Syndrome Anomaly Detection Chapter 1 - Motive
The world came to a screaming halt due to the pandemic crisis we are navigating. While, countries are learning how to adapt to the ever-changing situation to put stop the spread of COVID-19, we are looking one step ahead to prevent future pandemics from occurring. The most critical component to prevent a pandemic from occurring is early intervention. If a new disease is not identified early then the chances of containment are severely hindered. We believe that the key to the prevention of future pandemics is to improve healthcare monitoring.
Our vision to achieve we mean by superior healthcare monitoring does not involve one doctor speaking to another doctor. Rather, we envision artificial intelligence to be the interface and safeguard to prevent future disease outbreaks.
Every day doctors and healthcare professionals assess patients, to ultimately treat and diagnosis their patient’s ailments. To complicate this interaction patients can present with atypical symptoms. During the healthcare professional-patient interaction it is challenging enough for the healthcare provider to treat their patient, let alone monitor and report atypical symptoms to public health agencies. It is due to this unmet challenge that A.I. fits into the disease outbreak prevention equation. Over the next series of posts, we will walk you through our journey to create a deep learning solution to autonomously monitor for syndrome anomalies.