A machine learning-based predictive model of causality in orthopaedic medical malpractice cases in China
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ABSTRACT:
SUBMITTER: Yang Q
PROVIDER: S-EPMC11023448 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:ObjectiveThis study aimed to analyze the characteristics of surgical litigation cases and the risk factors that contribute to catastrophic compensation.MethodsWe downloaded and retrieved all cases related to surgical litigation cases from the China Jufaanli Database between 2008 and 2023. Multivariate logistic regression analysis was employed to identify independent risk factors that may contribute to catastrophic compensation.ResultsThis study included a total of 8,331 successfully resolved surgical litigation cases. Of these, 5,114 hospitals were defendants, with 25.34% of them involved in two or more lawsuits, thereby categorized as "repeat defendants." The total compensation amount was $269,163,545, with the highest compensation reaching $540,008. Most surgical litigation cases were concentrated in the eastern regions of China, with tertiary hospitals being the most frequently involved. The most common type of injury outcome was patient death. Compensation amounts and high compensation rates for severe disability exceeded those for death (p < 0.05). Independent risk factors associated with catastrophic compensation in surgical medical liability disputes included: Eastern region (OR = 1.462, 95% CI 1.038-2.060), secondary liability (OR = 2.457, 95% CI 1.633-3.696), main liability (OR = 9.353, 95% CI 6.195-14.121), major or full liability (OR = 10.878, 95% CI 7.152-16.546), severe disability (OR = 24.605, 95% CI 3.395-178.337), neurosurgery (OR = 3.488, 95% CI 2.265-5.373), thoracic surgery (OR = 1.810, 95% CI 1.017-3.219), general surgery (OR = 2.465, 95% CI 1.593-3.816), hepatobiliary surgery (OR = 3.251, 95% CI 1.980-5.338), gastrointestinal surgery (OR = 2.260, 95% CI 1.391-3.671), cardiovascular surgery (OR = 2.544, 95% CI 1.367-4.733), vascular surgery (OR = 2.916, 95% CI 1.246-6.827), and spinal surgery (OR = 2.921, 95% CI 1.763-4.841).ConclusionThis study analyzes the characteristics of surgical medical malpractice disputes in China from multiple perspectives and identifies independent risk factors for catastrophic compensation in surgical malpractice litigation. Our research has the potential to aid medical institutions in preventing and reducing surgical malpractice disputes, while also contributing to the provision of improved surgical care and nursing services for patients.
| S-EPMC11667895 | biostudies-literature
Project description:BackgroundThis study aimed to identify the most frequent reasons for orthopedic medical malpractice, gain insight into the related patient demographics and clinical characteristics, and identify the independent factors associated with it.MethodsWe collected and analyzed the demographic and injury characteristics, hospital levels and treatments, medical errors, and orthopedist's degree of responsibility for the patients who were subject to orthopedic medical malpractice at our institution. Univariate and multivariate analyses were performed to identify the factors associated with the orthopedist's degree of responsibility in the medical malpractice cases.ResultsWe included 1922 cases of medical malpractice in the final analysis. There were 1195 and 727 men and women, respectively (62.2% and 37.8%, respectively). Of the total patients, 1810, 1038, 1558, 1441, and 414 patients (94.2%, 54.0%, 81.1%, 75.0%, and 21.5%, respectively) were inpatients, had closed injuries, underwent surgery, were trauma cases, and had preoperative comorbidities, respectively. Most medical malpractice cases were in patients with fractures and spinal degenerative disease (1229 and 253 cases; 63.9% and 13.2%, respectively), and occurred in city-level hospitals (1006 cases, 52.3%), which were located in the eastern part of china (1001, 52.1%), including Jiangsu and Zhejiang (279 and 233 cases, 14.52% and 52.1%, respectively). Between 2016 and 2017, the orthopedist's degree of responsibility in medical malpractice claims were deemed as full, primary, equal, secondary, and minor in 135, 654, 77, 716, and 340 orthopedists (7.0%, 34.0%, 4.0%, 37.3%, and 17.7%). Most medical errors made by orthopedists in cases of medical malpractice were related to failure to supervise or monitor cases, improper performance of procedures, and failure to instruct or communicate with the patient (736, 716, and 423 cases; 38.3%, 37.3%, and 22.0%, respectively). The multivariate analysis found that patients with preoperative comorbidities, who sustained humerus injuries, who were aged ≥65 years, who were treated by doctors who failed to supervise or monitor them, and who were treated at the provincial and city level hospitals were more likely to claim that the orthopedist bore a serious degree of responsibility in the medical malpractice case.ConclusionsOur results provide detailed information on the plaintiff demographics, clinical characteristics, and factors associated with medical malpractice. Medical malpractice is related to poor treatment outcomes. The first preventative measure that is required is a comprehensive improvement in the medical staff quality, mainly through medical ethics cultivation, and professional ability and technique training. Additionally, failure to supervise or monitor cases was the leading cause of medical malpractice and one of the factors that led to orthopedists bearing an equal and higher responsibility for medical malpractice. Orthopedists should improve patient supervision, especially when treating older patients and those with preoperative comorbidities and humerus injuries.
| S-EPMC8115811 | biostudies-literature
Project description:BackgroundPost-stroke dementia (PSD), a common complication, diminishes rehabilitation efficacy and affects disease prognosis in stroke patients. Many factors may be related to PSD, including demographic, comorbidities, and examination characteristics. However, most existing methods are qualitative evaluations of independent factors, which ignore the interaction amongst various factors. Therefore, the purpose of this study is to explore the applicability of machine learning (ML) methods for predicting PSD.Methods9 acceptable features were screened out by the Spearman correlation analysis and Boruta algorithm. We developed and evaluated 8 ML models: logistic regression, elastic net, k-nearest neighbors, decision tree, extreme gradient boosting, support vector machine, random forest, and multilayer perceptron.ResultsA total of 539 stroke patients were included in this study. Among the 8 models used to predict PSD, extreme gradient boosting and random forest showed the highest area under the curve (AUC) of the receiver operating characteristic curve (ROC), with values of 0.7287 and 0.7285, respectively. The most important features for predicting PSD included age, high sensitivity C-reactive protein, stroke side and location, and the occurrence of cerebral hemorrhage.ConclusionOur findings suggest that ML models, especially extreme gradient boosting, can best predict the risk of PSD.
| S-EPMC11555950 | biostudies-literature
Project description:ObjectiveAiming to investigate diabetic retinopathy (DR) risk factors and predictive models by machine learning using a large sample dataset.DesignRetrospective study based on a large sample and a high dimensional database.SettingA Chinese central tertiary hospital in Beijing.ParticipantsInformation on 32 452 inpatients with type-2 diabetes mellitus (T2DM) were retrieved from the electronic medical record system from 1 January 2013 to 31 December 2017.MethodsSixty variables (including demography information, physical and laboratory measurements, system diseases and insulin treatments) were retained for baseline analysis. The optimal 17 variables were selected by recursive feature elimination. The prediction model was built based on XGBoost algorithm, and it was compared with three other popular machine learning techniques: logistic regression, random forest and support vector machine. In order to explain the results of XGBoost model more visually, the Shapley Additive exPlanation (SHAP) method was used.ResultsDR occurred in 2038 (6.28%) T2DM patients. The XGBoost model was identified as the best prediction model with the highest AUC (area under the curve value, 0.90) and showed that an HbA1c value greater than 8%, nephropathy, a serum creatinine value greater than 100 µmol/L, insulin treatment and diabetic lower extremity arterial disease were associated with an increased risk of DR. A patient's age over 65 was associated with a decreased risk of DR.ConclusionsWith better comprehensive performance, XGBoost model had high reliability to assess risk indicators of DR. The most critical risk factors of DR and the cut-off of risk factors can be found by SHAP method to render the output of the XGBoost model clinically interpretable.
| S-EPMC8628336 | biostudies-literature
Project description:Metabolic syndrome (MetS) is a chronic disease caused by obesity, high blood pressure, high blood sugar, and dyslipidemia and may lead to cardiovascular disease or type 2 diabetes. Therefore, the detection and prevention of MetS at an early stage are imperative. Individuals can detect MetS early and manage it effectively if they can easily monitor their health status in their daily lives. In this study, a predictive model for MetS was developed utilizing solely noninvasive information, thereby facilitating its practical application in real-world scenarios. The model's construction deliberately excluded three features requiring blood testing, specifically those for triglycerides, blood sugar, and HDL cholesterol. We used a large-scale Korean health examination dataset (n = 70, 370; the prevalence of MetS = 13.6%) to develop the predictive model. To obtain informative features, we developed three novel synthetic features from four basic information: waist circumference, systolic and diastolic blood pressure, and gender. We tested several classification algorithms and confirmed that the decision tree model is the most appropriate for the practical prediction of MetS. The proposed model achieved good performance, with an AUC of 0.889, a recall of 0.855, and a specificity of 0.773. It uses only four base features, which results in simplicity and easy interpretability of the model. In addition, we performed calibrations on the prediction probability and calibrated the model. Therefore, the proposed model can provide MetS diagnosis and risk prediction results. We also proposed a MetS risk map such that individuals could easily determine whether they had metabolic syndrome.
| S-EPMC10237504 | biostudies-literature
Project description:This study aims to construct a prediction model for the demand for medical and daily care services of the elderly and to explore the factors that affect the demand for medical and daily care services of the elderly. In this study, a questionnaire survey on the demand for medical and daily care services of 1291 elderly was conducted using multi-stage stratified whole cluster random sampling. SPSS21.0 statistical analysis software was used to describe the basic data of the elderly statistically, and univariate analysis was used to screen variables for model construction and binary logistic regression analysis. The acquired dataset has class imbalance, and to handle this issue, Synthetic Minority Over Sampling Technique with TomekLink (SMOTE-TomekLink) was adopted to resample the dataset for class-balancing. To improve computational efficiency, we used three algorithms to develop prediction models, including Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Light Gradient Boosting Machine (LightGBM) algorithms. The performance of each model was measured, and the performance of the prediction model was obtained using the following performance metrics: accuracy (ACC), recall (R), precision (P), F1-score, and area under the receiver operating characteristic (AUC). The prediction models for the medical and daily care services demand of the elderly were developed and validated using 12 and 13 key features, respectively. The LightGBM algorithm emerged as the superior prediction model for estimating the service needs of the elderly. For the medical service demand prediction model, LightGBM achieved an AUC of 0.910 and F1-score of 0.841. In the daily care services demand prediction model, LightGBM demonstrated an AUC of 0.906 and an F1-score of 0.819. In the LightGBM model, the analysis of feature importance indicates that the number of chronic diseases, education level, and financial sources emerge as the most significant predictors for the demand of healthcare services, encompassing both medical and daily care services. Based on questionnaire information combined with feature selection, unbalanced data processing and machine learning methods, this study constructed a machine learning model for predicting the demand for medical and daily care services for the elderly, and analyzed the influencing factors of the demand for medical and daily care services for the elderly, providing a reference for the construction and verification of future prediction models for the demand for medical and daily care services for the elderly.
| S-EPMC11897399 | biostudies-literature
Project description:The aim of the study was to build a machine learning-based predictive model to discriminate between hospitalized patients at low risk and high risk of bloodstream infection (BSI). A Data Mart including all patients hospitalized between January 2016 and December 2019 with suspected BSI was built. Multivariate logistic regression was applied to develop a clinically interpretable machine learning predictive model. The model was trained on 2016-2018 data and tested on 2019 data. A feature selection based on a univariate logistic regression first selected candidate predictors of BSI. A multivariate logistic regression with stepwise feature selection in five-fold cross-validation was applied to express the risk of BSI. A total of 5660 hospitalizations (4026 and 1634 in the training and the validation subsets, respectively) were included. Eleven predictors of BSI were identified. The performance of the model in terms of AUROC was 0.74. Based on the interquartile predicted risk score, 508 (31.1%) patients were defined as being at low risk, 776 (47.5%) at medium risk, and 350 (21.4%) at high risk of BSI. Of them, 14.2% (72/508), 30.8% (239/776), and 64% (224/350) had a BSI, respectively. The performance of the predictive model of BSI is promising. Computational infrastructure and machine learning models can help clinicians identify people at low risk for BSI, ultimately supporting an antibiotic stewardship approach.
| S-EPMC10887662 | biostudies-literature
Project description:Estimating the relationships between individuals is one of the fundamental challenges in many fields. In particular, relationship estimation could provide valuable information for missing persons cases. The recently developed investigative genetic genealogy approach uses high-density single nucleotide polymorphisms (SNPs) to determine close and more distant relationships, in which hundreds of thousands to tens of millions of SNPs are generated either by microarray genotyping or whole-genome sequencing. The current studies usually assume the SNP profiles were generated with minimum errors. However, in the missing person cases, the DNA samples can be highly degraded, and the SNP profiles generated from these samples usually contain lots of errors. In this study, a robust machine learning approach was developed for estimating the relationships with high error SNP profiles. In this approach, a hierarchical classification strategy was employed first to classify the relationships by degree and then the relationship types within each degree separately. As for each classification, feature selection was implemented to gain better performance. Both simulated and real data sets with various genotyping error rates were utilized in evaluating this approach, and the accuracies of this approach were higher than individual measures; namely, this approach was more accurate and robust than the individual measures for SNP profiles with genotyping errors. In addition, the highest accuracy could be obtained by providing the same genotyping error rates in train and test sets, and thus estimating genotyping errors of the SNP profiles is critical to obtaining high accuracy of relationship estimation.
2022-07-27 | GSE209804 | GEO
Project description:This paper addresses an important materials engineering question: How can one identify the complete space (or as much of it as possible) of microstructures that are theoretically predicted to yield the desired combination of properties demanded by a selected application? We present a problem involving design of magnetoelastic Fe-Ga alloy microstructure for enhanced elastic, plastic and magnetostrictive properties. While theoretical models for computing properties given the microstructure are known for this alloy, inversion of these relationships to obtain microstructures that lead to desired properties is challenging, primarily due to the high dimensionality of microstructure space, multi-objective design requirement and non-uniqueness of solutions. These challenges render traditional search-based optimization methods incompetent in terms of both searching efficiency and result optimality. In this paper, a route to address these challenges using a machine learning methodology is proposed. A systematic framework consisting of random data generation, feature selection and classification algorithms is developed. Experiments with five design problems that involve identification of microstructures that satisfy both linear and nonlinear property constraints show that our framework outperforms traditional optimization methods with the average running time reduced by as much as 80% and with optimality that would not be achieved otherwise.
| S-EPMC4477370 | biostudies-other
Project description:Motor deficits are observed in Alzheimer's disease (AD) prior to the appearance of cognitive symptoms. To investigate the role of amyloid proteins in gait disturbances, we characterized locomotion in APP-overexpressing transgenic J20 mice. We used three-dimensional motion capture to characterize quadrupedal locomotion on a treadmill in J20 and wild-type mice. Sixteen J20 mice and fifteen wild-type mice were studied at two ages (4- and 13-month). A random forest (RF) classification algorithm discriminated between the genotypes within each age group using a leave-one-out cross-validation. The balanced accuracy of the RF classification was 92.3 ± 5.2% and 93.3 ± 4.5% as well as False Negative Rate (FNR) of 0.0 ± 0.0% and 0.0 ± 0.0% for the 4-month and 13-month groups, respectively. Feature ranking algorithms identified kinematic features that when considered simultaneously, achieved high genotype classification accuracy. The identified features demonstrated an age-specific kinematic profile of the impact of APP-overexpression. Trunk tilt and unstable hip movement patterns were important in classifying the 4-month J20 mice, whereas patterns of shoulder and iliac crest movement were critical for classifying 13-month J20 mice. Examining multiple kinematic features of gait simultaneously could also be developed to classify motor disorders in humans.
| S-EPMC7889656 | biostudies-literature