Application of natural language processing techniques to identify off-label drug usage from various online health communities

Dreyfus, Brian; Chaudhary, Anuj; Bhardwaj, Parth; Shree, V Karthikhaa

doi:10.1093/jamia/ocab124

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…Statistics-based machine learning algorithms leveraging manually annotated corpora for supervised training have exhibited a significant increase in accuracy over rule-based and lexicon-based entity recognition approaches [ 11 , 12 ]. With the advent of deep learning, numerous neural-network-based models have effectively been used for the textual entity recognition of biological documents [ 13 , 14 ], electronic medical records [ 15 , 16 , 17 ], and online health communities [ 18 , 19 , 20 ] Dreyfus Dreyfus. Based on the entity recognition infrastructure deep learning model LSTM-CRF, Guillaume Lample et al [ 21 ] proposed a neural network model that combines bidirectional long short-term memory (BiLSTM) and conditional random fields (CRFs).…”

Section: Introductionmentioning

confidence: 99%

Named Entity Recognition of Diabetes Online Health Community Data Using Multiple Machine Learning Models

Zhou

Liao

et al. 2023

Bioengineering

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The rising prevalence of diabetes and the increasing awareness of self-health management have resulted in a surge in diabetes patients seeking health information and emotional support in online health communities. Consequently, there is a vast database of patient consultation information in these online health communities. However, due to the heterogeneity and incompleteness of the content, mining medical information and patient health data from these communities can be a challenge. To address this issue, we built the RoBERTa-BiLSTM-CRF (RBC) model for identifying entities in the online health community of diabetes. We selected 1889 question–answer texts from the most active online health community in China, Good Doctor Online, and used these public data to identify five types of entities. In addition, we conducted a comparative evaluation with three other commonly used models to validate the performance of our proposed model, including RoBERTa-CRF (RC), BilSTM-CRF (BC), and RoBERTa-Softmax (RS). The results showed that the RBC model achieved excellent performance on the test set, with an accuracy of 81.2% and an F1 score of 80.7%, outperforming the performance of traditional entity recognition models in named entity recognition in online medical communities for doctors and diabetes patients. The high performance of entity recognition in online health communities will provide a crucial knowledge source for constructing medical knowledge graphs. This integration would help alleviate the growing demand for medical consultations and the strain on healthcare resources, while assisting healthcare professionals in making informed decisions and providing personalized services to patients.

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Section: Introductionmentioning

confidence: 99%

Named Entity Recognition of Diabetes Online Health Community Data Using Multiple Machine Learning Models

Zhou

Liao

et al. 2023

Bioengineering

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The Application of Natural Language Processing Technology Based on Deep Learning in Japanese Sentiment Analysis

Zhang

2023

2023 International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE)

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Performance Improvement of the Natural Language Processing Tool to Extract Patient Narratives Related to Medical States From Japanese Pharmaceutical Care Records: Effects of an Increase in the Amount of Training Data (Preprint)

Ohno,

Aomori,

Nishiyama

et al. 2024

Preprint

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BACKGROUND To improve pharmacotherapy, patients’ oral expressions serve as valuable sources of clinical information. Natural language processing (NLP) is a useful approach for analyzing unstructured text data, such as patient narratives. However, few studies have focused on using NLP for narratives in the Japanese language. OBJECTIVE To develop a high-performance NLP system for extracting clinical information from patient narratives, we examined the performance progression as the amount of training data was gradually increased. METHODS Subjective texts from the pharmaceutical care records of Keio University Hospital from April 1, 2018 to March 31, 2019, comprising 12,004 records from 6,559 cases, were used. After preprocessing, we annotated diseases and symptoms within the texts. We then trained and evaluated deep learning models—bidirectional encoder representations from transformers combined with a conditional random field (BERT-CRF)—by 10-fold cross-validation. The annotated data were divided into 10 subsets, and the amount of training data was progressively increased over 10 steps. We also analyzed the causes of errors. Finally, we applied the developed system to the analysis of case report texts to evaluate its usability for texts from other sources. RESULTS The F1-score of the system improved from 0.67 to 0.82 as the amount of training data increased from 1,200 to 12,004 records. The F1-score reached 0.78 with 3,600 records and largely saturated thereafter. As performance improved, errors from incorrect extractions decreased significantly, increasing precision. For case reports, the F1-score also increased from 0.34 to 0.41 as the training dataset expanded from 1,200 to 12,004 records. Performance was lower for extracting symptoms from case report texts compared with pharmaceutical care records, suggesting that this system is more specialized for analyzing subjective data from pharmaceutical care records. CONCLUSIONS We successfully developed a high-performance system specialized in analyzing subjective data from pharmaceutical care records by training a large dataset, with near-complete saturation of system performance with about 3,600 training records. This system will be useful for monitoring symptoms, offering benefits for both clinical practice and research.

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Application of natural language processing techniques to identify off-label drug usage from various online health communities

Cited by 3 publications

References 35 publications

Named Entity Recognition of Diabetes Online Health Community Data Using Multiple Machine Learning Models

Named Entity Recognition of Diabetes Online Health Community Data Using Multiple Machine Learning Models

The Application of Natural Language Processing Technology Based on Deep Learning in Japanese Sentiment Analysis

Performance Improvement of the Natural Language Processing Tool to Extract Patient Narratives Related to Medical States From Japanese Pharmaceutical Care Records: Effects of an Increase in the Amount of Training Data (Preprint)

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