A Natural Language Processing Approach to Automated Highlighting of New Information in Clinical Notes

Su, Yu-Hsiang; Chao, Ching-Ping; Hung, Ling-Chien; Sung, Sheng Feng; Lee, Pei-Ju

doi:10.3390/app10082824

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…One prominent example in this area is analyzing and extracting data from the clinical notes entered by doctors in free form. One recent study utilized the Bigram Language Model for annotating clinical notes to distinguish new information from redundant and improve the performance of medical personnel [12]. Another example of applying NLP in healthcare is extracting adverse drug reactions and interactions from medical texts.…”

Section: Related Workmentioning

confidence: 99%

Automatic Extraction of Medication Information from Cylindrically Distorted Pill Bottle Labels

Gromova

Elangovan

2022

MAKE

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Patient compliance with prescribed medication regimens is critical for maintaining health and managing disease and illness. To encourage patient compliance, multiple aids, like automatic pill dispensers, pill organizers, and various reminder applications, have been developed to help people adhere to their medication regimens. However, when utilizing these aids, the user or patient must manually enter their medication information and schedule. This process is time-consuming and often prone to error. For example, elderly patients may have difficulty reading medication information on the bottle due to decreased eyesight, leading them to enter medication information incorrectly. This study explored methods for extracting pertinent information from cylindrically distorted prescription drug labels using Machine Learning and Computer Vision techniques. This study found that Deep Convolutional Neural Networks (DCNN) performed better than other techniques in identifying label key points under different lighting conditions and various backgrounds. This method achieved a percentage of Correct Key points PCK @ 0.03 of 97%. These key points were then used to correct the cylindrical distortion. Next, the multiple dewarped label images were stitched together and processed by an Optical Character Recognition (OCR) engine. Pertinent information, such as patient name, drug name, drug strength, and directions of use, were extracted from the recognized text using Natural Language Processing (NLP) techniques. The system created in this study can be used to improve patient health and compliance by creating an accurate medication schedule.

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Section: Related Workmentioning

confidence: 99%

Automatic Extraction of Medication Information from Cylindrically Distorted Pill Bottle Labels

Gromova

Elangovan

2022

MAKE

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“…To convert unstructured data into structured data that is readable, processable, and computable data, researchers use various methods [ 5 , 6 , 7 ]. These methods include a traditional approach via manual reviewing of notes and converting them into the structured data.…”

Section: Introductionmentioning

confidence: 99%

Validation of a Natural Language Processing Algorithm for the Extraction of the Sleep Parameters from the Polysomnography Reports

et al. 2022

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Background: There is a need to better understand the association between sleep and chronic diseases. In this study we developed a natural language processing (NLP) algorithm to mine polysomnography (PSG) free-text notes from electronic medical records (EMR) and evaluated the performance. Methods: Using the Veterans Health Administration EMR, we identified 46,093 PSG studies using CPT code 95,810 from 1 October 2000–30 September 2019. We randomly selected 200 notes to compare the accuracy of the NLP algorithm in mining sleep parameters including total sleep time (TST), sleep efficiency (SE) and sleep onset latency (SOL), wake after sleep onset (WASO), and apnea-hypopnea index (AHI) compared to visual inspection by raters masked to the NLP output. Results: The NLP performance on the training phase was >0.90 for precision, recall, and F-1 score for TST, SOL, SE, WASO, and AHI. The NLP performance on the test phase was >0.90 for precision, recall, and F-1 score for TST, SOL, SE, WASO, and AHI. Conclusions: This study showed that NLP is an accurate technique to extract sleep parameters from PSG reports in the EMR. Thus, NLP can serve as an effective tool in large health care systems to evaluate and improve patient care.

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Chinese medical event detection based on event frequency distribution ratio and document consistency

Han¹,

Zhang²,

Wu³

et al. 2023

MBE

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<abstract><p>Structured information especially medical events extracted from electronic medical records has extremely practical application value and play a basic role in various intelligent diagnosis and treatment systems. Fine-grained Chinese medical event detection is crucial in the process of structuring Chinese Electronic Medical Record (EMR). The current methods for detecting fine-grained Chinese medical events primarily rely on statistical machine learning and deep learning. However, they have two shortcomings: 1) they neglect to take into account the distribution characteristics of these fine-grained medical events. 2) they overlook the consistency in the distribution of medical events within each individual document. Therefore, this paper presents a fine-grained Chinese medical event detection method, which is based on event frequency distribution ratio and document consistency. To start with, a significant number of Chinese EMR texts are used to adapt the Chinese pre-training model BERT to the domain. Second, based on the fundamental features, the Event Frequency - Event Distribution Ratio (EF-DR) is devised to select distinct event information as supplementary features, taking into account the distribution of events within the EMR. Finally, using EMR document consistency within the model improves the outcome of event detection. Our experiments demonstrate that the proposed method significantly outperforms the baseline model.</p></abstract>

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A Natural Language Processing Approach to Automated Highlighting of New Information in Clinical Notes

Cited by 4 publications

References 31 publications

Automatic Extraction of Medication Information from Cylindrically Distorted Pill Bottle Labels

Automatic Extraction of Medication Information from Cylindrically Distorted Pill Bottle Labels

Validation of a Natural Language Processing Algorithm for the Extraction of the Sleep Parameters from the Polysomnography Reports

Chinese medical event detection based on event frequency distribution ratio and document consistency

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