The present pandemic is one of the most severe medical emergencies that we have faced in decades. The Covid-19 pandemic has exposed the vulnerability of our healthcare system. Data scientists around the world are trying their level best to develop solutions for fighting the covid-19 pandemic. In this regard, machine learning has helped data scientists in several ways like restricting the spread of the pandemic. There are several means by which machine learning can help us in this critical medical emergency. Let us take a look.

Machine learning at our disposal

Machine learning can help us in identifying the segments of our population that are at high risk of getting infected with the virus. It can also help us in the analysis of diagnostic reports of the infected patient. As per research, machine learning is pivotal for drug development and discovery at a faster pace. In the present state of affairs where medical staff is overburdened, it is machine learning that can help in medical diagnosis based on symptomatic input. The forecasting analysis of machine learning can also help us estimate the spread of the disease. In various types of glycoscience experiments, we have been able to understand the genetic structure of the coronavirus in a better way. This can not only help in the treatment of severely infected coronavirus patients but also in developing chemicals that can kill the virus in the first instance.

Identification of risk factors and vulnerabilities

With the help of machine learning techniques, we can detect risk factors based on three main parameters. The first parameter involves the detection of covid-19 infection in certain sections of a population. This population may inhabit small clusters and live in congested areas. The second parameter involves the detection of infection cases on the basis of age. This parameter helps in determining whether the young population can fight the infection better in comparison to the older population. This parameter has been used to prioritize the vaccination drives on the basis of age. The third factor that can be used for determining infection cases is related to the underlying ailments. This helps us in taking specific measures to safeguard a section of the population that is at high risk due to previous ailments.

Peripheral factors

It needs to be noted at this point in time that machine learning can even make the use of peripheral factors in determining the spread of the infection. For instance, the type of hygiene habits can be a critical factor in determining the risk of infection. Other peripheral factors that can be used for training a machine learning model include social habits. If we are able to quantify social habits in the form of social interactions, we can come up with reliable data sets. This may include the number of people that a patient has met in the last 14 days. In addition to this, geographical factors can also be used as inputs to train a machine learning model.

Diagnostic techniques

As the covid-19 virus continues to spread across various locations, the number of people affected by it continue to increase. In this case, the hospitals are often flooded with new patients and diagnosis becomes a challenging task. Machine learning courses can provide a preliminary idea of the diagnosis of the disease. This can later help in devising models for the analysis of CT scans as well as other laboratory tests. In addition to this, machine learning can help us to commercialize several wearable devices that can keep a track of the oxygen saturation of an individual. Machine learning techniques are also very helpful in natural language processing. The natural language processing capabilities of chatbots can be utilized to detect cases of infection on the basis of reported symptoms through an online platform.

The road ahead

Machine learning can help us accentuating the process of drug development. It can also help in the faster collection and processing of data related to vaccine trials. All this can be done without compromising quality control.

Researchers around the world are now working to construct different types of biomedical knowledge graphs. With the help of the biomedical knowledge graphs, we can understand the structure of pathogens at a micro-level. In the future, this can help in drug discovery and digital health monitoring. Needless to mention, we would be able to monitor the performance of a drug within the human body with the help of scientific devices conceived through machine learning techniques. Hence, the time is ripe to take the research in medical sciences to a new level with the help of machine learning techniques.