Exploring Machine Learning for Health Literacy
This project seeks to explore and investigate the use of machine learning to improve health literacy. Health literacy is concerned about people’s ability to meet the intricate needs of health in the contemporary world. It involves putting their own health, as well as the health of their family and community, in perspective, recognizing the variables affecting their health, and knowing how to deal with them (Sørensen et al., 2012).
Making poor health decisions, not understanding medical instructions, and skipping appointments are just a few of the serious effects of low health literacy (Shahid et al., 2022). It can make people less able to manage their health and lead to health inequities. Creating health information that is easier to understand and more available, offering guidance and assistance to consumers, and encouraging patient-provider communication
are all common components of initiatives to increase health literacy (Fitzpatrick, 2023).
Machine learning, a branch of artificial intelligence (AI) , focuses on creating models and algorithms that allow computers to learn from data and make decisions without explicit programming (Kufel et al., 2023). In the context of health literacy, machine learning has the potential to significantly contribute to better healthcare outcomes by enhancing people’s comprehension of health-related information. There are many ways that machine learning might advance health literacy, for example, Chatbots and virtual assistants with machine learning capabilities can respond to questions about health instantly and present facts in a conversational and understandable way. These tools can help people find the right services, clear up questions, and navigate complex health information (Liu and Xiao, 2021). In addition, a computer vision technique called image segmentation divides an image into several useful segments or regions. Image segmentation aims to simplify the way an image is represented and
improve its comprehension for subsequent analysis. Usually, each segment in a picture relates to a certain object or area (Minaee et al., 2021). To make complicated health concepts easier to understand, segmented images can be included in brochures, instructional materials, or web resources. People with varied degrees of health literacy can find this especially helpful as it can aid in their understanding of how health relates to their environment.