Quantitative feature extraction of unstructured data from GitLab BioAI pathology reports of cancer using an enhanced RPA NLP method

Abstract

Unstructured pathology report plays a major role in definitive cancer diagnosis. Accessing or searching unstructured textual information from the clinical pathology reports is one of the major concerns in cancer healthcare sector to provide precise medicine, analysis of cancer outcomes, providing cancer care services, accurate measurement for future prediction, treatment history, and comparative future research work. An efficient methodology has to be introduced for to extract quantitative information from the unstructured cancer data. Integrating computational intelligence in Robotic Process Automation can be done to process this data and automate repetitive activities for evaluating patients clinical pathology report. RPA-based NLP BERT system is designed and evaluated to automatically extract information on these variables for the patients from pathology report. In order to detect tumour and outcomes from documented pathology reports, a supervised machine learning keyword based extraction algorithm was developed in which the pathology data are examined to extract keywords from 2087 reports with 1579 of data reports being processed for the development phase and 508 of data being used for evaluation. The precision recall and accuracy are calculated for organ specimens for cancer test as (0.984, 0.982, 0.9839), test methodology(0.986, 0.981,0.9956) and pathological result(0.986, 0.9938, 0.9795) were achieved. The feasibility of autonomously extracting pre-defined data from clinical narratives for cancer research were established in this work. The outcomes showed that our methodology was suitable for actual use in obtaining essential information from pathology reports.