scholarly journals Development and internal validation of machine learning algorithms to predict patient satisfaction after total hip arthroplasty

Arthroplasty ◽  
2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Siyuan Zhang ◽  
Jerry Yongqiang Chen ◽  
Hee Nee Pang ◽  
Ngai Nung Lo ◽  
Seng Jin Yeo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Patient Satisfaction ◽  
Total Hip Arthroplasty ◽  
Hip Arthroplasty ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Lumbar Spine Surgery ◽  
Discriminative Ability ◽  
Total Hip

Abstract Background Patient satisfaction is a unique and important measure of success after total hip arthroplasty (THA). Our study aimed to evaluate the use of machine learning (ML) algorithms to predict patient satisfaction after THA. Methods Prospectively collected data of 1508 primary THAs performed between 2006 and 2018 were extracted from our joint replacement registry and split into training (80%) and test (20%) sets. Supervised ML algorithms (Random Forest, Extreme Gradient Boosting, Support Vector Machines, Logistic LASSO) were developed with the training set, using patient demographics, comorbidities and preoperative patient reported outcome measures (PROMs) (Short Form-36 [SF-36], physical component summary [PCS] and mental component summary [MCS], Western Ontario and McMaster’s Universities Osteoarthritis Index [WOMAC] and Oxford Hip Score [OHS]) to predict patient satisfaction at 2 years postoperatively. Predictive performance was evaluated using the independent test set. Results Preoperative models demonstrated fair discriminative ability in predicting patient satisfaction, with the LASSO model achieving a maximum AUC of 0.76. Permutation importance revealed that the most important predictors of dissatisfaction were (1) patient’s age, (2) preoperative WOMAC, (3) number of comorbidities, (4) preoperative MCS, (5) previous lumbar spine surgery, and (6) low BMI (< 18.5). Conclusion Machine learning algorithms demonstrated fair discriminative ability in predicting patient satisfaction after THA. We have identified modifiable and non-modifiable predictors of postoperative satisfaction which could enhance preoperative counselling and improve health optimization prior to THA.

Machine learning approaches in predicting ambulatory same day discharge patients after total hip arthroplasty

2021 ◽  
pp. rapm-2021-102715
Author(s):  
Haoyan Zhong ◽  
Jashvant Poeran ◽  
Alex Gu ◽  
Lauren A Wilson ◽  
Alejandro Gonzalez Della Valle ◽  
...  
Keyword(s):  
Machine Learning ◽  
Total Hip Arthroplasty ◽  
Length Of Stay ◽  
Hip Arthroplasty ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Total Hip ◽  
Machine Learning Models

BackgroundWith continuing financial and regulatory pressures, practice of ambulatory total hip arthroplasty is increasing. However, studies focusing on selection of optimal candidates are burdened by limitations related to traditional statistical approaches. Hereby we aimed to apply machine learning algorithm to identify characteristics associated with optimal candidates.MethodsThis retrospective cohort study included elective total hip arthroplasty (n=63 859) recorded in National Surgical Quality Improvement Program dataset from 2017 to 2018. The main outcome was length of stay. A total of 40 candidate variables were considered. We applied machine learning algorithms (multivariable logistic regression, artificial neural networks, and random forest models) to predict length of stay=0 day. Models’ accuracies and area under the curve were calculated.ResultsApplying machine learning models to compare length of stay=0 day to length of stay=1–3 days cases, we found area under the curve of 0.715, 0.762, and 0.804, accuracy of 0.65, 0.73, and 0.81 for logistic regression, artificial neural networks, and random forest model, respectively. Regarding the most important predictive features, anesthesia type, body mass index, age, ethnicity, white blood cell count, sodium level, and alkaline phosphatase were highlighted in machine learning models.ConclusionsMachine learning algorithm exhibited acceptable model quality and accuracy. Machine learning algorithms highlighted the as yet unrecognized impact of laboratory testing on future patient ambulatory pathway assignment.

A Comparative Analysis of Patient Satisfaction following Total Knee Arthroplasty and Total Hip Arthroplasty Using the Forgotten Joint Score

2020 ◽  
Vol 04 (02) ◽  
pp. 084-089
Author(s):  
Vivek Singh ◽  
Stephen Zak ◽  
Ran Schwarzkopf ◽  
Roy Davidovitch
Keyword(s):  
Total Knee Arthroplasty ◽  
Patient Satisfaction ◽  
Total Hip Arthroplasty ◽  
Knee Arthroplasty ◽  
Hip Arthroplasty ◽  
Demographic Data ◽  
Demographic Differences ◽  
Total Hip ◽  
Forgotten Joint Score ◽  
Total Knee

AbstractMeasuring patient satisfaction and surgical outcomes following total joint arthroplasty remains controversial with most tools failing to account for both surgeon and patient satisfaction in regard to outcomes. The purpose of this study was to use “The Forgotten Joint Score” questionnaire to assess clinical outcomes comparing patients who underwent a total hip arthroplasty (THA) with those who underwent a total knee arthroplasty (TKA). We conducted a retrospective review of patients who underwent primary THA or TKA between September 2016 and September 2019 and responded to the Forgotten Joint Score-12 (FJS-12) questionnaire at least at one of three time periods (3, 12, and 21 months), postoperatively. An electronic patient rehabilitation application was used to administer the questionnaire. Collected variables included demographic data (age, gender, race, body mass index [BMI], and smoking status), length of stay (LOS), and FJS-12 scores. t-test and chi-square were used to determine significance. Linear regression was used to account for demographic differences. A p-value of less than 0.05 was considered statistically significant. Of the 2,359 patients included in this study, 1,469 underwent a THA and 890 underwent a TKA. Demographic differences were observed between the two groups with the TKA group being older, with higher BMI, higher American Society of Anesthesiologists scores, and longer LOS. Accounting for the differences in demographic data, THA patients consistently had higher scores at 3 months (53.72 vs. 24.96; p < 0.001), 12 months (66.00 vs. 43.57; p < 0.001), and 21 months (73.45 vs. 47.22; p < 0.001). FJS-12 scores for patients that underwent THA were significantly higher in comparison to TKA patients at 3, 12, and 21 months postoperatively. Increasing patient age led to a marginal increase in FJS-12 score in both cohorts. With higher FJS-12 scores, patients who underwent THA may experience a more positive evolution with their surgery postoperatively than those who had TKA.

scholarly journals Excellent Functional Outcome and Quality of Life after Primary Cementless Total Hip Arthroplasty (THA) Using an Enhanced Recovery Setup

2021 ◽  
Vol 10 (4) ◽  
pp. 621
Author(s):  
Franziska Leiss ◽  
Julia Sabrina Götz ◽  
Günther Maderbacher ◽  
Matthias Meyer ◽  
Jan Reinhard ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Patient Satisfaction ◽  
Total Hip Arthroplasty ◽  
Functional Outcome ◽  
Hip Arthroplasty ◽  
Enhanced Recovery ◽  
Harris Hip Score ◽  
Total Hip ◽  
Cementless Total Hip Arthroplasty

Background: Total hip arthroplasty combined with the concept of enhanced recovery is of continued worldwide interest, as it is reported to improve early functional outcome and treatment quality without increasing complications. The aim of the study was to investigate functional outcome and quality of life 4 weeks and 12 months after cementless total hip arthroplasty in combination with an enhanced recovery concept. Methods: A total of 109 patients underwent primary cementless Total Hip Arthroplasty (THA) in an enhanced recovery concept and were retrospectively analyzed. After 4 weeks and 12 months, clinical examination was analyzed regarding function, pain and satisfaction; results were evaluated using Harris Hip score, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), EQ-5D-5L, EQ-VAS and subjective patient-related outcome measures (PROMs). Preoperatively, HADS (Hospital Anxiety and Depression Scale) was collected. A correlation analysis of age, American Society of Anesthesiologists (ASA), HADS and comorbidities (diabetes mellitus, art. hypertension, cardiovascular disease) with WOMAC, Harris Hip score (HHS) and EQ-5D was performed. Results: Patients showed a significant improvement in Harris Hip score 4 weeks and 12 months postoperatively (p < 0.001). WOMAC total score, subscale pain, subscale stiffness and subscale function improved significantly from preoperative to 12 months postoperative (p < 0.001). EQ-5D showed a significant improvement preoperative to postoperative (p < 0.001). The influence of anxiety or depression (HADS-A or HADS-D) on functional outcome could not be determined. There was a high patient satisfaction postoperatively, and almost 100% of patients would choose enhanced recovery surgery again. Conclusion: Cementless THA with the concept of enhanced recovery improves early clinical function and quality of life. PROMs showed a continuous improvement over a follow-up of 12 months after surgery. PROMs can help patients and surgeons to modify expectations and improve patient satisfaction.

scholarly journals Hierarchical Tactile Sensation Integration from Prosthetic Fingertips Enables Multi-Texture Surface Recognition

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4324
Author(s):  
Moaed A. Abd ◽  
Rudy Paul ◽  
Aparna Aravelli ◽  
Ou Bai ◽  
Leonel Lagos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Sliding Contact ◽  
Tactile Sensation ◽  
Support Vector ◽  
Textured Surfaces ◽  
K Nearest Neighbor ◽  
Tactile Sensors ◽  
Time Frequency

Multifunctional flexible tactile sensors could be useful to improve the control of prosthetic hands. To that end, highly stretchable liquid metal tactile sensors (LMS) were designed, manufactured via photolithography, and incorporated into the fingertips of a prosthetic hand. Three novel contributions were made with the LMS. First, individual fingertips were used to distinguish between different speeds of sliding contact with different surfaces. Second, differences in surface textures were reliably detected during sliding contact. Third, the capacity for hierarchical tactile sensor integration was demonstrated by using four LMS signals simultaneously to distinguish between ten complex multi-textured surfaces. Four different machine learning algorithms were compared for their successful classification capabilities: K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and neural network (NN). The time-frequency features of the LMSs were extracted to train and test the machine learning algorithms. The NN generally performed the best at the speed and texture detection with a single finger and had a 99.2 ± 0.8% accuracy to distinguish between ten different multi-textured surfaces using four LMSs from four fingers simultaneously. The capability for hierarchical multi-finger tactile sensation integration could be useful to provide a higher level of intelligence for artificial hands.

Patient Satisfaction After Total Hip Arthroplasty Is Not Influenced by Reductions in Opioid Prescribing

2021 ◽  
Author(s):  
David A. Bloom ◽  
Amit K. Manjunath ◽  
Anthony P. Gualtieri ◽  
Jordan W. Fried ◽  
Ran M. Schwarzkopf ◽  
...  
Keyword(s):  
Patient Satisfaction ◽  
Total Hip Arthroplasty ◽  
Hip Arthroplasty ◽  
Total Hip ◽  
Opioid Prescribing

Machine Learning Models for Finger Bend Evaluation using Implemented Low cost Flex Sensor

2021 ◽  
Vol 9 (VI) ◽  
pp. 3605-3611
Author(s):  
Pratyush Kaware
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Low Cost ◽  
Learning Algorithms ◽  
Cost Effective ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Models

In this paper a cost-effective sensor has been implemented to read finger bend signals, by attaching the sensor to a finger, so as to classify them based on the degree of bent as well as the joint about which the finger was being bent. This was done by testing with various machine learning algorithms to get the most accurate and consistent classifier. Finally, we found that Support Vector Machine was the best algorithm suited to classify our data, using we were able predict live state of a finger, i.e., the degree of bent and the joints involved. The live voltage values from the sensor were transmitted using a NodeMCU micro-controller which were converted to digital and uploaded on a database for analysis.

scholarly journals Heart disease prediction using machine learning techniques : a survey

2018 ◽  
Vol 7 (2.8) ◽  
pp. 684 ◽  
Author(s):  
V V. Ramalingam ◽  
Ayantan Dandapath ◽  
M Karthik Raja
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Complex Data ◽  
Learning Techniques ◽  
Vector Machines ◽  
Supervised Learning Algorithms ◽  
Life Threatening

Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques andanalyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.

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