Using Machine Learning to Predict Rehabilitation Outcomes in Postacute Hip Fracture Patients

Shtar, Guy; Rokach, Lior; Shapira, Bracha; Nissan, Ran; Hershkovitz, Avital

doi:10.1016/j.apmr.2020.08.011

Cited by 25 publications

(14 citation statements)

References 26 publications

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“…The multi-dimensional operation and maintenance status perception layer of medical equipment adopts sensor cluster collection. This provides the underlying data for fault awareness [ 2 ]. Unstructured medical equipment multi-source heterogeneous fault data are unstructured for the underlying data.…”

Section: Model Architecture Designmentioning

confidence: 99%

Sports Rehabilitation Treatment of Medical Information in Tertiary Hospitals Based on Computer Machine Learning

Zhang

2022

Computational Intelligence and Neuroscience

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Objective. The processing and analysis of medical rehabilitation information data in tertiary hospitals is a hot research topic. Combining medical data analysis with machine learning algorithms to improve data mining efficiency is a problem that needs to be solved at present. This paper proposes an autonomous perception model of sports medicine rehabilitation equipment based on a deep learning algorithm for sports medical rehabilitation data. Methods. This paper cites a deep learning multi-dimensional perception model for medical rehabilitation equipment autonomous perception. The model utilizes the automatic overhaul of medical rehabilitation equipment based on deep belief networks. This paper extracts features through a multi-layer neural network and obtains fault location results of medical rehabilitation equipment through softmax. Results. In similarity prediction, the accuracy rate of the first three kinds of feedback containing the target answer is 77%. The accuracy rate of the target answers included in the top five kinds of feedback was 92%. Conclusion. In this study, it is feasible to apply deep learning to the quality control information system of sports rehabilitation medical equipment. This improves the management efficiency of medical rehabilitation equipment to a certain extent.

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Section: Model Architecture Designmentioning

confidence: 99%

Sports Rehabilitation Treatment of Medical Information in Tertiary Hospitals Based on Computer Machine Learning

Zhang

2022

Computational Intelligence and Neuroscience

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“…Of 39 studies that met all criteria and were included in this analysis, 18 studies (46.2%) used AI models to diagnose hip fractures on plain radiographs and 21 studies (53.8%) used AI models to predict patient outcomes following hip fracture surgery. A PRISMA flowchart of included studies is displayed in eFigure 1 in Supplement 1.…”

Section: Resultsmentioning

confidence: 99%

“…Mortality followed by length of hospital stay were the most commonly predicted outcomes, with other predicted outcomes of 30-day complications, living situation, postoperative delirium, and modified functional independence measure. 47,58,61 A pooled total of 714 939 hip fractures were used for training, validating, and testing ML models specific to postoperative outcome prediction. All databases used for outcome prediction are listed in eTable 2 in Supplement 1.…”

Section: Study Selectionmentioning

confidence: 99%

“…Included Studies on Application of Artificial Intelligence for Diagnosis of Hip FracturesArtificial Intelligence for Hip Fracture Detection and Outcome Prediction Age (18 of 21 studies47,[49][50][51][52][54][55][56][57][58][59][60][61][63][64][65][66][67] [85.7%]) and sex (17 of 21 studies [80.9%][47][48][49][50][51][52][54][55][56][57][58]60,61,[64][65][66][67] …”

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confidence: 99%

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Artificial Intelligence for Hip Fracture Detection and Outcome Prediction

et al. 2023

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ImportanceArtificial intelligence (AI) enables powerful models for establishment of clinical diagnostic and prognostic tools for hip fractures; however the performance and potential impact of these newly developed algorithms are currently unknown.ObjectiveTo evaluate the performance of AI algorithms designed to diagnose hip fractures on radiographs and predict postoperative clinical outcomes following hip fracture surgery relative to current practices.Data SourcesA systematic review of the literature was performed using the MEDLINE, Embase, and Cochrane Library databases for all articles published from database inception to January 23, 2023. A manual reference search of included articles was also undertaken to identify any additional relevant articles.Study SelectionStudies developing machine learning (ML) models for the diagnosis of hip fractures from hip or pelvic radiographs or to predict any postoperative patient outcome following hip fracture surgery were included.Data Extraction and SynthesisThis study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses and was registered with PROSPERO. Eligible full-text articles were evaluated and relevant data extracted independently using a template data extraction form. For studies that predicted postoperative outcomes, the performance of traditional predictive statistical models, either multivariable logistic or linear regression, was recorded and compared with the performance of the best ML model on the same out-of-sample data set.Main Outcomes and MeasuresDiagnostic accuracy of AI models was compared with the diagnostic accuracy of expert clinicians using odds ratios (ORs) with 95% CIs. Areas under the curve for postoperative outcome prediction between traditional statistical models (multivariable linear or logistic regression) and ML models were compared.ResultsOf 39 studies that met all criteria and were included in this analysis, 18 (46.2%) used AI models to diagnose hip fractures on plain radiographs and 21 (53.8%) used AI models to predict patient outcomes following hip fracture surgery. A total of 39 598 plain radiographs and 714 939 hip fractures were used for training, validating, and testing ML models specific to diagnosis and postoperative outcome prediction, respectively. Mortality and length of hospital stay were the most predicted outcomes. On pooled data analysis, compared with clinicians, the OR for diagnostic error of ML models was 0.79 (95% CI, 0.48-1.31; P = .36; I2 = 60%) for hip fracture radiographs. For the ML models, the mean (SD) sensitivity was 89.3% (8.5%), specificity was 87.5% (9.9%), and F1 score was 0.90 (0.06). The mean area under the curve for mortality prediction was 0.84 with ML models compared with 0.79 for alternative controls (P = .09).Conclusions and RelevanceThe findings of this systematic review and meta-analysis suggest that the potential applications of AI to aid with diagnosis from hip radiographs are promising. The performance of AI in diagnosing hip fractures was comparable with that of expert radiologists and surgeons. However, current implementations of AI for outcome prediction do not seem to provide substantial benefit over traditional multivariable predictive statistics.

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“…Extra trees classifier, which is also a ML algorithm, works by generating a large number of unpruned decision trees using the training data set. Extra trees classifier has a collection of trees and adds randomization and optimization while building trees (Shtar et al , 2021). The predictions are made by taking the average of the predictions made in decision trees in the regression mode and using the majority voting if run in the classification mode.…”

Section: Literature Reviewmentioning

confidence: 99%

The role of predictive analytics to explain the employability of management graduates

Raman

Pramod

2021

BIJ

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PurposeIn India, one of the prime focuses of a post-graduate management program is to prepare students and make them job-ready. Masters in Business Management (MBA) program helps students to imbibe theoretical and practical skills which are required by the industry, which can make them hit the ground running from the day they start their career. Many students (almost 40–50%) get pre-placement offers based on their performance in summer internship. The selection for summer interns by the corporate happens within a few months of the student joining the MBA program. Signaling theory in education indicates that the level of productivity of an individual is independent of education, but the educational qualification acts as a testimony for higher ability. However, this theory does not explain the reason for the mismatch between “education and work” or “education and the disparity in salary” between individuals who earn differently but have the same qualification. The paper aims to explore three attributes namely – “employability”– the chance of being employable; “pre-placement offers” – the chance of securing a job offer based on the performance in internship and “salary” – the chance of bagging a good job offer with a high salary.Design/methodology/approachThe authors have used longitudinal data consisting of 1,202 students who graduated from reputable business schools (B-Schools) in India. In the study, the authors have used predictive analytics on six years data set that have been gathered. The authors have considered 24 attributes including educational background at the graduate level (BE, B Tech, B Com, BSc, BBA and others), score secured in class ten (high, medium and low), score secured in class twelve (high, medium and low), score secured in graduation (high, medium and low), competency in soft skills (high, medium and low), participation in co-curricular activities (high, medium and low) and social engagement status (high, medium and low).FindingsThe findings of the study contradict the signaling theory in education. The findings suggest that the educational qualification alone cannot be the predictor of the employability and the salary offered to the student. The authors note that the better performance at a lower level of qualification (class 12) is the strong predictor in comparison to the student performance at their graduation and post-graduation level. The authors further observed at the post-graduate management education level that soft skills and participation in co-curricular activities are the major deciding factors to predict employability and pre-placement job opportunity and marks secured in class 12 is one more factor that gets added to this list to predict salary. The paper can immensely help management graduates to focus on key aspects that can help to hone appropriate skills and also can help management institutions to select the right students for management programs.Research limitations/implicationsThe analysis and the predictive model may apply to Indian B-Schools wherein the quality of students are almost the same or better. Predictive analytics has been used to explain the employability of management graduates alone and not any other.Practical implicationsThe authors' study might be useful for those students who often fail to understand “what” skills are the most important predictors of their performance in the pre-placement and final-placement interviews. Moreover, the study may serve as a useful guide to those organizations that often face dilemmas to understand “how” to select an ideal candidate for the particular job profile from a campus.Originality/valueThe authors believe that the current study is one of the few studies that have attempted to examine the employability of management graduates using predictive analytics. The study further contradicts that the signaling theory in education does not help better explain the employability of the students in extremely high-paced business environments.

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Using Machine Learning to Predict Rehabilitation Outcomes in Postacute Hip Fracture Patients

Cited by 25 publications

References 26 publications

Sports Rehabilitation Treatment of Medical Information in Tertiary Hospitals Based on Computer Machine Learning

Sports Rehabilitation Treatment of Medical Information in Tertiary Hospitals Based on Computer Machine Learning

Artificial Intelligence for Hip Fracture Detection and Outcome Prediction

The role of predictive analytics to explain the employability of management graduates

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