arXiv:2607.05644v1 Announce Type: cross 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
The proliferation of NLP applications, particularly in critical fields like clinical research, necessitates robust and systematic development methodologies.
A methodical approach to NLP development, integrating System Development Life Cycle (SDLC) principles, can significantly improve the reliability and effectiveness of AI systems in sensitive applications.
The focus is shifting from purely algorithmic knowledge to comprehensive lifecycle management for successful NLP project implementation, emphasizing systematic processes over ad-hoc development.
- · Healthcare sector
- · Clinical research organizations
- · NLP system integrators
- · Data scientists
- · Ad-hoc AI development firms
- · Organizations with unstructured data
Improved accuracy and trustworthiness of information extracted from large datasets using NLP.
Faster and more reliable insights from medical records for research and decision-making, potentially accelerating drug discovery and personalized medicine.
Standardization of NLP development paving the way for easier compliance and integration into highly regulated industries, leading to broader adoption and greater societal impact.
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Read at arXiv cs.AI