Development of a Machine-Learning Model of Short-Term Prognostic Prediction for Spinal Stenosis Surgery in Korean Patients
Background: In this study, based on machine-learning technology, we aim to develop a predictive model of the short-term prognosis of Korean patients who received spinal stenosis surgery. Methods: Using the data obtained from 112 patients with spinal stenosis admitted at N hospital from February to N...
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| Format: | Article |
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MDPI AG
2020-10-01
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| Series: | Brain Sciences |
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| Online Access: | https://www.mdpi.com/2076-3425/10/11/764 |
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| author | Kyeong-Rae Kim Hyeun Sung Kim Jae-Eun Park Seung-Yeon Kang So-Young Lim Il-Tae Jang |
| author_facet | Kyeong-Rae Kim Hyeun Sung Kim Jae-Eun Park Seung-Yeon Kang So-Young Lim Il-Tae Jang |
| author_sort | Kyeong-Rae Kim |
| collection | DOAJ |
| description | Background: In this study, based on machine-learning technology, we aim to develop a predictive model of the short-term prognosis of Korean patients who received spinal stenosis surgery. Methods: Using the data obtained from 112 patients with spinal stenosis admitted at N hospital from February to November, 2019, a predictive analysis was conducted for the pain index, reoperation, and surgery time. Results: Results show that the predicted area under the curve was 0.803, 0.887, and 0.896 for the pain index, reoperation, and surgery time, respectively, thereby indicating the accuracy of the model. Conclusion: This study verified that the individual characteristics of the patient and treatment characteristics during surgery enable a prediction of the patient prognosis and validate the accuracy of the approach. Further studies should be conducted to extend the scope of this research by incorporating a larger and more accurate dataset. |
| first_indexed | 2024-03-10T15:25:25Z |
| format | Article |
| id | doaj.art-396b135b21d943528b3fda266cac0bfd |
| institution | Directory Open Access Journal |
| issn | 2076-3425 |
| language | English |
| last_indexed | 2024-03-10T15:25:25Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj.art-396b135b21d943528b3fda266cac0bfd2023-11-20T18:06:49ZengMDPI AGBrain Sciences2076-34252020-10-01101176410.3390/brainsci10110764Development of a Machine-Learning Model of Short-Term Prognostic Prediction for Spinal Stenosis Surgery in Korean PatientsKyeong-Rae Kim0Hyeun Sung Kim1Jae-Eun Park2Seung-Yeon Kang3So-Young Lim4Il-Tae Jang5Nanoori Medical Research Institute, Nanoori Hospital Gangnam, Seoul 06048, KoreaDepartment of Neurosurgery, Nanoori Hospital Gangnam, Seoul 06048, KoreaNanoori Medical Research Institute, Nanoori Hospital Gangnam, Seoul 06048, KoreaDepartment of Anesthesia and Pain Medicine, Nanoori Hospital Gangnam, Seoul 06048, KoreaNanoori Medical Research Institute, Nanoori Hospital Gangnam, Seoul 06048, KoreaDepartment of Neurosurgery, Nanoori Hospital Gangnam, Seoul 06048, KoreaBackground: In this study, based on machine-learning technology, we aim to develop a predictive model of the short-term prognosis of Korean patients who received spinal stenosis surgery. Methods: Using the data obtained from 112 patients with spinal stenosis admitted at N hospital from February to November, 2019, a predictive analysis was conducted for the pain index, reoperation, and surgery time. Results: Results show that the predicted area under the curve was 0.803, 0.887, and 0.896 for the pain index, reoperation, and surgery time, respectively, thereby indicating the accuracy of the model. Conclusion: This study verified that the individual characteristics of the patient and treatment characteristics during surgery enable a prediction of the patient prognosis and validate the accuracy of the approach. Further studies should be conducted to extend the scope of this research by incorporating a larger and more accurate dataset.https://www.mdpi.com/2076-3425/10/11/764machine learningpredictionpilot studyspinal surgeryKorean |
| spellingShingle | Kyeong-Rae Kim Hyeun Sung Kim Jae-Eun Park Seung-Yeon Kang So-Young Lim Il-Tae Jang Development of a Machine-Learning Model of Short-Term Prognostic Prediction for Spinal Stenosis Surgery in Korean Patients Brain Sciences machine learning prediction pilot study spinal surgery Korean |
| title | Development of a Machine-Learning Model of Short-Term Prognostic Prediction for Spinal Stenosis Surgery in Korean Patients |
| title_full | Development of a Machine-Learning Model of Short-Term Prognostic Prediction for Spinal Stenosis Surgery in Korean Patients |
| title_fullStr | Development of a Machine-Learning Model of Short-Term Prognostic Prediction for Spinal Stenosis Surgery in Korean Patients |
| title_full_unstemmed | Development of a Machine-Learning Model of Short-Term Prognostic Prediction for Spinal Stenosis Surgery in Korean Patients |
| title_short | Development of a Machine-Learning Model of Short-Term Prognostic Prediction for Spinal Stenosis Surgery in Korean Patients |
| title_sort | development of a machine learning model of short term prognostic prediction for spinal stenosis surgery in korean patients |
| topic | machine learning prediction pilot study spinal surgery Korean |
| url | https://www.mdpi.com/2076-3425/10/11/764 |
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