Machine learning, a subset of AI proves beneficial in detecting drug abuse. How?

According to the Commonwealth Fund report, drug overdose deaths witnessed a surge in the US during the pandemic. The report reveals that the monthly deaths grew by 50% between February and May.

Technology has disrupted almost everything around us. New cutting-edge techs like AI are being adopted across all industries to enhance growth and agility. But can these technologies do anything to help us understand the issue of rising drug abuse and aid in fighting it? Yes, indicates many pieces of research and studies conducted on how AI functions like machine learning and deep learning can identify substance abuse risk.

The United States has been going through an opioid crisis, which has spiked the cases of drug abuse in the country. There have been discussions on policies and expanding the mental health infrastructure.

Substance abuse is a pressing issue and can affect multiple facets of an individual's life.

The study conducted by researchers from the University of South Carolina, Mahidol University, BITS Pilani, and Arizona and Ohio State Universities analyzed posts on social media sites like Twitter and Reddit,  related to substance abuse along with tracking the sale of opioids through cryptomarket listings. They collected data from the dark web and cryptomarkets about various opioid substances and compared it with social media posts using sentiment analysis. Assessing social media data to understand hashtags and posts to understand the feelings they expressed and correlating it with the data obtained on drug usage. Isn't it an interesting criterion?

According to the study, they used Named Entity Recognition (NER), a previously trained deep learning system to gather data from crypto markets including names, product weight, and other specifications. This data was used to automatically categorize them based on an existing database. They revealed that they leveraged deep learning algorithms BERT, LTSM, and CNN to assign emotion labels to each SubReddit post. The results revealed that there were many posts that talked about Pyrovalerone, a psychoactive drug usually used in the treatment of chronic fatigue, and another drug called Methaqualone, a sedative, and hypnotic medication. They also found various slang used for the commonly used drugs.

An earlier study published in 2018 used data from the popular social media, Instagram, built a machine learning algorithm and used deep learning to identify substance use risk. The data extracted through deep neural networks were classified using deep learning text and image analysis, then labeled as a high or low-risk category by the machine learning model.

These are not the only studies conducted in this area. There are others like the one mentioned in Sciencedaily, which talks about an AI algorithm developed by researchers from the College of Information Sciences and Technology at Penn State. The model created by them could predict susceptibility to substance use disorder among the young homeless individuals of the US and even suggest a personalized rehabilitation program.

Deep learning is gaining popularity since it exhibits high accuracy rates and highly contributes to the development of machine intelligence. Deep learning image classification abilities along with clustering and recognition enable the technology to support well the studies that use visuals of any kind. Using artificial neural networks and deep learning to detect drug abuse risk will aid the nations in understanding the issue better and take the required actions. The commendable data mining qualities, high accuracy, increased speed, augment human behaviour, ability to analyse sentiments, and automation make machine learning and deep learning the best choice to deal with the problem of substance abuse. The support of technologies like AI is not just limited to identifying the risk but also suggesting treatment methods, assisting individuals during treatment, and also tracking the treatment process. Artificial intelligence future is bright is and the new avenues will reveal possibilities of more accurate and intelligent models to combat and understand drug abuse.