Machine learning using preoperative patient factors can predict duration of surgery and length of stay for total knee arthroplasty

Abbas, Aazad; Lex, Johnathan R.; Toor, Jay; Ravi, Bheeshma; Khalil, Elias B.; Whyne, Cari

doi:10.1016/j.ijmedinf.2021.104670

Cited by 27 publications

(23 citation statements)

References 37 publications

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“…Previous work has shown that these factors may effectively predict case costing, length of stay, and duration of surgery. [41][42][43][44] Accordingly, incorporating these factors would help improve the accuracy of the models in this study. Fourthly, this study does not address the intangible educational value of call shifts for junior residents as they are mentored by their senior resident counterparts.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning to Improve Resident Scheduling: Harnessing Artificial Intelligence to Enhance Resident Wellness

Abbas

Toor

et al. 2022

Preprint

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Purpose Excessive resident duty hours (RDH) is a recognized issue with implications for physician well-being and patient safety. A significant component of the RDH concern is on-call duty. While other industries have adopted machine learning models (MLMs) to optimize scheduling and employee well-being, medicine has lagged. This study aimed to investigate the use of MLMs to predict demand on orthopaedic residents to optimize scheduling. Methods Daily surgical handover emails over an eight-year (2012–2019) period at a level I trauma centre were used to model demand on residents. Various MLMs were trained to predict the workload, with their results compared to the current approach. Quality of models was determined by using the area under the receiver operator curve (AUC) and accuracy. The top ten most important variables were extracted from the most successful model. Results The reduction in orthopaedic resident shifts possible per annum was 24.7%. The most successful model during testing was the neural network (AUC: 0.81, accuracy: 73.7%). All models were better than the current approach (AUC: 0.50, accuracy: 50.1%). Key variables used by the neural network model were (descending order): spine call duty (y/n), year, weekday/weekend, month, and day of the week. Conclusion This was the first study using MLMs to predict demand for orthopaedic residents at a major academic institution. All MLMs were more successful than the current scheduling approach. Future work should look to incorporate predictive models with optimization strategies, matching scheduling with demand to improve resident well-being and patient care. Level of evidence: Level III.

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Section: Discussionmentioning

confidence: 99%

Machine Learning to Improve Resident Scheduling: Harnessing Artificial Intelligence to Enhance Resident Wellness

Abbas

Toor

et al. 2022

Preprint

Self Cite

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“…They found that RFR is the best model that achieved the 5 MSE and 0.92 R 2 scores. In another study, Abbas et al [ 17 ] established a model based on multilayer perceptron (MLP) to predict LOS for total knee arthroplasty. The model uses medical data such as a patient's white blood cell count and type of anesthesia, and finally received 0.715 and 0.690 MSE scores on training and test sets, respectively.…”

Section: Related Workmentioning

confidence: 99%

“…e sum of values of all trees is the predicted value 􏽢 y i for the entire model. Since the algorithm belongs to the additive model, the predicted value of the Kth tree 􏽢 y k i can be expressed by formula (17). Let the sum of the truth values be y i .…”

Section: Extreme Gradient Boosting Regressionmentioning

confidence: 99%

Length of Stay Prediction Model of Indoor Patients Based on Light Gradient Boosting Machine

Zeng

2022

Computational Intelligence and Neuroscience

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The influx of hospital patients has become common in recent years. Hospital management departments need to redeploy healthcare resources to meet the massive medical needs of patients. In this process, the hospital length of stay (LOS) of different patients is a crucial reference to the management department. Therefore, building a model to predict LOS is of great significance. Five machine learning (ML) algorithms named Lasso regression (LR), ridge regression (RR), random forest regression (RFR), light gradient boosting machine (LightGBM), and extreme gradient boosting regression (XGBR) and six feature encoding methods named label encoding, count encoding, one-hot encoding, target encoding, leave-one-out encoding, and the proposed encoding method are used to construct the regression prediction model. The Scikit-Learn toolbox on the Python platform builds the prediction model. The input is the dataset named Hospital Inpatient Discharges (SPARCS De-Identified) 2017 with 2343569 instances provided by the New York State Department of Health verify the model after removing 2.2% of the missing data, and the model ultimately uses mean squared error (MSE) and coefficient of determination (R2) as the performance measurement. The results show that the model with the LightGBM algorithm and the proposed encoding method has the best R2 (96.0%) and MSE score (2.231).

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“…It uses a backpropagation technique 34 that has a nonlinear function to optimize the weights and reduce the prediction errors. A study found that MLP provided the best performance result among other machine learning models 35 .…”

Section: Model Construction and Experimental Setupmentioning

confidence: 99%

Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients

Cho

Ahn

Gwon

et al. 2022

Preprint

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Understanding the length of stay of severe patients who require general anesthesia is key to enhancing health outcomes. Here, we aim to discover how machine learning can support resource allocation management and decision-making resulting from the length of stay prediction. A retrospective cohort study was conducted from January 2018 to October 2020. A total cohort of 240,000 patients’ medical records was collected. The data were collected exclusively for preoperative variables to accurately analyze the predictive factors impacting the postoperative length of stay. The main outcome of this study is an analysis of the length of stay (in days) after surgery until discharge. The prediction was performed with ridge regression, random forest, XGBoost, and multi-layer perceptron neural network models. The XGBoost resulted in the best performance with an average error within 3 days. Moreover, we explain each feature’s contribution over the XGBoost model and further display distinct predictors affecting the overall prediction outcome at the patient level. The risk factors that most importantly contributed to the stay after surgery were as follows: a direct bilirubin laboratory test, department change, calcium chloride medication, gender, and diagnosis with the removal of other organs. Our results suggest that healthcare providers take into account the risk factors such as the laboratory blood test, distributing patients, and the medication prescribed prior to the surgery. We successfully predicted the length of stay after surgery and provide explainable models with supporting analyses. In summary, we demonstrate the interpretation with the XGBoost model presenting insights on preoperative features and defining higher risk predictors to the length of stay outcome. Our development in explainable models supports the current in-depth knowledge for the future length of stay prediction on electronic medical records that aids the decision-making and facilitation of the operation department.

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Machine learning using preoperative patient factors can predict duration of surgery and length of stay for total knee arthroplasty

Cited by 27 publications

References 37 publications

Machine Learning to Improve Resident Scheduling: Harnessing Artificial Intelligence to Enhance Resident Wellness

Machine Learning to Improve Resident Scheduling: Harnessing Artificial Intelligence to Enhance Resident Wellness

Length of Stay Prediction Model of Indoor Patients Based on Light Gradient Boosting Machine

Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients

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