scholarly journals Comparing Precision Machine Learning With Consumer, Quality, and Volume Metrics for Ranking Orthopedic Surgery Hospitals: Retrospective Study (Preprint)

2020 ◽  
Author(s):  
Dev Goyal ◽  
John Guttag ◽  
Zeeshan Syed ◽  
Rudra Mehta ◽  
Zahoor Elahi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Metropolitan Area ◽  
Hip Replacement ◽  
Cost Of Care ◽  
Emergency Department Visits ◽  
Perioperative Outcomes ◽  
Patient Specific ◽  
Precision Machine ◽  
Consumer Ratings ◽  
Hip Replacements

BACKGROUND Patients’ choices of providers when undergoing elective surgeries significantly impact both perioperative outcomes and costs. There exist a variety of approaches that are available to patients for evaluating between different hospital choices. OBJECTIVE This paper aims to compare differences in outcomes and costs between hospitals ranked using popular internet-based consumer ratings, quality stars, reputation rankings, average volumes, average outcomes, and precision machine learning–based rankings for hospital settings performing hip replacements in a large metropolitan area. METHODS Retrospective data from 4192 hip replacement surgeries among Medicare beneficiaries in 2018 in a the Chicago metropolitan area were analyzed for variations in outcomes (90-day postprocedure hospitalizations and emergency department visits) and costs (90-day total cost of care) between hospitals ranked through multiple approaches: internet-based consumer ratings, quality stars, reputation rankings, average yearly surgical volume, average outcome rates, and machine learning–based rankings. The average rates of outcomes and costs were compared between the patients who underwent surgery at a hospital using each ranking approach in unadjusted and propensity-based adjusted comparisons. RESULTS Only a minority of patients (1159/4192, 27.6% to 2078/4192, 49.6%) were found to be matched to higher-ranked hospitals for each of the different approaches. Of the approaches considered, hip replacements at hospitals that were more highly ranked by consumer ratings, quality stars, and machine learning were all consistently associated with improvements in outcomes and costs in both adjusted and unadjusted analyses. The improvement was greatest across all metrics and analyses for machine learning–based rankings. CONCLUSIONS There may be a substantive opportunity to increase the number of patients matched to appropriate hospitals across a broad variety of ranking approaches. Elective hip replacement surgeries performed at hospitals where patients were matched based on patient-specific machine learning were associated with better outcomes and lower total costs of care.

scholarly journals Comparing Precision Machine Learning With Consumer, Quality, and Volume Metrics for Ranking Orthopedic Surgery Hospitals: Retrospective Study

10.2196/22765 ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. e22765
Author(s):  
Dev Goyal ◽  
John Guttag ◽  
Zeeshan Syed ◽  
Rudra Mehta ◽  
Zahoor Elahi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Metropolitan Area ◽  
Hip Replacement ◽  
Cost Of Care ◽  
Emergency Department Visits ◽  
Perioperative Outcomes ◽  
Patient Specific ◽  
Precision Machine ◽  
Consumer Ratings ◽  
Hip Replacements

Background Patients’ choices of providers when undergoing elective surgeries significantly impact both perioperative outcomes and costs. There exist a variety of approaches that are available to patients for evaluating between different hospital choices. Objective This paper aims to compare differences in outcomes and costs between hospitals ranked using popular internet-based consumer ratings, quality stars, reputation rankings, average volumes, average outcomes, and precision machine learning–based rankings for hospital settings performing hip replacements in a large metropolitan area. Methods Retrospective data from 4192 hip replacement surgeries among Medicare beneficiaries in 2018 in a the Chicago metropolitan area were analyzed for variations in outcomes (90-day postprocedure hospitalizations and emergency department visits) and costs (90-day total cost of care) between hospitals ranked through multiple approaches: internet-based consumer ratings, quality stars, reputation rankings, average yearly surgical volume, average outcome rates, and machine learning–based rankings. The average rates of outcomes and costs were compared between the patients who underwent surgery at a hospital using each ranking approach in unadjusted and propensity-based adjusted comparisons. Results Only a minority of patients (1159/4192, 27.6% to 2078/4192, 49.6%) were found to be matched to higher-ranked hospitals for each of the different approaches. Of the approaches considered, hip replacements at hospitals that were more highly ranked by consumer ratings, quality stars, and machine learning were all consistently associated with improvements in outcomes and costs in both adjusted and unadjusted analyses. The improvement was greatest across all metrics and analyses for machine learning–based rankings. Conclusions There may be a substantive opportunity to increase the number of patients matched to appropriate hospitals across a broad variety of ranking approaches. Elective hip replacement surgeries performed at hospitals where patients were matched based on patient-specific machine learning were associated with better outcomes and lower total costs of care.

Abstract 13218: Machine Learning Can Identify Cardiology Patients With High Future Healthcare Utilization in a Large Regional Health System

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Linyuan Jing ◽  
Rameswara S Challa ◽  
Alvaro Ulloa ◽  
William C Cauthorn ◽  
Dustin Hartzel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Healthcare Utilization ◽  
Vital Signs ◽  
Poor Performance ◽  
Cost Of Care ◽  
Emergency Department Visits ◽  
Predictive Values ◽  
Healthcare Facilities ◽  
Home Based ◽  
Future Utilization

Introduction: Healthcare cost has increased drastically in the last decade, and over 50% of the cost can be attributed to a small portion (5-10%) of the population. Certain clinical programs, such as home-based care, aim to reduce this utilization but need methods to identify the most appropriate patients to enroll. We hypothesized that machine learning can predict patients with high future utilization with good accuracy. Methods: 683,160 cardiology patients (defined broadly as those with an ECG, echocardiogram or cardiology visit) with ~17 million clinical episodes since 2004 were identified from Geisinger’s electronic health records. Utilization was estimated as total cost of care for outpatient, inpatient and emergency department visits. Patients with the highest 10% utilization in a given year were defined as high utilizers. Machine learning models were used to predict high utilization over the next 3, 6 and 12 months. Input variables (n=191) included age, sex, smoking, 5 vital signs, 21 labs, 18 medications (current and past), 40 ECG and 44 echocardiographic measurements, 43 comorbidities, 7 time / cyclical features, 6 past utilization metrics and 4 social metrics (e.g. distance to healthcare facilities). Results: XGBoost achieved the best performance with areas under the ROC curve (AUC) of 0.82, 0.81 and 0.78 for 3, 6, 12-month models, and average precision scores (AP) of 0.31, 0.36 and 0.37, respectively, while the commonly used Charlson Comorbidity Index had poor performance with AUCs of 0.63 - 0.64 and APs of 0.1 - 0.17. Past utilization was the best predictor of future utilization. Targeting patients with the top 5 and 10% highest risk for utilization achieved sensitivities of 26 and 40% and positive predictive values of 50 and 38% (12-month model, Figure). Conclusions: Machine learning can be used to predict which patients will have high future healthcare utilization. This may help target populations for intervention programs aimed at reducing utilization.

Patient-specific IMRT QA verification using machine learning and gamma radiomics

2021 ◽  
Vol 82 ◽  
pp. 100-108
Author(s):  
Jéssica Caroline Lizar ◽  
Carolina Cariolatto Yaly ◽  
Alexandre Colello Bruno ◽  
Gustavo Arruda Viani ◽  
Juliana Fernandes Pavoni
Keyword(s):  
Machine Learning ◽  
Patient Specific ◽  
Imrt Qa

scholarly journals Patient-Specific Predictive Antibiogram in Decision Support for Empiric Antibiotic Treatment

2020 ◽  
Vol 41 (S1) ◽  
pp. s521-s522
Author(s):  
Debarka Sengupta ◽  
Vaibhav Singh ◽  
Seema Singh ◽  
Dinesh Tewari ◽  
Mudit Kapoor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Antimicrobial Resistance ◽  
Model Building ◽  
Medical Center ◽  
Bacterial Species ◽  
Model Performance ◽  
The United States ◽  
Patient Specific ◽  
Gradient Boosting ◽  
Comparative Performance

Background: The rising trend of antibiotic resistance imposes a heavy burden on healthcare both clinically and economically (US$55 billion), with 23,000 estimated annual deaths in the United States as well as increased length of stay and morbidity. Machine-learning–based methods have, of late, been used for leveraging patient’s clinical history and demographic information to predict antimicrobial resistance. We developed a machine-learning model ensemble that maximizes the accuracy of such a drug-sensitivity versus resistivity classification system compared to the existing best-practice methods. Methods: We first performed a comprehensive analysis of the association between infecting bacterial species and patient factors, including patient demographics, comorbidities, and certain healthcare-specific features. We leveraged the predictable nature of these complex associations to infer patient-specific antibiotic sensitivities. Various base-learners, including k-NN (k-nearest neighbors) and gradient boosting machine (GBM), were used to train an ensemble model for confident prediction of antimicrobial susceptibilities. Base learner selection and model performance evaluation was performed carefully using a variety of standard metrics, namely accuracy, precision, recall, F1 score, and Cohen κ. Results: For validating the performance on MIMIC-III database harboring deidentified clinical data of 53,423 distinct patient admissions between 2001 and 2012, in the intensive care units (ICUs) of the Beth Israel Deaconess Medical Center in Boston, Massachusetts. From ~11,000 positive cultures, we used 4 major specimen types namely urine, sputum, blood, and pus swab for evaluation of the model performance. Figure 1 shows the receiver operating characteristic (ROC) curves obtained for bloodstream infection cases upon model building and prediction on 70:30 split of the data. We received area under the curve (AUC) values of 0.88, 0.92, 0.92, and 0.94 for urine, sputum, blood, and pus swab samples, respectively. Figure 2 shows the comparative performance of our proposed method as well as some off-the-shelf classification algorithms. Conclusions: Highly accurate, patient-specific predictive antibiogram (PSPA) data can aid clinicians significantly in antibiotic recommendation in ICU, thereby accelerating patient recovery and curbing antimicrobial resistance.Funding: This study was supported by Circle of Life Healthcare Pvt. Ltd.Disclosures: None

scholarly journals An MRI-Based Patient-Specific Computational Framework for the Calculation of Range of Motion of Total Hip Replacements

2021 ◽  
Vol 11 (6) ◽  
pp. 2852
Author(s):  
Maeruan Kebbach ◽  
Christian Schulze ◽  
Christian Meyenburg ◽  
Daniel Kluess ◽  
Mevluet Sungu ◽  
...  
Keyword(s):  
Range Of Motion ◽  
Internal Rotation ◽  
Computer Aided Design ◽  
External Rotation ◽  
Patient Specific ◽  
Computational Framework ◽  
Total Hip ◽  
Total Hip Replacements ◽  
Significant Difference ◽  
Hip Replacements

The calculation of range of motion (ROM) is a key factor during preoperative planning of total hip replacements (THR), to reduce the risk of impingement and dislocation of the artificial hip joint. To support the preoperative assessment of THR, a magnetic resonance imaging (MRI)-based computational framework was generated; this enabled the estimation of patient-specific ROM and type of impingement (bone-to-bone, implant-to-bone, and implant-to-implant) postoperatively, using a three-dimensional computer-aided design (CAD) to visualize typical clinical joint movements. Hence, patient-specific CAD models from 19 patients were generated from MRI scans and a conventional total hip system (Bicontact® hip stem and Plasmacup® SC acetabular cup with a ceramic-on-ceramic bearing) was implanted virtually. As a verification of the framework, the ROM was compared between preoperatively planned and the postoperatively reconstructed situations; this was derived based on postoperative radiographs (n = 6 patients) during different clinically relevant movements. The data analysis revealed there was no significant difference between preoperatively planned and postoperatively reconstructed ROM (∆ROM) of maximum flexion (∆ROM = 0°, p = 0.854) and internal rotation (∆ROM = 1.8°, p = 0.917). Contrarily, minor differences were observed for the ROM during maximum external rotation (∆ROM = 9°, p = 0.046). Impingement, of all three types, was in good agreement with the preoperatively planned and postoperatively reconstructed scenarios during all movements. The calculated ROM reached physiological levels during flexion and internal rotation movement; however, it exceeded physiological levels during external rotation. Patients, where implant-to-implant impingement was detected, reached higher ROMs than patients with bone-to-bone impingement. The proposed framework provides the capability to predict postoperative ROM of THRs.

scholarly journals Multi-Drug Featurization and Deep Learning Improve Patient-Specific Predictions of Adverse Events

2021 ◽  
Vol 18 (5) ◽  
pp. 2600
Author(s):  
Ioannis N. Anastopoulos ◽  
Chloe K. Herczeg ◽  
Kasey N. Davis ◽  
Atray C. Dixit
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Variable Number ◽  
Patient Specific ◽  
Real World Evidence ◽  
Genetic Features ◽  
The Uk ◽  
Therapeutic Compounds ◽  
Improve Patient ◽  
Reaction Profile

While the clinical approval process is able to filter out medications whose utility does not offset their adverse drug reaction profile in humans, it is not well suited to characterizing lower frequency issues and idiosyncratic multi-drug interactions that can happen in real world diverse patient populations. With a growing abundance of real-world evidence databases containing hundreds of thousands of patient records, it is now feasible to build machine learning models that incorporate individual patient information to provide personalized adverse event predictions. In this study, we build models that integrate patient specific demographic, clinical, and genetic features (when available) with drug structure to predict adverse drug reactions. We develop an extensible graph convolutional approach to be able to integrate molecular effects from the variable number of medications a typical patient may be taking. Our model outperforms standard machine learning methods at the tasks of predicting hospitalization and death in the UK Biobank dataset yielding an R2 of 0.37 and an AUC of 0.90, respectively. We believe our model has potential for evaluating new therapeutic compounds for individualized toxicities in real world diverse populations. It can also be used to prioritize medications when there are multiple options being considered for treatment.

scholarly journals Machine Learning Approach to Automated Quality Identification of Human Induced Pluripotent Stem Cell Colony Images

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Henry Joutsijoki ◽  
Markus Haponen ◽  
Jyrki Rasku ◽  
Katriina Aalto-Setälä ◽  
Martti Juhola
Keyword(s):  
Machine Learning ◽  
Stem Cell ◽  
Pluripotent Stem Cell ◽  
Disease Modeling ◽  
Induced Pluripotent Stem Cell ◽  
Patient Specific ◽  
Support Vector ◽  
Ips Cell ◽  
Cell Technology ◽  
Induced Pluripotent

The focus of this research is on automated identification of the quality of human induced pluripotent stem cell (iPSC) colony images. iPS cell technology is a contemporary method by which the patient’s cells are reprogrammed back to stem cells and are differentiated to any cell type wanted. iPS cell technology will be used in future to patient specific drug screening, disease modeling, and tissue repairing, for instance. However, there are technical challenges before iPS cell technology can be used in practice and one of them is quality control of growing iPSC colonies which is currently done manually but is unfeasible solution in large-scale cultures. The monitoring problem returns to image analysis and classification problem. In this paper, we tackle this problem using machine learning methods such as multiclass Support Vector Machines and several baseline methods together with Scaled Invariant Feature Transformation based features. We perform over 80 test arrangements and do a thorough parameter value search. The best accuracy (62.4%) for classification was obtained by using ak-NN classifier showing improved accuracy compared to earlier studies.

Complications with the Zurich Canine Total Hip Replacement System in an Initial Series of Cases Performed by a Single Surgeon

2021 ◽  
Author(s):  
Samuel P. Franklin ◽  
Nathan A. Miller ◽  
Todd Riecks
Keyword(s):  
Total Hip Replacement ◽  
Hip Replacement ◽  
Weight Bearing ◽  
Normal Function ◽  
High Rate ◽  
Total Hip ◽  
Initial Series ◽  
Major Complications ◽  
Hip Replacements ◽  
Replacement System

Abstract Objective The aim of this study was to quantify the complications using the Zurich total hip replacement system in an initial series of cases performed by a single surgeon who had experience with other total hip replacement systems. Materials and Methods This was a retrospective study in which complications were classified as major if any treatment was needed or if the outcome was less than near-normal function. Complications that did not warrant treatment and that did not result in function that was inferior to near-normal were considered minor. Outcomes were assessed by radiographic review, physical examination, subjective gait evaluation or, in one case, by objective gait analysis. Bilateral total hip replacements were considered separate procedures. Results The first 21 procedures in 19 dogs performed by a single surgeon were included. The mean time to follow-up was 48 weeks (range: 8–120 weeks; standard deviation: 36 weeks). Two cases (of 21) experienced major complications including one dog with excess internal femoral rotation during weight bearing and one dog having luxation. One case (of 21) had a minor complication; femoral fracture in the presence of an intact bone plate that maintained alignment and healed without treatment. Clinical Significance A high rate of successful outcomes with few major complications can be obtained in the initial cases treated using the Zurich total hip replacement system for surgeons with prior experience with other total hip replacement systems.

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