🤖 AI Summary
This work addresses the frequent failure of natural language processing (NLP) projects in clinical settings, which often stems from a lack of systematic engineering practices and an overemphasis on algorithms at the expense of development rigor. To bridge this gap, the paper proposes a structured methodology grounded in the Systems Development Life Cycle (SDLC) framework to guide the end-to-end construction of NLP systems for extracting clinical information from electronic health records. By integrating SDLC principles throughout the NLP development pipeline, the approach counteracts the algorithm-centric bias prevalent in conventional tutorials and establishes a reproducible, generalizable development paradigm. This systematic integration enhances both the success rate and reliability of clinical text information extraction initiatives, offering a robust foundation for real-world deployment.
📝 Abstract
Natural language processing (NLP) is a common method for supplying data to clinical research and decision making by extracting information from electronic medical records. Numerous textbooks and tutorials describe specific algorithms and applications for text processing, yet algorithmic knowledge is only one ingredient of a successful NLP project. Drawing on the available literature, this paper presents a stepwise approach that applies the Systems Development Life Cycle (SDLC) to projects that rely on data extraction through language processing.