Advancements in Natural Language Processing for Automated Phenotyping and Predictive Analytics in Oncology EHRS

• Author(s): Rishi Reddy Kothinti
• Paper ID: 1707223
• Page: 245-252
• Published Date: 30-04-2021
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 4 Issue 10 April-2021

Abstract

Hospital systems using Electronic Health Records in oncology now depend heavily on Natural Language Processing technology to deliver automated phenotyping while generating predictive analytics for better patient care and clinical research. EHRs for oncology contain extensive unstructured clinical text that includes physician notes together with pathology reports and radiology findings which traditional manual extraction cannot efficiently process. The unstructured EHR data becomes useful through advanced linguistic methods reinforced by machine learning that helps computers automatically detect patient characteristics and disease types and biomarkers. NLP-based systems strengthen analysis of intricate medical stories to enhance disease grouping and patient profiling which supports the development of precision medicine in oncology treatment. Progress made in transformer NLP models including BERT and Bio BERT along with GPT-based systems has resulted in major improvements of clinical text processing efficiency and accuracy. The models deliver exceptional performance for all aspects of named entity recognition (NER) and clinical text mining and predictive modeling tasks in oncology work. The combination of predicting analytics with NLP technology provides physicians with data-based choices by helping them anticipate disease progression and treatment outcomes along with patient survival possibilities. Real-world evidence generation becomes possible through NLP because it systematizes the analysis of extensive oncology EHR datasets which advances the development of patient-specific therapy plans and identification of drug responses. Various obstacles stand in the way of NLP's wider acceptance for clinical oncology applications. Primary obstacles for clinical adoption stem from the need to address data protection matters together with both explanation limits of models and language hurdles unique to oncology domains. The application of NLP models which receive training from generic biomedical material needs domain-specific adaptation to understand cancer-related terminology properly. Successful implementation of NLP in regular oncology practice demands interdisciplinary collaboration together with better model transparency measures and compliance with regulatory standards to handle current technical obstacles. Artificial intelligence combined with computational biology and clinical oncology will drive NLP-driven insight potential through continuous field development to establish precise data-driven personalized cancer care.

Keywords

Cloud Migration, Compliance, Cost Optimization, Scalability, Security

Citations

IRE Journals:
Rishi Reddy Kothinti "Advancements in Natural Language Processing for Automated Phenotyping and Predictive Analytics in Oncology EHRS" Iconic Research And Engineering Journals Volume 4 Issue 10 2021 Page 245-252

IEEE:
Rishi Reddy Kothinti "Advancements in Natural Language Processing for Automated Phenotyping and Predictive Analytics in Oncology EHRS" Iconic Research And Engineering Journals, 4(10)