Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2310.19967 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913453005864960 |
|---|---|
| author | Wang, Bing Li, Weizi Bradlow, Anthony Chan, Antoni T. Y. Bazuaye, Eghosa |
| author_facet | Wang, Bing Li, Weizi Bradlow, Anthony Chan, Antoni T. Y. Bazuaye, Eghosa |
| contents | Early detection of inflammatory arthritis (IA) is critical to efficient and accurate hospital referral triage for timely treatment and preventing the deterioration of the IA disease course, especially under limited healthcare resources. The manual assessment process is the most common approach in practice for the early detection of IA, but it is extremely labor-intensive and inefficient. A large amount of clinical information needs to be assessed for every referral from General Practice (GP) to the hospitals. Machine learning shows great potential in automating repetitive assessment tasks and providing decision support for the early detection of IA. However, most machine learning-based methods for IA detection rely on blood testing results. But in practice, blood testing data is not always available at the point of referrals, so we need methods to leverage multimodal data such as semi-structured and unstructured data for early detection of IA. In this research, we present fusion and ensemble learning-based methods using multimodal data to assist decision-making in the early detection of IA, and a conformal prediction-based method to quantify the uncertainty of the prediction and detect any unreliable predictions. To the best of our knowledge, our study is the first attempt to utilize multimodal data to support the early detection of IA from GP referrals. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_19967 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Early detection of inflammatory arthritis to improve referrals using multimodal machine learning from blood testing, semi-structured and unstructured patient records Wang, Bing Li, Weizi Bradlow, Anthony Chan, Antoni T. Y. Bazuaye, Eghosa Machine Learning Early detection of inflammatory arthritis (IA) is critical to efficient and accurate hospital referral triage for timely treatment and preventing the deterioration of the IA disease course, especially under limited healthcare resources. The manual assessment process is the most common approach in practice for the early detection of IA, but it is extremely labor-intensive and inefficient. A large amount of clinical information needs to be assessed for every referral from General Practice (GP) to the hospitals. Machine learning shows great potential in automating repetitive assessment tasks and providing decision support for the early detection of IA. However, most machine learning-based methods for IA detection rely on blood testing results. But in practice, blood testing data is not always available at the point of referrals, so we need methods to leverage multimodal data such as semi-structured and unstructured data for early detection of IA. In this research, we present fusion and ensemble learning-based methods using multimodal data to assist decision-making in the early detection of IA, and a conformal prediction-based method to quantify the uncertainty of the prediction and detect any unreliable predictions. To the best of our knowledge, our study is the first attempt to utilize multimodal data to support the early detection of IA from GP referrals. |
| title | Early detection of inflammatory arthritis to improve referrals using multimodal machine learning from blood testing, semi-structured and unstructured patient records |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2310.19967 |