This system is based on folksonomy, which is a concept based on the traditional taxonomy in which categories are defined in which the labels found in the document will later be classified. The main difference is that folksonomy makes a bottom-up classification of labels. That means that instead of determining the categories prior to the analysis, the analysis provides categories according to the frequency of appearance of the tags. Folksonomy is a little known concept, although we use it daily.
Folksonomy Text Analytics uses artificial intelligence techniques such as machine learning and Large Language Model (LLM), to analyse large amounts of unstructured natural language data (Big Data). As a result, it is capable of extracting valuable patterns and information from alerts, discharges and clinical histories in the clinical setting. The advantages of using machine learning and LLM are that they allow for greater precision in data analysis and a greater ability to identify patterns and trends that would be difficult to detect manually. Moreover, these models can learn and adapt as they are fed with more data, which means that their accuracy and ability to analyse data will continue to improve over time. With Bismart Folksonomy, the administration and clinical professionals can obtain valuable information and make informed decisions based on the analysed data.
Huge amounts of data are generated daily in the clinical field. These data include medical discharges and discharges and medical records, among many other types. Within these large amounts of data are patterns and information that can be of great value to management and professionals.
However, extracting this information manually is impossible, given the magnitude of the data and the fact that the discharges, discharges and histories are written in unstructured natural language.
The main features of Bismart Folksonomy are:
