalized COVID-19 patients in Massachusetts.
“What we found is that the course of the disease for COVID-19 patients is highly predictable,” says Paschalidis. “We now know who is more likely to be hospitalized based on age, pre-existing conditions, and basic vitals. We also know who is more likely to develop severe disease and require ICU-level care and support of their breathing through mechanical ventilation, based on their initial clinical profile upon hospital admission. COVID-19 is absolutely not your typical pneumonia or flu. It has a very distinct clinical profile.”
The research also revealed that in developing countries, such as Mexico and Brazil, the socioeconomic status of the patient is an important predictor of COVID-19 outcomes, revealing existing disparities. “This is consistent with a picture that is emerging in the US as well,” says Prof. Paschalidis.
Another aspect of the research was the ability to use Artificial Intelligence to facilitate the process of processing the data to extract what is needed in training these predictive models. “In our work with data from five Massachusetts-based hospitals, we went from doctor reports all the way to predictive models using Natural Language Processing and machine learning,” adds Prof. Paschalidis. “Painstaking manual efforts to carefully compile data sets are becoming a thing of the past, drastically speeding up the process of developing models from raw data.”
Learn more about the papers here:
Boran Hao, Shahabeddin Sotudian, Taiyao Wang, Tingting Xu, Yang Hu, Apostolos Gaitanidis, Kerry Breen, George C Velmahos, Ioannis Ch Paschalidis. (Boston University, Massachusetts General Hospital, Harvard Medical School) “Early prediction of level-of-care requirements in patients with COVID-19.” eLife. Oct. 12, 2020. doi: 10.7554/eLife.60519
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Salomón Wollenstein-Betech, Amanda A. B. Silva, Julia L. Fleck, Christos G. Cassandras, Ioannis Ch. Paschalidis. (Boston University, Pontifícia Universidade Católica do Rio de Janeiro) “Physiological and socioeconomic characteristics predict COVID-19 mortality and resource utilization in Brazil.” PLOS ONE. Oct. 14, 2020. https://doi.org/10.1371/journal.pone.0240346
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Salomón Wollenstein-Betech, Christos G. Cassandras, Ioannis Ch. Paschalidis (Boston University) “Personalized Predictive Models for Symptomatic COVID-19 Patients Using Basic Preconditions: Hospitalizations, Mortality, and the Need for an ICU or Ventilator.” International Journal of Medical Informatics. Volume 142, October 2020, 104258. https://doi.org/10.1016/j.ijmedinf.2020.104258
Overview: This study developed personalized models that predict an individual’s likelihood of hospitalization, mortality, need for ICU, and need for ventilation with a sample of 91,000 COVID-19 cases from records made public by the Mexican government. After analyzing the information, the researchers built models that consider pre-existing conditions and how the disease could progress. They discovered that key factors affecting the need for hospitalization and ICU included age, pregnancy, diabetes, gender, chronic renal insufficiency, and immunosuppression.