Sivarajkumar, Sonish

PURPOSE Manual extraction of treatment outcomes from unstructured oncology clinical notes is a significant challenge for real-world evidence (RWE) generation. This study aimed to develop and evaluate a robust natural language processing (NLP) system to automatically extract cancer treatments and their associated RECIST-based response categories (complete response, partial response, stable disease, and progressive disease) from non–small cell lung cancer (NSCLC) clinical notes. METHODS This retrospective NLP development and validation study used a corpus of 250 NSCLC oncology notes from University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center, annotated by physician experts. An end-to-end NLP pipeline was designed, integrating a rule-based module for entity extraction (treatments and responses) and a machine learning module using biomedical clinical bidirectional encoder representations from transformers for relation classification. The system's performance was evaluated on a held-out test set, with partial external validation for relation extraction on a Mayo Clinic data set. RESULTS The NLP system achieved high overall accuracy. On the UPMC test set (64 notes), the relation classification model attained an area under the receiver operating characteristic curve of 0.94 and an F1 score of 0.92 for linking treatments with documented responses. The rule-based entity extraction demonstrated a macro-averaged F1 score of 0.87 (precision 0.98, recall 0.81). Although precision was high for chemotherapy and most response types (1.00), recall for cancer surgery was 0.45. External validation at Mayo Clinic showed moderate relation extraction F1 scores (range: 0.51-0.64). CONCLUSION The proposed NLP system can reliably extract structured treatment and response information from unstructured NSCLC oncology notes with high accuracy. This automated approach can assist in abstracting critical cancer treatment outcomes from clinical narrative text, thereby streamlining real-world data analysis and supporting the generation of RWE in oncology.

Abstract Background: Clinical information retrieval (IR) plays a vital role in modern healthcare by facilitating efficient access and analysis of medical literature for clinicians and researchers. This scoping review aims to offer a comprehensive overview of the current state of clinical IR research and identify gaps and potential opportunities for future studies in this field. Objectives: The main objective of this review is to identify and analyze published research on clinical IR, including the methods, techniques, and tools used to retrieve and analyze clinical information from various sources. We aim to provide a comprehensive overview of the current state of clinical IR research and guide future research efforts in this field. Methods: We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and conducted a thorough search of multiple databases, including Ovid Embase, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, Scopus, ACM Digital Library, IEEE Xplore, and Web of Science, from January 1st, 2012, to January 4th, 2023. The screening process involved multiple reviewers, and we included 184 papers for the final review. Results: We conducted a detailed analysis and discussion of various aspects of clinical IR research, including publication year, data sources, methods, techniques, evaluation metrics, shared tasks, and applications. Our analysis revealed key research areas in clinical IR, such as indexing, ranking, and query expansion, and identified opportunities for future research in these areas.