Safety risk factors comprehensive analysis for construction project: Combined cascading effect and machine learning approach

2021 ◽  
Vol 143 ◽  
pp. 105410
Author(s):  
Guofeng Ma ◽  
Zhijiang Wu ◽  
Jianyao Jia ◽  
Shanshan Shang
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Construction Project ◽  
Comprehensive Analysis ◽  
Learning Approach ◽  
Safety Risk ◽  
Cascading Effect ◽  
Machine Learning Approach

A Machine Learning Approach to Risk Factors: A Case Study Using the Fama–French–Carhart Model

2019 ◽  
Vol 1 (1) ◽  
pp. 32-44
Author(s):  
Joseph Simonian ◽  
Chenwei Wu ◽  
Daniel Itano ◽  
Vyshaal Narayanam
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Assessment of bilateral knee pain from MR imaging using deep neural networks

2018 ◽  
Author(s):  
Gary H. Chang ◽  
David T. Felson ◽  
Shangran Qiu ◽  
Terence D. Capellini ◽  
Vijaya B. Kolachalama
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Neural Networks ◽  
Deep Learning ◽  
Knee Pain ◽  
Deep Neural Networks ◽  
Learning Approach ◽  
Mr Images ◽  
Bilateral Knee ◽  
Machine Learning Approach

ABSTRACTBackground and objectiveIt remains difficult to characterize pain in knee joints with osteoarthritis solely by radiographic findings. We sought to understand how advanced machine learning methods such as deep neural networks can be used to analyze raw MRI scans and predict bilateral knee pain, independent of other risk factors.MethodsWe developed a deep learning framework to associate information from MRI slices taken from the left and right knees of subjects from the Osteoarthritis Initiative with bilateral knee pain. Model training was performed by first extracting features from two-dimensional (2D) sagittal intermediate-weighted turbo spin echo slices. The extracted features from all the 2D slices were subsequently combined to directly associate using a fused deep neural network with the output of interest as a binary classification problem.ResultsThe deep learning model resulted in predicting bilateral knee pain on test data with 70.1% mean accuracy, 51.3% mean sensitivity, and 81.6% mean specificity. Systematic analysis of the predictions on the test data revealed that the model performance was consistent across subjects of different Kellgren-Lawrence grades.ConclusionThe study demonstrates a proof of principle that a machine learning approach can be applied to associate MR images with bilateral knee pain.SIGNIFICANCE AND INNOVATIONKnee pain is typically considered as an early indicator of osteoarthritis (OA) risk. Emerging evidence suggests that MRI changes are linked to pre-clinical OA, thus underscoring the need for building image-based models to predict knee pain. We leveraged a state-of-the-art machine learning approach to associate raw MR images with bilateral knee pain, independent of other risk factors.

scholarly journals A machine learning approach to predict early outcomes after pituitary adenoma surgery

2018 ◽  
Vol 45 (5) ◽  
pp. E8 ◽  
Author(s):  
Todd C. Hollon ◽  
Adish Parikh ◽  
Balaji Pandian ◽  
Jamaal Tarpeh ◽  
Daniel A. Orringer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Pituitary Adenoma ◽  
Predictive Model ◽  
Odds Ratio ◽  
Postoperative Outcomes ◽  
Learning Approach ◽  
Ratio Analysis ◽  
Machine Learning Approach ◽  
The Mean

OBJECTIVEPituitary adenomas occur in a heterogeneous patient population with diverse perioperative risk factors, endocrinopathies, and other tumor-related comorbidities. This heterogeneity makes predicting postoperative outcomes challenging when using traditional scoring systems. Modern machine learning algorithms can automatically identify the most predictive risk factors and learn complex risk-factor interactions using training data to build a robust predictive model that can generalize to new patient cohorts. The authors sought to build a predictive model using supervised machine learning to accurately predict early outcomes of pituitary adenoma surgery.METHODSA retrospective cohort of 400 consecutive pituitary adenoma patients was used. Patient variables/predictive features were limited to common patient characteristics to improve model implementation. Univariate and multivariate odds ratio analysis was performed to identify individual risk factors for common postoperative complications and to compare risk factors with model predictors. The study population was split into 300 training/validation patients and 100 testing patients to train and evaluate four machine learning models using binary classification accuracy for predicting early outcomes.RESULTSThe study included a total of 400 patients. The mean ± SD patient age was 53.9 ± 16.3 years, 59.8% of patients had nonfunctioning adenomas and 84.7% had macroadenomas, and the mean body mass index (BMI) was 32.6 ± 7.8 (58.0% obesity rate). Multivariate odds ratio analysis demonstrated that age < 40 years was associated with a 2.86 greater odds of postoperative diabetes insipidus and that nonobese patients (BMI < 30) were 2.2 times more likely to develop postoperative hyponatremia. Using broad criteria for a poor early postoperative outcome—major medical and early surgical complications, extended length of stay, emergency department admission, inpatient readmission, and death—31.0% of patients met criteria for a poor early outcome. After model training, a logistic regression model with elastic net (LR-EN) regularization best predicted early postoperative outcomes of pituitary adenoma surgery on the 100-patient testing set—sensitivity 68.0%, specificity 93.3%, overall accuracy 87.0%. The receiver operating characteristic and precision-recall curves for the LR-EN model had areas under the curve of 82.7 and 69.5, respectively. The most important predictive variables were lowest perioperative sodium, age, BMI, highest perioperative sodium, and Cushing’s disease.CONCLUSIONSEarly postoperative outcomes of pituitary adenoma surgery can be predicted with 87% accuracy using a machine learning approach. These results provide insight into how predictive modeling using machine learning can be used to improve the perioperative management of pituitary adenoma patients.

Risk factors associated with skeletal-related events following denosumab cessation among patients with bone metastases from solid tumors: A real-world machine learning approach.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1567-1567
Author(s):  
Alison Stopeck ◽  
Celestia S. Higano ◽  
David H. Henry ◽  
Basia A. Bachmann ◽  
Marko Rehn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Bone Metastases ◽  
Real World ◽  
Treatment Duration ◽  
Learning Approach ◽  
Factors Associated ◽  
Machine Learning Approach ◽  
Skeletal Related Events ◽  
Denosumab Treatment

1567 Background: The anti-RANKL monoclonal antibody denosumab has been shown to be superior to the bisphosphonate zoledronate for the prevention of skeletal-related events (SREs) in patients with incident bone metastases (BM) from solid tumors (ST). Clinical guidelines recommend the use of a bone-targeting agent for SRE prevention for ≥ 2 years. However, real-world treatment patterns in the U.S. suggest that the denosumab treatment duration is often < 1 year. Applying a machine learning approach, we sought to identify risk factors associated with SRE incidence following cessation of denosumab to help inform optimal clinical SRE prevention strategies. Methods: Using the Optum PanTher Electronic Health Record repository, patients diagnosed with incident BM from a primary ST between 1 Jan 2007 and 1 Sep 2019 were evaluated for inclusion in the study. Eligible patients had to receive ≥ 2 consecutive 120 mg denosumab doses on an every 4-week (± 14 days) schedule and have a minimum follow-up ≥ 1 year after the last denosumab dose or an SRE occurring between days 84 and 365 after denosumab cessation. Extreme gradient boosting was used to develop an SRE risk prediction model evaluated on a test dataset. Impact and relative importance of available medical, clinical, and treatment factors on SRE risk following denosumab cessation were extracted from the model using Shapley additive explanations (SHAP). Univariate analyses on risk factors with the highest importance from pooled and tumor-specific models were also conducted. Results: A total of 1,414 patients (breast, n = 563 [40%]; prostate, 421 [30%]; lung, 180 [13%]; other cancers, 250 [17%]) met inclusion criteria, with a median of 253 (min, 88; max, 2726) days of denosumab treatment; 490 (35%) experienced ≥ 1 SRE following denosumab cessation. With a meaningful model performance based on an area under the receiver operating characteristic (AUROC) score of 77%, SHAP identified several significant factors that predicted an increased SRE risk following denosumab cessation, including prior SREs, shorter denosumab treatment duration, and a higher number of clinic visits as the top-ranked factors (Table). Conclusions: A machine learning approach to SRE risk factor identification may help clinicians better assess the individualized patient’s need for denosumab treatment persistence and improve patient outcomes. Results from tumor-specific groups will be presented at the meeting.[Table: see text]

A Bayesian network model for predicting type 2 diabetes risk based on electronic health records

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740055 ◽  
Author(s):  
Jiang Xie ◽  
Yan Liu ◽  
Xu Zeng ◽  
Wu Zhang ◽  
Zhen Mei
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Electronic Health Records ◽  
Physical Examination ◽  
Risk Model ◽  
Learning Approach ◽  
Health Records ◽  
Machine Learning Approach

An extensive, in-depth study of diabetes risk factors (DBRF) is of crucial importance to prevent (or reduce) the chance of suffering from type 2 diabetes (T2D). Accumulation of electronic health records (EHRs) makes it possible to build nonlinear relationships between risk factors and diabetes. However, the current DBRF researches mainly focus on qualitative analyses, and the inconformity of physical examination items makes the risk factors likely to be lost, which drives us to study the novel machine learning approach for risk model development. In this paper, we use Bayesian networks (BNs) to analyze the relationship between physical examination information and T2D, and to quantify the link between risk factors and T2D. Furthermore, with the quantitative analyses of DBRF, we adopt EHR and propose a machine learning approach based on BNs to predict the risk of T2D. The experiments demonstrate that our approach can lead to better predictive performance than the classical risk model.

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