Deaths and COVID AI

Artificial Intelligence applied to COVID, i.e., COVID AI, is key to fighting the deadly virus. Over the past ten months, the virus has taken more lives than H.I.V., malaria, influenza, and cholera over the same period. And the COVID-19 pandemic continues to propagate around the globe. 

Fighting deaths: entering Clinical Trials

Since outbreaks started at the end of 2019, the coronavirus has ripped through country after country. According to Johns Hopkins University data, it has been sickening more than 34 million people worldwide and caused over 1.13 million mortality. A severe toll gathered from official counts, yet one that notably underestimates how many have really perished. COVID-19 is the underlying cause of deaths attributed to other diseases. 

The high death rate reported arrives ten months after the first cases in Wuhan, China. Coronavirus may already have beaten tuberculosis and hepatitis as the world’s deadliest infectious disease in the general population. And the mortality statistics are growing fast. Still, it is far behind the overall deaths attributed to H.I.V. with 32 million since its first occurrences.

In comparison, it is nowhere near the lethal pandemic of the 1918 Spanish flu that claimed 50 million lives across countries. The coronavirus’s survival rate and mortality toll make it unprecedented and deadly among modern epidemics. In contrast, the 2009 H1N1 flu demanded 18,500 lives. The threat of these infectious diseases forces countries to focus on control and prevention.

A few countries such as China, Germany, South Korea, and New Zealand, have slowed the pandemic enough to limit infections and deaths while still reopening businesses and schools. It requires a combination of well-coordinated government actions that may be difficult to achieve in some countries: wide-scale testing, contact tracing, quarantining, social distancing, mask-wearing, developing a clear and consistent strategy, and being ready to lock things down in a rush when difficulties occur. 

But the countries which have managed to handle this crisis are scarce. Many look with hope at a potential vaccine to reduce the mortality rate attributed to COVID-19. Yet, vaccine trials enter their final phases toward the end of 2020. And there is no such thing as a 100% successful vaccine.

Furthermore, not all citizens will get vaccinated even though it might cause self-harm. Therefore, even a successful vaccine may not replace the disease control and prevention methods. Besides, it remains unclear how the virus mutates; hence it is impossible to predict how long a possible vaccine might work. 

COVID AI: what makes people more prone to death?

As a data scientist team, we have tried to get some insights from data regarding what caused people to die from COVID-19. The data we have chosen is a Mexican database. It is made publicly available by the Mexican government. 

It is cited in various research studies and papers. One of these studies is an article by the medical researcher Omar Yaxmehen Bello-Chavolla and his team. 

The records in this database are inputs from patients checking in Mexican hospitals with various conditions: cardiovascular diseases, respiratory diseases, kidney diseases, influenza, and pneumonia. It includes the gender, the age-groups, the comorbidities. We have already analyzed this database from the perspective of COVID-19 infections, entry in I.C.U. and intubation.

We have run our Machine Learning tool TADA over this database to understand the causes of morbidity and mortality. The question we asked the tool was, “given a COVID-19 patient, what risk factors are likely to become causes of death?”. TADA created numerous machine learning models and extracted the most impacting elements from them. Globally, the rate of death among COVID-19 patients is less than 5%. 

The average accuracy of the resulting COVID AI models was 86%, with an 87% sensitivity. It means that the model reasonably predicts the five percent of death. The false negatives, i.e., people expected to live who died, represented one percent. These figures account for excellent models. 

The main criteria used by the ai models to predict the death of a COVID-19 patient were:

  • whether the patient was intubated,
  • had pneumonia,
  • belonged to an age-group over 65,
  • and was a contact case.

Pneumonia was an overwhelming criterion and, consequently, considered one of the leading causes of death. We decided to explore what the COVID AI model had to say when pneumonia was discarded. So we reran TADA without pneumonia, the resulting models showed an accuracy of 80% and a similar sensitivity. 

The impacting criteria stayed identical, except for pneumonia, of course: intubation, age, contact case. However, a new criterion appeared in this new study: obesity. Chronic obstructive pulmonary disease (COPD) does not appear as a leading cause of death in this study, nor do other pulmonary diseases, diabetes, or coronary diseases.

The Fight Against COVID-19 Pandemic with Artificial Intelligence

This article is the last of a series of four about COVID-19 and Machine Learning. We have shown that it is possible to propose strong hypotheses for further investigation by medical research with Machine Learning models. Age appears to be a significant aggravating criterion for all the stages we have analyzed: contamination, entry in emergency care, intubation, and death. 

For contamination and entry in I.C.U., diabetes was a criterion which surprised us. In this last article, we were astonished to see that obesity was a leading cause of death in COVID-19 patients.

We do not claim to invent COVID treatment for COVID-19 patients. We claim to fight COVID-19 by helping medical researchers be quicker with the data they gather. 


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