NLP data mining Sample Clauses

NLP data mining. Text data mining or natural language processing is the process of extracting high- quality meaningful information from the available text in different document format files. This process involves the interface between computer languages and human (nature) languages in order to produce meaningful knowledge to a certain required query (22,23). By utilizing NLP (with neural network), we can implement relationship extraction and sentiment analysis to match patients and clinical trials through extracting phrases from patient’s medical records (patient side) and inclusion and exclusion criteria (investigator side) and create a matching value between both data sources. NLP works by two main components, Natural Language understanding (NLU) and National Language Generation (NLG). NLU is the process of providing a meaningful value (output) of the natural (human) language that the algorithm received. This happens by breaking the available texts into small units, then compare these units to pre-trained units in the memory in order to learn what these small units mean and what is the relationships between these units and each other. The second step, the NLG process, involves the translation of the output of NLU into another natural (human) text that is meaningful to the original required query(24). There are many tasks that NLP can be used for. Here we are only focusing and referring to a certain task of NLP which is Deep analytics. Deep analytics is the task of data processing from multiple source documents to allow the extracting of valid specific knowledge. Deep analytics is important when targeting highly comprehensive queries with glandular source documents that are both structured and unstructured. It is used in different sectors including social network feeding, trends, Rich Site Summary (RSS), biomedical industries and data mining for certain business values(25). As we mentioned earlier, our algorithm will superimpose neural network modeling on textural NLP. The approach provides two strong artificial intelligence tools that synergize each other. This approach had gotten more popular recently with very encouraging outcomes (26). Neural Networks are very robust learning approaches that can adapt to different learning model from supervised to unsupervised and from liner to non-linear models. On the other hand, NLP algorithms are created mainly for machine learning models. They rely on analyzing a set of examples (collection of highlighted sentences), then making ...
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