Artificial Intelligence To Fight The Spread Of Infectious Diseases

Epidemiology and infectious disease specialists work together to define diseases, which helps clinicians working on the ground to diagnose a specific infectious disease. This process is usually was quite long and labor-intensive, and requires a large number of cases. In the case of the infamous Zika virus, scientists made use of an incredibly interesting disease detection system, known as GUARDIAN, which is automated and operates in real time. From a practical point of view, as soon as the cases come to the Emergency Department, real-time analysis is performed on various aspects of electronic health records (chief complaints, vital parameters, etc.) and laboratory results. This means that a physician is informed and alerted of a case as soon as it is identified. The main benefit is avoiding delays and preventing further spread of contagious infectious diseases such as influenza, plague and anthrax. Even better, comparison with traditional systems using rigorous scientific protocols has shown it to be the most accurate prediction model.

Healthcare management and policy

Public Health is not just about dealing with infectious diseases outbreaks, but much more. One important aspect of public health, believe it or not, is argumentation. This is not the first time we have tussled with politicians to put forward our public health arguments and ensure the highest quality health for our population.

Cereal products, specifically bread, are used by decision makers as an instrument to fight diseases such as obesity or diabetes. Scientists have succeeded in creating models that output new recommendations based on stakeholders’ arguments by targeting specific audiences. Imagine a consensus that must be reached on risk/benefit evaluation between stakeholders with differing views, such as the massive food industry, government regulators and public health professionals. This workflow allows the consensus to be achieved in a much shorter time and with positive effects, in contrast to a lengthy and mostly bureaucratic process.

Policy has a crucial role in management, and specific policies often influence the way matters are handled within a healthcare system. A change in policy could bring forth various forms of feedback — negative, positive and constructive. What if there was a way to bring all of this together through existing social networks? Sentiment analysis, especially when done across different social networks, can offer insight into the experience of patients making use of various healthcare institutions and organizations.

Way back in 2010, a research team from the UK succeeded in analyzing approximately 6,412 free-text online comments using machine learning. This can be quite useful, in comparison to relying solely on surveys or tedious questionnaires, which take time to fill out and could be biased or not truly reflective of the situation, especially if done in a hurry. The research team developed their viewpoint further and explored patient experience and engagement through Twitter in the US, and has even proposed updates to the way we report on hospital quality.

The vast public health field is ripe with potential; there is so much more to cover and learn. We will be covering disease prevention, behavior change, evidence synthesis and more in Part 2 of this series.

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