Diabetes research is an ongoing area in the medical industry that plays a significant role in how effectively patients can manage their condition. There are various ways in which scientists and medical professionals conduct research to improve diabetes management. In recent years, the use of natural language processing in diabetes research has become a popular topic. We take a closer look at what natural language processing is and how it can fit into diabetes research efforts.
What is Natural Language Processing?
Most people are familiar with or have at least heard the term artificial intelligence in the modern day. It is a technology that we should no longer look past but rather embrace. Natural language processing is a specific branch of artificial intelligence technology. These technologies can be used to help computers better understand the natural language spoken by humans.
By feeding the technology data in a human language, the AI can not only help the computer understand it but also enable the interpretation of the information. Additionally, the AI can manipulate the data as well. There are different ways in which natural language processing can be used, and it has numerous applications in the medical industry.
How Natural Language Processing Can Be Used In Diabetes Research
As technologies behind natural language processing improve, researchers are finding ways to use these programs and AI solutions to assist with diabetes research and care. In this section, we turn our focus to potential uses of natural language processing in areas related to diabetes.
When data is submitted to a natural language processing model, it can help to filter through all the information and extract what is most important. This can be useful in several different cases, such as the following:
- Data can be used to identify patients who might have a high risk of diabetes. With relevant information available to an NLP model, it’s possible to identify individuals with certain symptoms or consider lifestyle habits that are increasing their risk of the disease. This can make a big difference when it comes to the early detection of diabetes, resulting in improved outcomes.
- It is also possible to use an NLP model to assess data from multiple scientific studies. This can help researchers identify patterns, such as trends in behavioral factors, that increase the risk of diabetes and its associated complications.
- With the correct data made available to the NLP model, researchers can also use the AI technology to evaluate how effective specific diabetes treatment options are. This can be very helpful when new treatments are tested. It’s a strategy that can help determine whether the treatments are superior to other options, as well as monitor side-effect reports. This can also contribute to better care quality.
In one report, it was also noted that a language model effectively predicted hypoglycemia using EHRs (electronic health records).
Apart from focusing on helping to identify patients at risk of diabetes, as well as considering how effective specific treatments are, other methods for utilizing AI and natural language processing also exist.
Researchers can use an NLP model to help them identify specific topics that are trending in the field of diabetes. This can assist in developing future research projects that focus on factors that both experts and patients are currently talking about. Additionally, it’s possible to gain a deeper understanding of how diabetes management and research have evolved by examining data from a specific period.
For example, the World Health Organization has reported that between 1980 and 2014, the prevalence of diabetes went from 108 million to 422 million. This means the incidence of the disease has increased by more than 300%. This data is valuable, as now researchers can search for clinical trials, medical publications, expert notes, survey data, and other information that can be assessed through an NLP model. The result would be a comprehensive assessment of how diabetes research into care and treatment strategies has evolved.
Important Considerations When Using Natural Language Processing
Many opportunities come to mind when looking at the use of natural language processing in diabetes research insights. With this said, researchers who consider using these models in their research projects need to keep a few things in mind. NLP models in the field of diabetes research are still a relatively new subject. Technology still has a long way to go, but it is advancing at a rapid rate.
Access to the right computational resources is currently an issue that many researchers may face. As the algorithms used in NLP models improve, they also require access to the right hardware. This type of hardware can be costly, which means scientists and researchers working on a tight budget might not have access to the right resources to execute these natural language processing models.
The considerations or challenges that researchers can face when they want to use NLP models to help with diabetes research do not only extend to resources or the fact that the technology is still new. It’s also important to take into account the data that they have access to.
When there is not enough data available or the data has poor quality, these factors can also significantly impact the efficacy of NLP models. Researchers need access to the right data to fully utilize the benefits that can come with the use of natural language processing technology.
Insufficient experience in using the technology can also be an obstacle. This would mean the researchers either need to complete a course on the use of NLP models in medical research. Alternatively, they will need to hire a technician who is already experienced in the field, but this can lead to a significantly higher overall cost for the project.
Conclusion
By analyzing research notes, online posts, and even survey data, natural language processing can be used to identify the risk for diabetes and the complications that the disease causes. Additionally, this type of technology can also be helpful when it comes to identifying gaps in treatment and evaluating how effective a diabetes management program is.
