A central problem in machine learning is to learn from data (``big''...
Five Papers Detail New Computational Models to Predict Severe Illness and Mortality 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, Pontifícia Universidade Católica do Rio de Janeiro, and Huazhong University of Science and Technology has resulted in five distinct papers published during 2020 in eLife, Engineering, PLOS One, JMIR Med. Informatics, and Int. J. Med. Informatics, 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 team has released easy to use calculators for calculating the risk for severe COVID-19 illness for patients who have tested positive, based on demographics, pre-existing conditions, and laboratory findings.
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.
Overview: This study examined records of 2,566 consecutive COVID-19 patients at five Massachusetts hospitals and sought to predict level-of-care requirements based on clinical and laboratory data. Several classification methods were applied and compared against standard pneumonia severity scores. The need for hospitalization, ICU care, and mechanical ventilation were predicted with a validation accuracy of 88%, 87%, and 86%, respectively. ICU care and ventilation. When predictions are limited to patients with more complex disease, the accuracy of the ICU and ventilation prediction models achieved accuracy of 83% and 82%, respectively. Vital signs, age, BMI, dyspnea, and comorbidities were the most important predictors of hospitalization. Opacities on chest imaging, age, admission vital signs and symptoms, male gender, admission laboratory results, and diabetes were the most important risk factors for ICU admission and mechanical ventilation. The factors identified collectively form a signature of the novel COVID-19 disease.
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.
Overview: This study used a nation-wide multicenter data of COVID-19 patients in Brazil to predict mortality and ventilator usage. A total of 113,214 patients with 50,387 deceased, were included. Death from COVID-19 was strongly associated with demographics, socioeconomic factors, and comorbidities. Classifying the medical risk of COVID-19 patients was shown to be relevant for low- and medium- income countries in order to assign limited medical resources more effectively, as well as to help design targeted physical-distancing and work accommodation policies that will assist in reducing economic loss during the current pandemic. The results highlight the predictive power of socioeconomic information in assessing COVID-19 mortality and medical resource allocation, and shed light on existing disparities in the Brazilian health care system during the COVID-19 pandemic. In the future, this model could help prioritize vaccine distribution to the more risk-vulnerable and to those who need to interact with them.
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.
Taiyao Wang, Aris Paschalidis, Quanying Liu, Yingxia Liu, Ye Yuan, and Ioannis Ch. Paschalidis (Boston University, Brown University, University of Science and Technology, Shenzen, Huazhong University of Science and Technology, Wuhan), “Predictive Models of Mortality for Hospitalized COVID-19 Patients: Retrospective Cohort Study,” JMIR Medical Informatics, Vol. 8, No. 10, e21788, 2020, https://doi.org/10.2196/21788.
Overview: This study developed accurate models to predict the mortality of hospitalized patients with COVID-19 using basic demographics and easily obtainable laboratory data, analyzing data for 375 hospitalized patients with COVID-19 in Wuhan, China. Two types of models were developed: the first type used laboratory findings from the entire length of the patient’s hospital stay, and the second type used laboratory findings that were obtained no later than 12 hours after admission. The models were further validated on a multicenter external cohort of 542 patients. The models achieved high accuracies and could predict outcomes more than one week in advance; suggesting that they can be highly useful for resource allocation in hospitals.
YeYuan, Chuan Sun, Xiuchuan Tang, Cheng Cheng, Laurent Mombaerts, Maolin Wang, Tao Hue, Chenyu Sun, Yuqi Guo, Xiuting Li, Hui Xu, Tongxin Ren, Yang Xiao, Yaru Xiao, Hongling Zhu, Honghan Wu, Kezhi Li, Chuming Chen, Yingxia Liu, Zhichao Liang, Zhiguo Cao, Hai-Tao Zhang, Ioannis Ch. Paschalidis, Quanying Liu, Jorge Goncalves, Qiang Zhong, and Li Yan (Huazhong University of Science and Technology, Wuhan, University of Luxembourg, University of Cambridge, AMITA Health Saint Joseph Hospital Chicago, University College London, Southern University of Science and Technology, Shenzhen, Boston University), “Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients: A Multi-Center Retrospective Study in China”, Engineering, 2020, https://doi.org/10.1016/j.eng.2020.10.013.
Overview: This study developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital, Wuhan, China (development cohort) and externally validated with data from two other centers: 141 inpatients from Jinyintan Hospital, Wuhan, China (validation cohort 1) and 432 inpatients from The Third People’s Hospital of Shenzhen, Shenzhen, China (validation cohort 2). The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of death. The risk score can predict the mortality of individual patients more than 12 days in advance with more than 90% accuracy across all cohorts.