scholarly journals A Clinical Predictive Model of Progesterone Elevation on the Day of HCG Administration Was Establish Based on the Basal P, BMI and Ovarian Induction Regimen

2021 ◽  
Author(s):  
chunmei yu ◽  
chao zhou ◽  
xiuliang dai ◽  
jianmei zhou ◽  
haiyan yang ◽  
...  
Keyword(s):  
Predictive Model ◽  
Prediction Models ◽  
Graphical Representation ◽  
Area Under The Curve ◽  
Care Hospital ◽  
Fresh Embryo Transfer ◽  
Ovarian Induction ◽  
Clinical Predictive Model ◽  
Infertility Patients ◽  
User Friendly

Abstract Background The progesterone elevation (PE) on the day of human chorionic gonadotrophin (hCG) is associated with a significant decrease in the probability of clinical pregnancy after fresh embryo transfer. The goal of this study was to develop a nomogram to explore the baseline indexes to predict the occurrence of PE (0.9ng/ml) on the day of HCG administration before IVF/ICSI treatment.Methods The patients who were performed a GnRH agonist or antagonist pituitary suppression protocol during controlled ovulation stimulation(COS) in reproductive center of Changzhou maternal and child health care hospital from 2017-2019 were included. The nomogram was built from all participants.Results Three variates significantly associated with the PE occurrence on the HCG administration of infertility women were the type ovulation regiment, basal P and BMI. This predictive model showed good calibration and discriminatory abilities, with an area under the curve (AUC) of 0.639（95%CI 0.596~0.681）. Hosmer and Leme show test were performed for evaluating both calibration and discrimination and confirmed our nomogram is a user-friendly graphical representation of the model (χ2= 4.750，P=0.784)Conclusion The nomogram presents graphically association factors (type ovulation regiment, basal P and BMI) and prediction models which can offer simple and useful guidance to avoid the PE occurrence on the HCG administration for clinicians and infertility patients.

scholarly journals Using Data Analytics to Predict Hospital Mortality in Sepsis Patients

2019 ◽  
Vol 14 (3) ◽  
pp. 40-57
Author(s):  
Yazan Alnsour ◽  
Rassule Hadidi ◽  
Neetu Singh
Keyword(s):  
Hospital Mortality ◽  
Predictive Model ◽  
Health Care Providers ◽  
Prediction Models ◽  
Predictive Analytics ◽  
Area Under The Curve ◽  
The United States ◽  
Hospital Death ◽  
Care Providers ◽  
Chi Square

Predictive analytics can be used to anticipate the risks associated with some patients, and prediction models can be employed to alert physicians and allow timely proactive interventions. Recently, health care providers have been using different types of tools with prediction capabilities. Sepsis is one of the leading causes of in-hospital death in the United States and worldwide. In this study, the authors used a large medical dataset to develop and present a model that predicts in-hospital mortality among Sepsis patients. The predictive model was developed using a dataset of more than one million records of hospitalized patients. The independent predictors of in-hospital mortality were identified using the chi-square automatic interaction detector. The authors found that adding hospital attributes to the predictive model increased the accuracy from 82.08% to 85.3% and the area under the curve from 0.69 to 0.84, which is favorable compared to using only patients' attributes. The authors discuss the practical and research contributions of using a predictive model that incorporates both patient and hospital attributes in identifying high-risk patients.

scholarly journals Using Machine Learning to Predict Bacteremia in Febrile Children Presented to the Emergency Department

Diagnostics ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 307 ◽  
Author(s):  
Chih-Min Tsai ◽  
Chun-Hung Richard Lin ◽  
Huan Zhang ◽  
I-Min Chiu ◽  
Chi-Yung Cheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Blood Culture ◽  
Predictive Model ◽  
Prediction Models ◽  
Area Under The Curve ◽  
Pediatric Emergency ◽  
High Rate ◽  
Support Vector ◽  
Retrospective Case

Blood culture is frequently used to detect bacteremia in febrile children. However, a high rate of negative or false-positive blood culture results is common at the pediatric emergency department (PED). The aim of this study was to use machine learning to build a model that could predict bacteremia in febrile children. We conducted a retrospective case-control study of febrile children who presented to the PED from 2008 to 2015. We adopted machine learning methods and cost-sensitive learning to establish a predictive model of bacteremia. We enrolled 16,967 febrile children with blood culture tests during the eight-year study period. Only 146 febrile children had true bacteremia, and more than 99% of febrile children had a contaminant or negative blood culture result. The maximum area under the curve of logistic regression and support vector machines to predict bacteremia were 0.768 and 0.832, respectively. Using the predictive model, we can categorize febrile children by risk value into five classes. Class 5 had the highest probability of having bacteremia, while class 1 had no risk. Obtaining blood cultures in febrile children at the PED rarely identifies a causative pathogen. Prediction models can help physicians determine whether patients have bacteremia and may reduce unnecessary expenses.

scholarly journals A tutorial on calibration measurements and calibration models for clinical prediction models

2020 ◽  
Vol 27 (4) ◽  
pp. 621-633 ◽  
Author(s):  
Yingxiang Huang ◽  
Wentao Li ◽  
Fima Macheret ◽  
Rodney A Gabriel ◽  
Lucila Ohno-Machado
Keyword(s):  
Predictive Model ◽  
Predictive Models ◽  
Prediction Models ◽  
Model Performance ◽  
Simulated Data ◽  
Primary Objective ◽  
Adequate Model ◽  
Starting Point ◽  
Calibration Models ◽  
Clinical Predictive Model

Abstract Our primary objective is to provide the clinical informatics community with an introductory tutorial on calibration measurements and calibration models for predictive models using existing R packages and custom implemented code in R on real and simulated data. Clinical predictive model performance is commonly published based on discrimination measures, but use of models for individualized predictions requires adequate model calibration. This tutorial is intended for clinical researchers who want to evaluate predictive models in terms of their applicability to a particular population. It is also for informaticians and for software engineers who want to understand the role that calibration plays in the evaluation of a clinical predictive model, and to provide them with a solid starting point to consider incorporating calibration evaluation and calibration models in their work. Covered topics include (1) an introduction to the importance of calibration in the clinical setting, (2) an illustration of the distinct roles that discrimination and calibration play in the assessment of clinical predictive models, (3) a tutorial and demonstration of selected calibration measurements, (4) a tutorial and demonstration of selected calibration models, and (5) a brief discussion of limitations of these methods and practical suggestions on how to use them in practice.

scholarly journals Using Data Analytics to Predict Hospital Mortality in Sepsis Patients

2019 ◽  
Vol 14 (3) ◽  
pp. 1-18
Author(s):  
Yazan Alnsour ◽  
Rassule Hadidi ◽  
Neetu Singh
Keyword(s):  
Hospital Mortality ◽  
Predictive Model ◽  
Health Care Providers ◽  
Prediction Models ◽  
Predictive Analytics ◽  
Area Under The Curve ◽  
The United States ◽  
Hospital Death ◽  
Care Providers ◽  
Chi Square

Predictive analytics can be used to anticipate the risks associated with some patients, and prediction models can be employed to alert physicians and allow timely proactive interventions. Recently, health care providers have been using different types of tools with prediction capabilities. Sepsis is one of the leading causes of in-hospital death in the United States and worldwide. In this study, the authors used a large medical dataset to develop and present a model that predicts in-hospital mortality among Sepsis patients. The predictive model was developed using a dataset of more than one million records of hospitalized patients. The independent predictors of in-hospital mortality were identified using the chi-square automatic interaction detector. The authors found that adding hospital attributes to the predictive model increased the accuracy from 82.08% to 85.3% and the area under the curve from 0.69 to 0.84, which is favorable compared to using only patients' attributes. The authors discuss the practical and research contributions of using a predictive model that incorporates both patient and hospital attributes in identifying high-risk patients.

scholarly journals Prediction of COVID-19 severity using laboratory findings on admission: informative values, thresholds, ML model performance

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e044500
Author(s):  
Yauhen Statsenko ◽  
Fatmah Al Zahmi ◽  
Tetiana Habuza ◽  
Klaus Neidl-Van Gorkom ◽  
Nazar Zaki
Keyword(s):  
Lymphocyte Count ◽  
Prediction Models ◽  
Model Performance ◽  
Area Under The Curve ◽  
Predictive Biomarkers ◽  
Supervised Machine Learning ◽  
Study Cohort ◽  
Threshold Values ◽  
Laboratory Findings ◽  
Study Results

BackgroundDespite the necessity, there is no reliable biomarker to predict disease severity and prognosis of patients with COVID-19. The currently published prediction models are not fully applicable to clinical use.ObjectivesTo identify predictive biomarkers of COVID-19 severity and to justify their threshold values for the stratification of the risk of deterioration that would require transferring to the intensive care unit (ICU).MethodsThe study cohort (560 subjects) included all consecutive patients admitted to Dubai Mediclinic Parkview Hospital from February to May 2020 with COVID-19 confirmed by the PCR. The challenge of finding the cut-off thresholds was the unbalanced dataset (eg, the disproportion in the number of 72 patients admitted to ICU vs 488 non-severe cases). Therefore, we customised supervised machine learning (ML) algorithm in terms of threshold value used to predict worsening.ResultsWith the default thresholds returned by the ML estimator, the performance of the models was low. It was improved by setting the cut-off level to the 25th percentile for lymphocyte count and the 75th percentile for other features. The study justified the following threshold values of the laboratory tests done on admission: lymphocyte count <2.59×109/L, and the upper levels for total bilirubin 11.9 μmol/L, alanine aminotransferase 43 U/L, aspartate aminotransferase 32 U/L, D-dimer 0.7 mg/L, activated partial thromboplastin time (aPTT) 39.9 s, creatine kinase 247 U/L, C reactive protein (CRP) 14.3 mg/L, lactate dehydrogenase 246 U/L, troponin 0.037 ng/mL, ferritin 498 ng/mL and fibrinogen 446 mg/dL.ConclusionThe performance of the neural network trained with top valuable tests (aPTT, CRP and fibrinogen) is admissible (area under the curve (AUC) 0.86; 95% CI 0.486 to 0.884; p<0.001) and comparable with the model trained with all the tests (AUC 0.90; 95% CI 0.812 to 0.902; p<0.001). Free online tool at https://med-predict.com illustrates the study results.

scholarly journals Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Espen Jimenez-Solem ◽  
Tonny S. Petersen ◽  
Casper Hansen ◽  
Christian Hansen ◽  
Christina Lioma ◽  
...  
Keyword(s):  
Prediction Models ◽  
Severe Disease ◽  
External Validation ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Mortality Prediction ◽  
Icu Admission ◽  
Risk Of Death ◽  
Online Risk ◽  
The United Kingdom

AbstractPatients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic markers of disease progression and death. From a cohort of approx. 2.6 million citizens in Denmark, SARS-CoV-2 PCR tests were performed on subjects suspected for COVID-19 disease; 3944 cases had at least one positive test and were subjected to further analysis. SARS-CoV-2 positive cases from the United Kingdom Biobank was used for external validation. The ML models predicted the risk of death (Receiver Operation Characteristics—Area Under the Curve, ROC-AUC) of 0.906 at diagnosis, 0.818, at hospital admission and 0.721 at Intensive Care Unit (ICU) admission. Similar metrics were achieved for predicted risks of hospital and ICU admission and use of mechanical ventilation. Common risk factors, included age, body mass index and hypertension, although the top risk features shifted towards markers of shock and organ dysfunction in ICU patients. The external validation indicated fair predictive performance for mortality prediction, but suboptimal performance for predicting ICU admission. ML may be used to identify drivers of progression to more severe disease and for prognostication patients in patients with COVID-19. We provide access to an online risk calculator based on these findings.

scholarly journals Predicting bacteriophage hosts based on sequences of annotated receptor-binding proteins

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dimitri Boeckaerts ◽  
Michiel Stock ◽  
Bjorn Criel ◽  
Hans Gerstmans ◽  
Bernard De Baets ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Receptor Binding ◽  
Bacterial Infections ◽  
Sequence Data ◽  
Sequence Similarity ◽  
Area Under The Curve ◽  
Local Alignment ◽  
Search Tool ◽  
Different Levels

AbstractNowadays, bacteriophages are increasingly considered as an alternative treatment for a variety of bacterial infections in cases where classical antibiotics have become ineffective. However, characterizing the host specificity of phages remains a labor- and time-intensive process. In order to alleviate this burden, we have developed a new machine-learning-based pipeline to predict bacteriophage hosts based on annotated receptor-binding protein (RBP) sequence data. We focus on predicting bacterial hosts from the ESKAPE group, Escherichia coli, Salmonella enterica and Clostridium difficile. We compare the performance of our predictive model with that of the widely used Basic Local Alignment Search Tool (BLAST). Our best-performing predictive model reaches Precision-Recall Area Under the Curve (PR-AUC) scores between 73.6 and 93.8% for different levels of sequence similarity in the collected data. Our model reaches a performance comparable to that of BLASTp when sequence similarity in the data is high and starts outperforming BLASTp when sequence similarity drops below 75%. Therefore, our machine learning methods can be especially useful in settings in which sequence similarity to other known sequences is low. Predicting the hosts of novel metagenomic RBP sequences could extend our toolbox to tune the host spectrum of phages or phage tail-like bacteriocins by swapping RBPs.

Modified Portland Cement Association Stress Analysis and Thickness Design Procedures

1997 ◽  
Vol 1568 (1) ◽  
pp. 77-88 ◽  
Author(s):  
Ying-Haur Lee ◽  
Jean-Hwa Bair ◽  
Chao-Tsung Lee ◽  
Shao-Tang Yen ◽  
Ying-Ming Lee
Keyword(s):  
Portland Cement ◽  
Stress Analysis ◽  
Prediction Models ◽  
Equivalent Stress ◽  
Design Procedure ◽  
Concrete Pavements ◽  
Spreadsheet Program ◽  
Practical Trial ◽  
Portland Cement Association ◽  
User Friendly

A new stress analysis and thickness design procedure for jointed concrete pavements was developed. On the basis of Westergaard’s edge stress solution and several prediction models for stress adjustments for a variety of loading and environmental (i.e., thermal curling) conditions, a modified Portland Cement Association (PCA) equivalent stress analysis and thickness design procedure was proposed and implemented in a highly user-friendly, Windows-based program, TKUPAV, for practical trial applications. The proposed approach has been further verified by reproducing results very close to the PCA’s equivalent stresses and fatigue damages using a spreadsheet program and the TKUPAV program. The possible detrimental effect of loading plus daytime curling has been illustrated in a case study, which also indicated that the effect of thermal curling should be considered in the thickness design of concrete pavements.

A Clinical Predictive Model for Risk Stratification of Patients with Severe Acute Lower Gastrointestinal Bleeding

2021 ◽  
Author(s):  
Manraj Singh ◽  
Jayne Chiang ◽  
Andre Seah ◽  
Nan Liu ◽  
Ronnie Mathew ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Predictive Model ◽  
Primary Outcome Measure ◽  
Initial Assessment ◽  
Intestinal Bleeding ◽  
Clinical Parameters ◽  
Predictive Values ◽  
Management Algorithm ◽  
Patients At Risk ◽  
Clinical Predictive Model

Abstract Background: Lower Gastro-Intestinal Bleeding (LGIB) is a common presentation of surgical admissions, imposing a significant burden on healthcare costs and resources. There is a paucity of standardised clinical predictive tools available for the initial assessment and risk stratification of patients with LGIB. We propose a simple clinical scoring model to prognosticate patients at risk of severe LGIB and an algorithm to guide management of such patients.Methods: A retrospective cohort study was conducted, identifying consecutive patients admitted to our institution for LGIB over a 1-year period. Baseline demographics, clinical parameters at initial presentation and treatment interventions were recorded. Severe LGIB was the primary outcome measure. Multivariate logistic regression was performed to identify factors predictive of severe LGIB. A clinical management algorithm was developed to discriminate between patients requiring admission, and to guide endoscopic, angiographic and/or surgical intervention.Results: 226/649 (34.8%) patients had severe LGIB. Six variables were entered into a clinical predictive model for risk stratification of LGIB: Tachycardia (HR>100), hypotension (SBP<90mmHg), anemia (Hb<9g/dL), metabolic acidosis, use of antiplatelet/anticoagulants, and active per-rectal bleeding. The optimum cut-off score of >1 had a sensitivity of 91.9%, specificity of 39.8%, and Positive and Negative Predictive Values of 45% and 90.2% respectively for predicting severe LGIB. The Area Under Curve (AUC) was 0.77.BConclusion: Early diagnosis and management of severe LGIB remains a challenge for the acute care surgeon. The predictive model described comprises objective clinical parameters routinely obtained at initial triage to guide risk stratification, disposition and inpatient management of patients.

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