The application of natural language processing technology in hospital network information management systems: Potential for improving diagnostic accuracy and efficiency
Background: Processing scanned documents in electronic health records (EHR) was one of the problem in hospital network information management systems (HNIMS). To overcome this difficulty, the complex interactions among natural language processing (NLP), optical character recognition (OCR) and image...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | SLAS Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2472630325000457 |
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| Summary: | Background: Processing scanned documents in electronic health records (EHR) was one of the problem in hospital network information management systems (HNIMS). To overcome this difficulty, the complex interactions among natural language processing (NLP), optical character recognition (OCR) and image preprocessing was used. Objective: The goal is to investigate the possibilities of improving diagnostic efficiency and accuracy in healthcare settings by using NLP technologies into HNIMS. These individuals received diagnoses for a wide range of sleep problems. The data collected were converted into scanned PDF images which were then preprocessed by using gray scaling and OCR. Bag of Words (BoW) is used to extract the featured data. Method: Reports are divided among 70 % training and 30 % test sets for NLP model evaluation. By employing a hidden Bayesian technique on the development set, we suggest a novel hidden Bayesian integrated dense Bi-LSTM (HB-DBi-LSTM) strategy for optimizing bag-of-words models. A 6:1 ratio is further separated for training and validation sets in deep learning-based sequence models because of their high computing requirements. After 100 epochs of Adam optimization, the dense Bi-LSTM model is trained. Result: The models are evaluated assessed at the segment level for AHI and SaO2 for ROC and AUROC on test sets. In the finding assessment phase, the detection capacity of the suggested model is evaluated using many criteria, such as F1-score (0.9637), accuracy (0.9321), recall (0.9421) and precision (0.9532). To evaluate information extraction, a document-level examination is also carried out. Conclusion: To improve diagnostic speed and accuracy, especially when handling scanned documents in EHR, it emphasizes the critical need for strong natural language processing (NLP) systems inside HNIMS. |
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| ISSN: | 2472-6303 |