TY - JOUR
T1 - Surgery's Rosetta Stone
T2 - Natural language processing to predict discharge and readmission after general surgery
AU - Kovoor, Joshua G
AU - Bacchi, Stephen
AU - Gupta, Aashray K
AU - Stretton, Brandon
AU - Nann, Silas D
AU - Aujayeb, Nidhi
AU - Lu, Amy
AU - Nathin, Kayla
AU - Lam, Lydia
AU - Jiang, Melinda
AU - Lee, Shane
AU - To, Minh-Son
AU - Ovenden, Christopher D
AU - Hewitt, Joseph N
AU - Goh, Rudy
AU - Gluck, Samuel
AU - Reid, Jessica L
AU - Khurana, Sanjeev
AU - Dobbins, Christopher
AU - Hewett, Peter J
AU - Padbury, Robert T
AU - Malycha, James
AU - Trochsler, Markus I
AU - Hugh, Thomas J
AU - Maddern, Guy J
PY - 2023/12
Y1 - 2023/12
N2 - Background: This study aimed to examine the accuracy with which multiple natural language processing artificial intelligence models could predict discharge and readmissions after general surgery. Methods: Natural language processing models were derived and validated to predict discharge within the next 48 hours and 7 days and readmission within 30 days (based on daily ward round notes and discharge summaries, respectively) for general surgery inpatients at 2 South Australian hospitals. Natural language processing models included logistic regression, artificial neural networks, and Bidirectional Encoder Representations from Transformers. Results: For discharge prediction analyses, 14,690 admissions were included. For readmission prediction analyses, 12,457 patients were included. For prediction of discharge within 48 hours, derivation and validation data set area under the receiver operator characteristic curves were, respectively: 0.86 and 0.86 for Bidirectional Encoder Representations from Transformers, 0.82 and 0.81 for logistic regression, and 0.82 and 0.81 for artificial neural networks. For prediction of discharge within 7 days, derivation and validation data set area under the receiver operator characteristic curves were, respectively: 0.82 and 0.81 for Bidirectional Encoder Representations from Transformers, 0.75 and 0.72 for logistic regression, and 0.68 and 0.67 for artificial neural networks. For readmission prediction within 30 days, derivation and validation data set area under the receiver operator characteristic curves were, respectively: 0.55 and 0.59 for Bidirectional Encoder Representations from Transformers and 0.77 and 0.62 for logistic regression. Conclusion: Modern natural language processing models, particularly Bidirectional Encoder Representations from Transformers, can effectively and accurately identify general surgery patients who will be discharged in the next 48 hours. However, these approaches are less capable of identifying general surgery patients who will be discharged within the next 7 days or who will experience readmission within 30 days of discharge.
AB - Background: This study aimed to examine the accuracy with which multiple natural language processing artificial intelligence models could predict discharge and readmissions after general surgery. Methods: Natural language processing models were derived and validated to predict discharge within the next 48 hours and 7 days and readmission within 30 days (based on daily ward round notes and discharge summaries, respectively) for general surgery inpatients at 2 South Australian hospitals. Natural language processing models included logistic regression, artificial neural networks, and Bidirectional Encoder Representations from Transformers. Results: For discharge prediction analyses, 14,690 admissions were included. For readmission prediction analyses, 12,457 patients were included. For prediction of discharge within 48 hours, derivation and validation data set area under the receiver operator characteristic curves were, respectively: 0.86 and 0.86 for Bidirectional Encoder Representations from Transformers, 0.82 and 0.81 for logistic regression, and 0.82 and 0.81 for artificial neural networks. For prediction of discharge within 7 days, derivation and validation data set area under the receiver operator characteristic curves were, respectively: 0.82 and 0.81 for Bidirectional Encoder Representations from Transformers, 0.75 and 0.72 for logistic regression, and 0.68 and 0.67 for artificial neural networks. For readmission prediction within 30 days, derivation and validation data set area under the receiver operator characteristic curves were, respectively: 0.55 and 0.59 for Bidirectional Encoder Representations from Transformers and 0.77 and 0.62 for logistic regression. Conclusion: Modern natural language processing models, particularly Bidirectional Encoder Representations from Transformers, can effectively and accurately identify general surgery patients who will be discharged in the next 48 hours. However, these approaches are less capable of identifying general surgery patients who will be discharged within the next 7 days or who will experience readmission within 30 days of discharge.
KW - Artificial intelligence
KW - Natural language processing
KW - General surgery
KW - Patient discharge
UR - http://www.scopus.com/inward/record.url?scp=85172916801&partnerID=8YFLogxK
U2 - 10.1016/j.surg.2023.08.021
DO - 10.1016/j.surg.2023.08.021
M3 - Article
C2 - 37778968
AN - SCOPUS:85172916801
SN - 0039-6060
VL - 174
SP - 1309
EP - 1314
JO - Surgery (United States)
JF - Surgery (United States)
IS - 6
ER -