Particle filter-based parameter estimation algorithm for prognostic risk assessment of progression in non-small cell lung cancer
© 2023. The Author(s)..
Non-small cell lung cancer (NSCLC) is a malignant tumor that threatens human life and health. The development of a new NSCLC risk assessment model based on electronic medical records has great potential for reducing the risk of cancer recurrence. In this process, machine learning is a powerful method for automatically extracting risk factors and indicating impact weights for NSCLC deaths. However, when the number of samples reaches a certain value, it is difficult for machine learning to improve the prediction accuracy, and it is also challenging to use the characteristic data of subsequent patients effectively. Therefore, this study aimed to build a postoperative survival risk assessment model for patients with NSCLC that updates the model parameters and improves model accuracy based on new patient data. The model perspective was a combination of particle filtering and parameter estimation. To demonstrate the feasibility and further evaluate the performance of our approach, we performed an empirical analysis experiment. The study showed that our method achieved an overall accuracy of 92% and a recall of 71% for deceased patients. Compared with traditional machine learning models, the accuracy of the model estimated by particle filter parameters has been improved by 2%, and the recall rate for dead patients has been improved by 11%. Additionally, this study outcome shows that this method can better utilize subsequent patients' characteristic data, be more relevant to different patients, and help achieve precision medicine.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:23 |
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| Enthalten in: |
BMC medical informatics and decision making - 23(2023), 1 vom: 20. Dez., Seite 296 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Shang, Shi [VerfasserIn] |
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| Links: |
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| Themen: |
Journal Article |
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| Anmerkungen: |
Date Completed 22.12.2023 Date Revised 11.01.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1186/s12911-023-02373-3 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM366147439 |
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| 245 | 1 | 0 | |a Particle filter-based parameter estimation algorithm for prognostic risk assessment of progression in non-small cell lung cancer |
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| 500 | |a Date Revised 11.01.2024 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023. The Author(s). | ||
| 520 | |a Non-small cell lung cancer (NSCLC) is a malignant tumor that threatens human life and health. The development of a new NSCLC risk assessment model based on electronic medical records has great potential for reducing the risk of cancer recurrence. In this process, machine learning is a powerful method for automatically extracting risk factors and indicating impact weights for NSCLC deaths. However, when the number of samples reaches a certain value, it is difficult for machine learning to improve the prediction accuracy, and it is also challenging to use the characteristic data of subsequent patients effectively. Therefore, this study aimed to build a postoperative survival risk assessment model for patients with NSCLC that updates the model parameters and improves model accuracy based on new patient data. The model perspective was a combination of particle filtering and parameter estimation. To demonstrate the feasibility and further evaluate the performance of our approach, we performed an empirical analysis experiment. The study showed that our method achieved an overall accuracy of 92% and a recall of 71% for deceased patients. Compared with traditional machine learning models, the accuracy of the model estimated by particle filter parameters has been improved by 2%, and the recall rate for dead patients has been improved by 11%. Additionally, this study outcome shows that this method can better utilize subsequent patients' characteristic data, be more relevant to different patients, and help achieve precision medicine | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a NSCLC | |
| 650 | 4 | |a Parameter estimation | |
| 650 | 4 | |a Particle filtering | |
| 650 | 4 | |a Risk assessment model | |
| 700 | 1 | |a Yuan, Junyi |e verfasserin |4 aut | |
| 700 | 1 | |a Pan, Changqing |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Sufen |e verfasserin |4 aut | |
| 700 | 1 | |a Tu, Xuemin |e verfasserin |4 aut | |
| 700 | 1 | |a Cen, Xingxing |e verfasserin |4 aut | |
| 700 | 1 | |a Mi, Linhui |e verfasserin |4 aut | |
| 700 | 1 | |a Hou, Xumin |e verfasserin |4 aut | |
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