Machines can learn from experience, adapt to new inputs, and perform human-like tasks thanks to artificial intelligence (AI). Most AI examples you hear about today rely heavily on deep learning and natural language processing, from chess-playing computers to self-driving cars.
In many countries, drug overdose is one of the leading causes of death. Researchers at the University of California discovered that by automating overdose death data with an artificial intelligence (AI) tool, public health officials can provide a timely response to reduce overdose deaths.
An automated process based on computer algorithms that can read text from medical examiners' death certificates can significantly speed up data collection of overdose deaths, ensuring a faster public health response time than the current system, according to new UCLA research.
Overdose data collection currently entails several steps, beginning with medical examiners and coroners, who determine a cause of death and record suspected drug overdoses on death certificates, including the drugs that caused the death. The certificates are then forwarded to local authorities or the Centers for Disease Control and Prevention (CDC) for coding.
Because it must be done manually, this coding process takes time. As a result, there is a significant time lag between the date of death and the reporting of those deaths, slowing the release of surveillance data. As a result, the public health response is slowed.
Using proxy data sources such as public health information and law enforcement data, researchers created a machine learning model capable of estimating national weekly opioid overdose mortality trends in near real-time.
These tools can address issues with delayed overdose data, allowing health departments to respond to overdose spikes more effectively.
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