Optimizing classification of diseases through language model analysis of symptoms
Abstract This paper investigated the use of language models and deep learning techniques for automating disease prediction from symptoms. Specifically, we explored the use of two Medical Concept Normalization—Bidirectional Encoder Representations from Transformers (MCN-BERT) models and a Bidirection...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-51615-5 |
| _version_ | 1797350000575905792 |
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| author | Esraa Hassan Tarek Abd El-Hafeez Mahmoud Y. Shams |
| author_facet | Esraa Hassan Tarek Abd El-Hafeez Mahmoud Y. Shams |
| author_sort | Esraa Hassan |
| collection | DOAJ |
| description | Abstract This paper investigated the use of language models and deep learning techniques for automating disease prediction from symptoms. Specifically, we explored the use of two Medical Concept Normalization—Bidirectional Encoder Representations from Transformers (MCN-BERT) models and a Bidirectional Long Short-Term Memory (BiLSTM) model, each optimized with a different hyperparameter optimization method, to predict diseases from symptom descriptions. In this paper, we utilized two distinct dataset called Dataset-1, and Dataset-2. Dataset-1 consists of 1,200 data points, with each point representing a unique combination of disease labels and symptom descriptions. While, Dataset-2 is designed to identify Adverse Drug Reactions (ADRs) from Twitter data, comprising 23,516 rows categorized as ADR (1) or Non-ADR (0) tweets. The results indicate that the MCN-BERT model optimized with AdamP achieved 99.58% accuracy for Dataset-1 and 96.15% accuracy for Dataset-2. The MCN-BERT model optimized with AdamW performed well with 98.33% accuracy for Dataset-1 and 95.15% for Dataset-2, while the BiLSTM model optimized with Hyperopt achieved 97.08% accuracy for Dataset-1 and 94.15% for Dataset-2. Our findings suggest that language models and deep learning techniques have promise for supporting earlier detection and more prompt treatment of diseases, as well as expanding remote diagnostic capabilities. The MCN-BERT and BiLSTM models demonstrated robust performance in accurately predicting diseases from symptoms, indicating the potential for further related research. |
| first_indexed | 2024-03-08T12:39:26Z |
| format | Article |
| id | doaj.art-70d66c7e97874a93a08c4e617ca3c6b9 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T12:39:26Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-70d66c7e97874a93a08c4e617ca3c6b92024-01-21T12:16:19ZengNature PortfolioScientific Reports2045-23222024-01-0114112410.1038/s41598-024-51615-5Optimizing classification of diseases through language model analysis of symptomsEsraa Hassan0Tarek Abd El-Hafeez1Mahmoud Y. Shams2Faculty of Artificial Intelligence, Kafrelsheikh UniversityDepartment of Computer Science, Faculty of Science, Minia UniversityFaculty of Artificial Intelligence, Kafrelsheikh UniversityAbstract This paper investigated the use of language models and deep learning techniques for automating disease prediction from symptoms. Specifically, we explored the use of two Medical Concept Normalization—Bidirectional Encoder Representations from Transformers (MCN-BERT) models and a Bidirectional Long Short-Term Memory (BiLSTM) model, each optimized with a different hyperparameter optimization method, to predict diseases from symptom descriptions. In this paper, we utilized two distinct dataset called Dataset-1, and Dataset-2. Dataset-1 consists of 1,200 data points, with each point representing a unique combination of disease labels and symptom descriptions. While, Dataset-2 is designed to identify Adverse Drug Reactions (ADRs) from Twitter data, comprising 23,516 rows categorized as ADR (1) or Non-ADR (0) tweets. The results indicate that the MCN-BERT model optimized with AdamP achieved 99.58% accuracy for Dataset-1 and 96.15% accuracy for Dataset-2. The MCN-BERT model optimized with AdamW performed well with 98.33% accuracy for Dataset-1 and 95.15% for Dataset-2, while the BiLSTM model optimized with Hyperopt achieved 97.08% accuracy for Dataset-1 and 94.15% for Dataset-2. Our findings suggest that language models and deep learning techniques have promise for supporting earlier detection and more prompt treatment of diseases, as well as expanding remote diagnostic capabilities. The MCN-BERT and BiLSTM models demonstrated robust performance in accurately predicting diseases from symptoms, indicating the potential for further related research.https://doi.org/10.1038/s41598-024-51615-5 |
| spellingShingle | Esraa Hassan Tarek Abd El-Hafeez Mahmoud Y. Shams Optimizing classification of diseases through language model analysis of symptoms Scientific Reports |
| title | Optimizing classification of diseases through language model analysis of symptoms |
| title_full | Optimizing classification of diseases through language model analysis of symptoms |
| title_fullStr | Optimizing classification of diseases through language model analysis of symptoms |
| title_full_unstemmed | Optimizing classification of diseases through language model analysis of symptoms |
| title_short | Optimizing classification of diseases through language model analysis of symptoms |
| title_sort | optimizing classification of diseases through language model analysis of symptoms |
| url | https://doi.org/10.1038/s41598-024-51615-5 |
| work_keys_str_mv | AT esraahassan optimizingclassificationofdiseasesthroughlanguagemodelanalysisofsymptoms AT tarekabdelhafeez optimizingclassificationofdiseasesthroughlanguagemodelanalysisofsymptoms AT mahmoudyshams optimizingclassificationofdiseasesthroughlanguagemodelanalysisofsymptoms |