Prediction of Bedridden Duration of Hospitalized Patients by Machine Learning Based on EMRs at Admission

Abstract

Being bedridden is a frequent comorbid condition that leads to a series of complications in clinical practice. The present study aimed to predict bedridden duration of hospitalized patients based on EMR at admission by machine learning. The medical data of 4345 hospitalized patients who were bedridden for at least 24 hours after admission were retrospectively collected. After preprocessing of the data, features for modeling were selected by support vector machine recursive feature elimination. Thereafter, logistic regression, support vector machine, and extreme gradient boosting algorithms were adopted to predict the bedridden duration. The feasibility and efficacy of above models were evaluated by performance indicators. Our results demonstrated that the most important features related to bedridden duration were Charlson Comorbidity Index, age, bedridden duration before admission, mobility capability, and perceptual ability. The extreme gradient boosting algorithm showed the best performance (accuracy, 0.797; area under the curve, 0.841) when compared with support vector machine (accuracy, 0.771; area under the curve, 0.803) and logistic regression (accuracy, 0.765; area under the curve, 0.809) algorithms. Meanwhile, the extreme gradient boosting algorithm had a higher sensitivity (0.856), specificity (0.650), and F1 score (0.858) than that of support vector machine algorithm (0.843, 0.589, and 0.841) and logistic regression (0.852, 0.545, and 0.839), respectively. These findings indicate that machine learning based on EMRs at admission is a feasible avenue to predict the bedridden duration. The extreme gradient boosting algorithm shows great potential for further clinical application.

Rapid Estimation of Contact Stresses in Imageless Total Knee Arthroplasty

“…Motivated by the limitations, this work presents a robust and rapid contact stress estimation technique using Latin hypercube sampling (LHS) and the machine learning regression model. To create a robust model, a large number of input values, such as weight, alignment status, and more, are sampled through LHS because it provides good distribution characteristics and high computation efficiency [38,39]. LHS is an advanced approach to Monte Carlo simulation, which divides the range of input variables to have the same intervals and samples value from it.…”

“…Mathematics 2023, 11, x FOR PEER REVIEW 3 of 16 weight, alignment status, and more, are sampled through LHS because it provides good distribution characteristics and high computation efficiency [38,39]. LHS is an advanced approach to Monte Carlo simulation, which divides the range of input variables to have the same intervals and samples value from it.…”

Long-Term Risk of Being Bedridden in Elderly Patients Who Underwent Oncologic Surgery: A Retrospective Study Using a Japanese Claims Database

“… 7 , 8 The impaired activity would result in poor quality of life; moreover, if a patient becomes bedridden, the condition is accompanied by several complications, such as bedsores, deep venous thrombosis, pneumonia, and urinary tract infections, deteriorating the optimal allocation of medical resources. 9 – 12 Because poor functional capacity will have a negative impact on survival, maintenance of function and independence has become one of the major principles of cancer management in the elderly. 13 Furthermore, many elderly patients prioritize functional prognosis over survival prognosis; that is, they do not prefer undergoing surgery to prolong overall survival if the surgery worsens the activities of daily living.…”