A preliminary prediction model using a deep learning software program for prolonged hospitalization after cardiovascular surgery
Abstract A prolonged length of hospital stay (LOS) has become an important issue among patients undergoing cardiovascular surgery in our aging society. However, there are no established prediction models for a prolonged LOS. We therefore created a prediction model of a prolonged LOS using a deep learning software program (Prediction One; Sony Network Communications Inc., Tokyo, Japan) using preoperative data. Subjects were 157 patients (121 for training data, 36 for validation data). A prolonged LOS was defined as a more than 30-day postoperative stay due to physical inactivity. The area under the receiver operating characteristic curve and the accuracy of the model in the validation data were 0.806 and 67%, respectively. In conclusion, the preliminary model demonstrated acceptable performance for the prediction of a prolonged LOS after cardiovascular surgery..
| Medienart: |
Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:53 |
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| Enthalten in: |
Surgery today - 53(2022), 3 vom: 05. Aug., Seite 393-395 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Murase, Ryota [VerfasserIn] |
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| Links: |
Volltext [lizenzpflichtig] |
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| Themen: |
Artificial intelligence |
| Anmerkungen: |
© The Author(s) under exclusive licence to Springer Nature Singapore Pte Ltd. 2022 |
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| doi: |
10.1007/s00595-022-02565-w |
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| 520 | |a Abstract A prolonged length of hospital stay (LOS) has become an important issue among patients undergoing cardiovascular surgery in our aging society. However, there are no established prediction models for a prolonged LOS. We therefore created a prediction model of a prolonged LOS using a deep learning software program (Prediction One; Sony Network Communications Inc., Tokyo, Japan) using preoperative data. Subjects were 157 patients (121 for training data, 36 for validation data). A prolonged LOS was defined as a more than 30-day postoperative stay due to physical inactivity. The area under the receiver operating characteristic curve and the accuracy of the model in the validation data were 0.806 and 67%, respectively. In conclusion, the preliminary model demonstrated acceptable performance for the prediction of a prolonged LOS after cardiovascular surgery. | ||
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Prolonged hospital stay | |
| 650 | 4 | |a Cardiovascular surgery | |
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| 700 | 1 | |a Wakasa, Satoru |4 aut | |
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