scholarly journals Predicting postoperative vomiting among orthopedic patients receiving patient-controlled epidural analgesia using SVM and LR

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Hsin-Yun Wu ◽  
Cihun-Siyong Alex Gong ◽  
Shih-Pin Lin ◽  
Kuang-Yi Chang ◽  
Mei-Yung Tsou ◽  
...  
Keyword(s):  
High Risk ◽  
Epidural Analgesia ◽  
Predictive Model ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Roc Curves ◽  
Supervised Machine Learning ◽  
Patient Specific ◽  
Support Vector ◽  
Orthopedic Patients

Abstract Patient-controlled epidural analgesia (PCEA) has been applied to reduce postoperative pain in orthopedic surgical patients. Unfortunately, PCEA is occasionally accompanied by nausea and vomiting. The logistic regression (LR) model is widely used to predict vomiting, and recently support vector machines (SVM), a supervised machine learning method, has been used for classification and prediction. Unlike our previous work which compared Artificial Neural Networks (ANNs) with LR, this study uses a SVM-based predictive model to identify patients with high risk of vomiting during PCEA and comparing results with those derived from the LR-based model. From January to March 2007, data from 195 patients undergoing PCEA following orthopedic surgery were applied to develop two predictive models. 75% of the data were randomly selected for training, while the remainder was used for testing to validate predictive performance. The area under curve (AUC) was measured using the Receiver Operating Characteristic curve (ROC). The area under ROC curves of LR and SVM models were 0.734 and 0.929, respectively. A computer-based predictive model can be used to identify those who are at high risk for vomiting after PCEA, allowing for patient-specific therapeutic intervention or the use of alternative analgesic methods.

scholarly journals Predicting Postoperative Vomiting for Orthopedic Patients Receiving Patient-Controlled Epidural Analgesia with the Application of an Artificial Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Cihun-Siyong Alex Gong ◽  
Lu Yu ◽  
Chien-Kun Ting ◽  
Mei-Yung Tsou ◽  
Kuang-Yi Chang ◽  
...  
Keyword(s):  
Epidural Analgesia ◽  
Predictive Model ◽  
Orthopedic Surgery ◽  
Roc Curves ◽  
Alternative Methods ◽  
Patient Specific ◽  
Data Sets ◽  
Ann Model ◽  
Artificial Neural ◽  
Orthopedic Patients

Patient-controlled epidural analgesia (PCEA) was used in many patients receiving orthopedic surgery to reduce postoperative pain but is accompanied with certain incidence of vomiting. Predictions of the vomiting event, however, were addressed by only a few authors using logistic regression (LR) models. Artificial neural networks (ANN) are pattern-recognition tools that can be used to detect complex patterns within data sets. The purpose of this study was to develop the ANN based predictive model to identify patients with high risk of vomiting during PCEA used. From January to March 2007, the PCEA records of 195 patients receiving PCEA after orthopedic surgery were used to develop the two predicting models. The ANN model had a largest area under curve (AUC) in receiver operating characteristic (ROC) curve. The areas under ROC curves of ANN and LR models were 0.900 and 0.761, respectively. The computer-based predictive model should be useful in increasing vigilance in those patients most at risk for vomiting while PCEA is used, allowing for patient-specific therapeutic intervention, or even in suggesting the use of alternative methods of analgesia.

scholarly journals A Framework for Effective Application of Machine Learning to Microbiome-Based Classification Problems

mBio ◽  
2020 ◽  
Vol 11 (3) ◽  
Author(s):  
Begüm D. Topçuoğlu ◽  
Nicholas A. Lesniak ◽  
Mack T. Ruffin ◽  
Jenna Wiens ◽  
Patrick D. Schloss
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Sequence Data ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Model Complexity ◽  
Support Vector ◽  
Classification Problems ◽  
Microbial Biomarkers

ABSTRACT Machine learning (ML) modeling of the human microbiome has the potential to identify microbial biomarkers and aid in the diagnosis of many diseases such as inflammatory bowel disease, diabetes, and colorectal cancer. Progress has been made toward developing ML models that predict health outcomes using bacterial abundances, but inconsistent adoption of training and evaluation methods call the validity of these models into question. Furthermore, there appears to be a preference by many researchers to favor increased model complexity over interpretability. To overcome these challenges, we trained seven models that used fecal 16S rRNA sequence data to predict the presence of colonic screen relevant neoplasias (SRNs) (n = 490 patients, 261 controls and 229 cases). We developed a reusable open-source pipeline to train, validate, and interpret ML models. To show the effect of model selection, we assessed the predictive performance, interpretability, and training time of L2-regularized logistic regression, L1- and L2-regularized support vector machines (SVM) with linear and radial basis function kernels, a decision tree, random forest, and gradient boosted trees (XGBoost). The random forest model performed best at detecting SRNs with an area under the receiver operating characteristic curve (AUROC) of 0.695 (interquartile range [IQR], 0.651 to 0.739) but was slow to train (83.2 h) and not inherently interpretable. Despite its simplicity, L2-regularized logistic regression followed random forest in predictive performance with an AUROC of 0.680 (IQR, 0.625 to 0.735), trained faster (12 min), and was inherently interpretable. Our analysis highlights the importance of choosing an ML approach based on the goal of the study, as the choice will inform expectations of performance and interpretability. IMPORTANCE Diagnosing diseases using machine learning (ML) is rapidly being adopted in microbiome studies. However, the estimated performance associated with these models is likely overoptimistic. Moreover, there is a trend toward using black box models without a discussion of the difficulty of interpreting such models when trying to identify microbial biomarkers of disease. This work represents a step toward developing more-reproducible ML practices in applying ML to microbiome research. We implement a rigorous pipeline and emphasize the importance of selecting ML models that reflect the goal of the study. These concepts are not particular to the study of human health but can also be applied to environmental microbiology studies.

Predicting 90-Day and 1-Year Mortality in Spinal Metastatic Disease: Development and Internal Validation

Neurosurgery ◽  
2019 ◽  
Vol 85 (4) ◽  
pp. E671-E681 ◽  
Author(s):  
Aditya V Karhade ◽  
Quirina C B S Thio ◽  
Paul T Ogink ◽  
Christopher M Bono ◽  
Marco L Ferrone ◽  
...  
Keyword(s):  
Metastatic Disease ◽  
Web Application ◽  
Performance Status ◽  
Predictive Performance ◽  
Operative Management ◽  
Stochastic Gradient ◽  
Patient Specific ◽  
Gradient Boosting ◽  
Support Vector ◽  
Stochastic Gradient Boosting

Abstract BACKGROUND Increasing prevalence of metastatic disease has been accompanied by increasing rates of surgical intervention. Current tools have poor to fair predictive performance for intermediate (90-d) and long-term (1-yr) mortality. OBJECTIVE To develop predictive algorithms for spinal metastatic disease at these time points and to provide patient-specific explanations of the predictions generated by these algorithms. METHODS Retrospective review was conducted at 2 large academic medical centers to identify patients undergoing initial operative management for spinal metastatic disease between January 2000 and December 2016. Five models (penalized logistic regression, random forest, stochastic gradient boosting, neural network, and support vector machine) were developed to predict 90-d and 1-yr mortality. RESULTS Overall, 732 patients were identified with 90-d and 1-yr mortality rates of 181 (25.1%) and 385 (54.3%), respectively. The stochastic gradient boosting algorithm had the best performance for 90-d mortality and 1-yr mortality. On global variable importance assessment, albumin, primary tumor histology, and performance status were the 3 most important predictors of 90-d mortality. The final models were incorporated into an open access web application able to provide predictions as well as patient-specific explanations of the results generated by the algorithms. The application can be found at https://sorg-apps.shinyapps.io/spinemetssurvival/ CONCLUSION Preoperative estimation of 90-d and 1-yr mortality was achieved with assessment of more flexible modeling techniques such as machine learning. Integration of these models into applications and patient-centered explanations of predictions represent opportunities for incorporation into healthcare systems as decision tools in the future.

scholarly journals Validation of a Prediction Tool for Chemotherapy Toxicity in Older Adults With Cancer

2016 ◽  
Vol 34 (20) ◽  
pp. 2366-2371 ◽  
Author(s):  
Arti Hurria ◽  
Supriya Mohile ◽  
Ajeet Gajra ◽  
Heidi Klepin ◽  
Hyman Muss ◽  
...  
Keyword(s):  
Older Adults ◽  
High Risk ◽  
Prediction Model ◽  
Receiver Operating Characteristic Curve ◽  
Predictive Model ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Chemotherapy Toxicity ◽  
Operating Characteristic Curve ◽  
Grade 3

Purpose Older adults are at increased risk for chemotherapy toxicity, and standard oncology assessment measures cannot identify those at risk. A predictive model for chemotherapy toxicity was developed (N = 500) that consisted of geriatric assessment questions and other clinical variables. This study aims to externally validate this model in an independent cohort (N = 250). Patients and Methods Patients age ≥ 65 years with a solid tumor, fluent in English, and who were scheduled to receive a new chemotherapy regimen were recruited from eight institutions. Risk of chemotherapy toxicity was calculated (low, medium, or high risk) on the basis of the prediction model before the start of chemotherapy. Chemotherapy-related toxicity was captured (grade 3 [hospitalization indicated], grade 4 [life threatening], and grade 5 [treatment-related death]). Validation of the prediction model was performed by calculating the area under the receiver-operating characteristic curve. Results The study sample (N = 250) had a mean age of 73 years (range, 65 to 94 [standard deviation, 5.8]). More than one half of patients (58%) experienced grade ≥ 3 toxicity. Risk of toxicity increased with increasing risk score (36.7% low, 62.4% medium, 70.2% high risk; P < .001). The area under the curve of the receiver-operating characteristic curve was 0.65 (95% CI, 0.58 to 0.71), which was not statistically different from the development cohort (0.72; 95% CI, 0.68 to 0.77; P = .09). There was no association between Karnofsky Performance Status and chemotherapy toxicity (P = .25). Conclusion This study externally validated a chemotherapy toxicity predictive model for older adults with cancer. This predictive model should be considered when discussing the risks and benefits of chemotherapy with older adults.

Abstract MP10: Microbiome-based Diagnostic Screening Of Cardiovascular Disease Using A Machine Learning Approach

Hypertension ◽  
2020 ◽  
Vol 76 (Suppl_1) ◽  
Author(s):  
Sachin Aryal ◽  
Ahmad Alimadadi ◽  
Ishan Manandhar ◽  
Bina Joe ◽  
Xi Cheng
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Cardiovascular Health ◽  
Characteristic Curve ◽  
Feature Space ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Diagnostic Screening ◽  
New Strategy ◽  
Metagenomics Data

In recent years, the microbiome has been recognized as an important factor associated with cardiovascular disease (CVD), which is the leading cause of human mortality worldwide. Disparities in gut microbial compositions between individuals with and without CVD were reported, whereby, we hypothesized that utilizing such microbiome-based data for training with supervised machine learning (ML) models could be exploited as a new strategy for evaluation of cardiovascular health. To test our hypothesis, we analyzed the metagenomics data extracted from the American Gut Project. Specifically, 16S rRNA reads from stool samples of 478 CVD and 473 non-CVD control samples were analyzed using five supervised ML algorithms: random forest (RF), support vector machine with radial kernel (svmRadial), decision tree (DT), elastic net (ENet) and neural networks (NN). Thirty-nine differential bacterial taxa (LEfSe: LDA > 2) were identified between CVD and non-CVD groups. ML classifications, using these taxonomic features, achieved an AUC (area under the receiver operating characteristic curve) of ~0.58 (RF). However, by choosing the top 500 high-variance features of operational taxonomic units (OTUs) for training ML models, an improved AUC of ~0.65 (RF) was achieved. Further, by limiting the selection to only the top 25 highly contributing OTU features to reduce the dimensionality of feature space, the AUC was further significantly enhanced to ~0.70 (RF). In summary, this study is the first to demonstrate the successful development of a ML model using microbiome-based datasets for a systematic diagnostic screening of CVD.

scholarly journals Prediction of post-stroke epilepsy using machine learning method

2020 ◽  
Author(s):  
Anjiao Peng ◽  
Xiaorong Yang ◽  
Zhining Wen ◽  
Wanling Li ◽  
Yusha Tang ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Cerebral Infarction ◽  
Cerebral Hemorrhage ◽  
Characteristic Curve ◽  
Support Vector ◽  
Learning Methods ◽  
Post Stroke ◽  
Machine Learning Methods ◽  
One Year

Abstract Background : Stroke is one of the most important causes of epilepsy and we aimed to find if it is possible to predict patients with high risk of developing post-stroke epilepsy (PSE) at the time of discharge using machine learning methods. Methods : Patients with stroke were enrolled and followed at least one year. Machine learning methods including support vector machine (SVM), random forest (RF) and logistic regression (LR) were used to learn the data. Results : A total of 2730 patients with cerebral infarction and 844 patients with cerebral hemorrhage were enrolled and the risk of PSE was 2.8% after cerebral infarction and 7.8% after cerebral hemorrhage in one year. Machine learning methods showed good performance in predicting PSE. The area under the receiver operating characteristic curve (AUC) for SVM and RF in predicting PSE after cerebral infarction was close to 1 and it was 0.92 for LR. When predicting PSE after cerebral hemorrhage, the performance of SVM was best with AUC being close to 1, followed by RF ( AUC = 0.99) and LR (AUC = 0.85) . Conclusion : Machine learning methods could be used to predict patients with high risk of developing PSE, which will help to stratify patients with high risk and start treatment earlier. Nevertheless, more work is needed before the application of thus intelligent predictive model in clinical practice.

scholarly journals Comparative Analysis of Supervised Machine Learning Algorithms to Build a Predictive Model for Evaluating Students’ Performance

2021 ◽  
Vol 17 (02) ◽  
pp. 90
Author(s):  
Inssaf El Guabassi ◽  
Zakaria Bousalem ◽  
Rim Marah ◽  
Aimad Qazdar
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Predictive Model ◽  
Learning Algorithms ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
The Future ◽  
Log Linear

In recent years, the world's population is increasingly demanding to predict the future with certainty, predicting the right information in any area is becoming a necessity. One of the ways to predict the future with certainty is to determine the possible future. In this sense, machine learning is a way to analyze huge datasets to make strong predictions or decisions. The main objective of this research work is to build a predictive model for evaluating students’ performance. Hence, the contributions are threefold. The first is to apply several supervised machine learning algorithms (i.e. ANCOVA, Logistic Regression, Support Vector Regression, Log-linear Regression, Decision Tree Regression, Random Forest Regression, and Partial Least Squares Regression) on our education dataset. The second purpose is to compare and evaluate algorithms used to create a predictive model based on various evaluation metrics. The last purpose is to determine the most important factors that influence the success or failure of the students. The experimental results showed that the Log-linear Regression provides a better prediction as well as the behavioral factors that influence students’ performance.

scholarly journals 771. Prospective validation of the universal vital assessment (UVA) score to predict the in-hospital mortality of patients with acute illness admitted to a government district hospital in KwaZulu-Natal, South Africa

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S430-S430
Author(s):  
Amiya A Ahmed ◽  
Joseph B Ladines-Lim ◽  
Christopher Moore ◽  
Sipho Malinga ◽  
Anthony Moll ◽  
...  
Keyword(s):  
South Africa ◽  
High Risk ◽  
District Hospital ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Roc Curves ◽  
Routine Care ◽  
Resource Limited ◽  
Tract Infections ◽  
Better Than

Abstract Background Critical illness is a frequent cause of mortality in resource-limited settings. Improved triage on admission could improve mortality, but existing tools depend on variables that often are not available. We prospectively evaluated the universal vital assessment (UVA) score to predict mortality among patients admitted to a district hospital in rural, highly HIV-prevalent South Africa. Figure 1. Receiver operator characteristic (ROC) curves for the UVA and qSOFA scoring tools. Methods In February-March 2020, adults admitted to the medical wards were enrolled, prior to interruption by covid19, and 30-day mortality assessed. Clinical parameters including temperature, heart and respiratory rates, systolic blood pressure, oxygen saturation, Glasgow Coma Scale score, and HIV status were collected within 24 hours of admission as part of routine care. Lower respiratory tract infections (LRTI) included pneumonia and suspected pulmonary tuberculosis. To evaluate the predictive ability of the UVA score, area under the receiver operating characteristic curve (aROC) and age-sex adjusted binary logistic regression models were generated, and compared to the sequential organ failure assessment (qSOFA). Results Sixty one patients were enrolled; outcomes were available for 56 patients. Patients had a mean age of 52 (SD+17), 51% were women, and 46% were HIV infected. The 30-day mortality was 16.1% (9/56) with infections and non-communicable diseases comprising 47% and 47% of admission diagnoses, respectively. The most common admitting diagnosis was LRTI (24.6%). The median (+IQR) UVA score was 2 (+3) accounting for 36% of participants. Medium-risk (2-4) and high-risk (&gt;4) UVA groups were not associated with 30-day mortality, although the high-risk score trended towards significance (p=0.07). However, a UVA score &gt; 3 was significantly associated with 30-day mortality, both alone and after adjusting for age and sex (aOR 6.2, 95% CI 1.2-33.1; p=0.03). The aROC (95% CI) for the UVA score was 0.74 (0.55 – 0.93), which performed better than qSOFA (aROC 0.59, 95% CI 0.37-0.81) and is shown in Figure 1. Conclusion In this resource-limited, HIV-prevalent setting, the UVA score predicted mortality better than the qSOFA score. A moderate-risk UVA score (&gt;3) was predictive of 30-day mortality, though needs to be confirmed in larger studies. Disclosures All Authors: No reported disclosures

scholarly journals Machine learning prediction of antiviral-HPV protein interactions for anti-HPV pharmacotherapy

2021 ◽  
Author(s):  
Hui-Heng Lin ◽  
Qian-Ru Zhang ◽  
Xiangjun Kong ◽  
Liuping Zhang ◽  
Yong Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
High Risk ◽  
Protein Interactions ◽  
Predictive Models ◽  
Drug Repositioning ◽  
Predictive Performance ◽  
Support Vector ◽  
Interaction Dataset ◽  
Target Interaction

Background: Persistent infection with high-risk types Human Papillomavirus could cause diseases including cervical cancers and oropharyngeal cancers. Nonetheless, so far there is no effective pharmacotherapy for treating the infection from high-risk HPV types, and hence it remains to be a severe threat to health of female. Methods: In light of drug repositioning strategy, we trained and benchmarked multiple machine learning predictive models so as to predict potential effective antiviral drugs for HPV infection in this work. Based on antiviral-target interaction dataset, we generated high dimension feature set of drug-target interaction pairs and used the dataset to train and construct machine learning predictive models. Results: Through optimizing models, measuring models predictive performance using 182 pairs of antiviral-target interaction dataset which were all approved by United States Food and Drug Administration, and benchmarking different models predictive performance, we identified the optimized Support Vector Machine and K-Nearest Neighbor classifier with high precision score were the best two predictors (0.80 and 0.85 respectively) amongst classifiers of Support Vector Machine, Random forest, Adaboost, Naive Bayes, K-Nearest Neighbors, and Logistic regression classifier. We applied these two predictors together and successfully predicted 58 pairs of antiviral-HPV protein interactions from 846 pairs of antiviral-HPV protein associations. Conclusions: Our work provided good drug candidates for anti-HPV drug discovery. So far as we know, we are the first one to conduct such HPV-oriented computational drug repositioning study.

scholarly journals Recapitulation of patient-specific 3D chromatin conformation using machine learning and validation of identified enhancer-gene targets

2021 ◽  
Author(s):  
Duo Xu ◽  
Andre Neil Forbes ◽  
Sandra Cohen ◽  
Ann Palladino ◽  
Tatiana Karadimitriou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Target Genes ◽  
Characteristic Curve ◽  
Regulatory Elements ◽  
Supervised Machine Learning ◽  
Patient Specific ◽  
Cancer Type ◽  
Omics Data ◽  
Cancer Genes ◽  
Large Patient

Regulatory networks containing enhancer to gene edges define cellular state and their rewiring is a hallmark of cancer. While efforts, such as ENCODE, have revealed these networks for reference tissues and cell-lines by integrating multi-omics data, the same methods cannot be applied for large patient cohorts due to the constraints on generating ChIP-seq and three-dimensional data from limited material in patient biopsies. We trained a supervised machine learning model using genomic 3D signatures of physical enhancer-gene connections that can predict accurate connections using data from ATAC-seq and RNA-seq assays only, which can be easily generated from patient biopsies. Our method overcomes the major limitations of correlation-based approaches that cannot distinguish between distinct target genes of given enhancers in different samples, which is a hallmark of network rewiring in cancer. Our model achieved an AUROC (area under receiver operating characteristic curve) of 0.91 and, importantly, can distinguish between active regulatory elements with connections to target genes and poised elements with no connections to target genes. Our predicted regulatory elements are validated by multi-omics data, including histone modification marks from ENCODE, with an average specificity of 0.92. Application of our model on chromatin accessibility and transcriptomic data from 400 cancer patients across 22 cancer types revealed novel cancer-type and subtype-specific enhancer-gene connections for known cancer genes. In one example, we identified two enhancers that regulate the expression of ESR1 in only ER+ breast cancer (BRCA) samples but not in ER- samples. These enhancers are predicted to contribute to the high expression of ESR1 in 93% of ER+ BRCA samples. Functional validation using CRISPRi confirms that inhibition of these enhancers decreases the expression of ESR1 in ER+ samples.

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