The predictive performance of the bipolarity index in a Dutch epidemiological sample manuscript

2020 ◽  
Vol 262 ◽  
pp. 373-380 ◽  
Author(s):  
Wendela G. ter Meulen ◽  
Stasja Draisma ◽  
Aartjan T.F. Beekman ◽  
Brenda W.J.H. Penninx ◽  
Ralph W. Kupka
Keyword(s):  
Predictive Performance ◽  
Epidemiological Sample

Diagnosing Alcohol Use Disorders: Results From a Clinical Epidemiological Sample

2008 ◽  
Author(s):  
Lara A. Ray ◽  
Iwona Chelminski ◽  
Diane Young ◽  
Mark Zimmerman
Keyword(s):  
Alcohol Use ◽  
Alcohol Use Disorders ◽  
Epidemiological Sample

Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Wastewater Treatment ◽  
Artificial Neural Networks ◽  
Performance Modeling ◽  
Predictive Performance ◽  
Mathematical Tool ◽  
Effluent Water ◽  
Artificial Neural

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

Machine Learning-Based Scoring Functions. Development and Applications with SAnDReS.

2020 ◽  
Vol 27 ◽  
Author(s):  
Gabriela Bitencourt-Ferreira ◽  
Camila Rizzotto ◽  
Walter Filgueira de Azevedo Junior
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Drug Targets ◽  
Computational Models ◽  
Factor Xa ◽  
Coagulation Factor ◽  
Predictive Performance ◽  
Machine Learning Techniques ◽  
Scoring Functions ◽  
Molegro Virtual Docker

Background: Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can create models to assess protein-ligand potential energy and binding affinity. These methods show superior predictive performance when compared with classical scoring functions available in docking programs. Objective: Our purpose here is to review the development and application of the program SAnDReS. We describe the creation of machine learning models to assess the binding affinity of protein-ligand complexes. Method: SAnDReS implements machine learning methods available in the scikit-learn library. This program is available for download at https://github.com/azevedolab/sandres. SAnDReS uses crystallographic structures, binding, and thermodynamic data to create targeted scoring functions. Results: Recent applications of the program SAnDReS to drug targets such as Coagulation factor Xa, cyclin-dependent kinases, and HIV-1 protease were able to create targeted scoring functions to predict inhibition of these proteins. These targeted models outperform classical scoring functions. Conclusion: Here, we reviewed the development of machine learning scoring functions to predict binding affinity through the application of the program SAnDReS. Our studies show the superior predictive performance of the SAnDReS-developed models when compared with classical scoring functions available in the programs such as AutoDock4, Molegro Virtual Docker, and AutoDock Vina.

Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2

2020 ◽  
Vol 28 (2) ◽  
pp. 253-265 ◽  
Author(s):  
Gabriela Bitencourt-Ferreira ◽  
Amauri Duarte da Silva ◽  
Walter Filgueira de Azevedo
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Predictive Performance ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Scoring Functions ◽  
Cyclin Dependent Kinase ◽  
Learning Models ◽  
Learning Techniques ◽  
Machine Learning Models

Background: The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the development of drugs intended to modulate cellcycle progression and control. Such drugs have potential anticancer activities. Objective: Our goal here is to review recent applications of machine learning methods to predict ligand- binding affinity for protein targets. To assess the predictive performance of classical scoring functions and targeted scoring functions, we focused our analysis on CDK2 structures. Methods: We have experimental structural data for hundreds of binary complexes of CDK2 with different ligands, many of them with inhibition constant information. We investigate here computational methods to calculate the binding affinity of CDK2 through classical scoring functions and machine- learning models. Results: Analysis of the predictive performance of classical scoring functions available in docking programs such as Molegro Virtual Docker, AutoDock4, and Autodock Vina indicated that these methods failed to predict binding affinity with significant correlation with experimental data. Targeted scoring functions developed through supervised machine learning techniques showed a significant correlation with experimental data. Conclusion: Here, we described the application of supervised machine learning techniques to generate a scoring function to predict binding affinity. Machine learning models showed superior predictive performance when compared with classical scoring functions. Analysis of the computational models obtained through machine learning could capture essential structural features responsible for binding affinity against CDK2.

Optimised retinopathy of prematurity screening guideline in China based on a 5-year cohort study

2020 ◽  
pp. bjophthalmol-2020-316401
Author(s):  
Qian Yang ◽  
Xiaohong Zhou ◽  
Yingqin Ni ◽  
Haidong Shan ◽  
Wenjing Shi ◽  
...  
Keyword(s):  
Cohort Study ◽  
Retinopathy Of Prematurity ◽  
Neonatal Intensive Care ◽  
Predictive Performance ◽  
Screening Guideline ◽  
Postmenstrual Age ◽  
The Mean ◽  
Type 1 Rop ◽  
Rop Screening

PurposesTo develop an optimised retinopathy of prematurity (ROP) screening guideline by adjusting the screening schedule and thresholds of gestational age (GA) and birth weight (BW).MethodsA multicentre retrospective cohort study was conducted based on data from four tertiary neonatal intensive care units in Shanghai, China. The medical records of enrolled infants, born from 2012 to 2016 who underwent ROP examinations, were collected and analysed. The incidence and risk factors for ROP were analysed in all infants. Postnatal age (PNA) and postmenstrual age (PMA) of infants, detected to diagnose ROP for the first time, were compared with the present examination schedule. The predictive performance of screening models was evaluated by internally validating sensitivity and specificity.ResultsOf the 5606 eligible infants, ROP was diagnosed in 892 (15.9%) infants; 63 (1.1%) of them received treatment. The mean GA of ROP patients was 29.4±2.4 weeks, and the mean BW was 1260±330 g. Greater prematurity was associated with an older PNA at which ROP developed. The minimum PMA and PNA at which diagnosis of treatable ROP occurred were 32.43 and 3 weeks, respectively. The optimised criteria (GA <32 weeks or BW <1600 g) correctly predicted 98.4% type 1 ROP infants, reducing the infants requiring examinations by 43.2% when internally validated.ConclusionsThe incidence of type 1 ROP and the mean GA and BW of ROP infants have decreased in China. The suggested screening threshold and schedule may be reliably used to guide the modification of ROP screening guideline and decrease medical costs.

scholarly journals Progression to dementia in memory clinic patients with mild cognitive impairment and normal β-amyloid

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Anna Rosenberg ◽  
Alina Solomon ◽  
Vesna Jelic ◽  
Göran Hagman ◽  
Nenad Bogdanovic ◽  
...  
Keyword(s):  
Medial Temporal Lobe ◽  
Predictive Performance ◽  
Csf Biomarkers ◽  
Memory Clinic ◽  
Alzheimer Type ◽  
Normal Range ◽  
Medial Temporal Lobe Atrophy ◽  
Dementia Risk ◽  
Β Amyloid ◽  
Lobe Atrophy

Abstract Background Determination of β-amyloid (Aβ) positivity and likelihood of underlying Alzheimer’s disease (AD) relies on dichotomous biomarker cut-off values. Individuals with mild cognitive impairment (MCI) and Aβ within the normal range may still have a substantial risk of developing dementia, primarily of Alzheimer type. Their prognosis, as well as predictors of clinical progression, are not fully understood. The aim of this study was to explore the associations of cerebrospinal fluid (CSF) biomarkers (Aβ42, total tau, phosphorylated tau) and other characteristics, including modifiable vascular factors, with the risk of progression to dementia among patients with MCI and normal CSF Aβ42. Methods Three hundred eighteen memory clinic patients with CSF and clinical data, and at least 1-year follow-up, were included. Patients had normal CSF Aβ42 levels based on clinical cut-offs. Cox proportional hazard models with age as time scale and adjusted for sex, education, and cognition (Mini-Mental State Examination) were used to investigate predictors of progression to dementia and Alzheimer-type dementia. Potential predictors included CSF biomarkers, cognitive performance (verbal learning and memory), apolipoprotein E (APOE) ε4 genotype, medial temporal lobe atrophy, family history of dementia, depressive symptoms, and vascular factors, including the Cardiovascular Risk Factors, Aging and Dementia (CAIDE) risk score. Predictive performance of patient characteristics was further explored with Harrell C statistic. Results Lower normal Aβ42 and higher total tau and phosphorylated tau were associated with higher dementia risk, and the association was not driven by Aβ42 values close to cut-off. Additional predictors included poorer cognition, APOE ε4 genotype, higher systolic blood pressure, and lower body mass index, but not the CAIDE dementia risk score. Aβ42 individually and in combination with other CSF biomarkers improved the risk prediction compared to age and cognition alone. Medial temporal lobe atrophy or vascular factors did not increase the predictive performance. Conclusions Possibility of underlying AD pathology and increased dementia risk should not be ruled out among MCI patients with CSF Aβ42 within the normal range. While cut-offs may be useful in clinical practice to identify high-risk individuals, personalized risk prediction tools incorporating continuous biomarkers may be preferable among individuals with intermediate risk. The role of modifiable vascular factors could be explored in this context.

scholarly journals Wound Infections of Snakebites from the Venomous Protobothrops mucrosquamatus and Viridovipera stejnegeri in Taiwan: Bacteriology, Antibiotic Susceptibility, and Predicting the Need for Antibiotics—A BITE Study

Toxins ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 575 ◽  
Author(s):  
Chih-Chuan Lin ◽  
Yen-Chia Chen ◽  
Zhong Ning Leonard Goh ◽  
Chen-Ken Seak ◽  
Joanna Chen-Yeen Seak ◽  
...  
Keyword(s):  
Wound Infection ◽  
Roc Curve ◽  
Predictive Performance ◽  
Antibiotic Usage ◽  
Emergency Department Visits ◽  
Treatment Modalities ◽  
Wound Infections ◽  
Neutrophil Lymphocyte Ratio ◽  
Cell Counts ◽  
Freeze Dried

Snakebites from Taiwan habus (Protobothrops mucrosquamatus) and green bamboo vipers (Viridovipera stejnegeri) account for two-thirds of all venomous snakebites in Taiwan. While there has been ongoing optimization of antivenin therapy, the proper management of superimposed bacterial wound infections is not well studied. In this Bacteriology of Infections in Taiwanese snake Envenomation (BITE) study, we investigated the prevalence of wound infection, bacteriology, and corresponding antibiotic usage in patients presenting with snakebites from these two snakes. We further developed a BITE score to evaluate the probability of wound infections and guide antibiotic usage in this patient population. All snakebite victims who presented to the emergency departments of seven training and research hospitals and received at least one vial of freeze-dried hemorrhagic antivenin between January 2001 and January 2017 were identified. Patient biodata, laboratory investigation results, and treatment modalities were retrieved. We developed our BITE score via univariate and multiple logistic regression analyses. The receiver operating characteristic (ROC) curve was plotted to evaluate the predictive performance of the BITE score. Out of 8,295,497 emergency department visits, 726 patients presented with snakebites from a Taiwan habu or a green bamboo viper. The wound infection rate was 22.45%, with seven positive wound cultures, including six polymicrobial infections. Morganella morganii, Enterococcus spp., Bacteroides fragilis, and Aeromonas hydrophila were most frequently cultured. There were no positive blood cultures. A total of 33.0% (n = 106) of snakebite patients who received prophylactic antibiotics nevertheless developed wound infections, while 44.8% (n = 73) of wound infection patients were satisfactorily treated with one of the following antibiotics: amoxicillin/clavulanic acid, oxacillin, cefazolin, and ampicillin/sulbactam. With the addition of gentamicin, the success of antibiotic therapy increased by up to 66.54%. The prognostic factors for the secondary bacterial infection of snakebites were white blood cell counts, the neutrophil lymphocyte ratio, and the need for hospital admission. The area under the ROC curve for the BITE score was 0.839. At the optimal cut-off point of 5, the BITE score had a 79.58% accuracy, 82.31% sensitivity, and 79.71% specificity when predicting infection in snakebite patients. Our BITE score may help with antibiotic stewardship by guiding appropriate antibiotic use in patients presenting with snakebites. It may also be employed in further studies into antibiotic prophylaxis in snakebite patients for the prevention of superimposed bacterial wound infections.

scholarly journals Prediction of Incident Cancers in the Lifelines Population-Based Cohort

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2133
Author(s):  
Francisco O. Cortés-Ibañez ◽  
Sunil Belur Nagaraj ◽  
Ludo Cornelissen ◽  
Gerjan J. Navis ◽  
Bert van der Vegt ◽  
...  
Keyword(s):  
Cancer Incidence ◽  
Binary Classification ◽  
Predictive Performance ◽  
Population Based ◽  
Support Vector ◽  
Clinical Variables ◽  
Incident Cancer ◽  
History Of ◽  
Diagnosis Of Cancer ◽  
Auc Value

Cancer incidence is rising, and accurate prediction of incident cancers could be relevant to understanding and reducing cancer incidence. The aim of this study was to develop machine learning (ML) models that could predict an incident diagnosis of cancer. Participants without any history of cancer within the Lifelines population-based cohort were followed for a median of 7 years. Data were available for 116,188 cancer-free participants and 4232 incident cancer cases. At baseline, socioeconomic, lifestyle, and clinical variables were assessed. The main outcome was an incident cancer during follow-up (excluding skin cancer), based on linkage with the national pathology registry. The performance of three ML algorithms was evaluated using supervised binary classification to identify incident cancers among participants. Elastic net regularization and Gini index were used for variables selection. An overall area under the receiver operator curve (AUC) <0.75 was obtained, the highest AUC value was for prostate cancer (random forest AUC = 0.82 (95% CI 0.77–0.87), logistic regression AUC = 0.81 (95% CI 0.76–0.86), and support vector machines AUC = 0.83 (95% CI 0.78–0.88), respectively); age was the most important predictor in these models. Linear and non-linear ML algorithms including socioeconomic, lifestyle, and clinical variables produced a moderate predictive performance of incident cancers in the Lifelines cohort.

Sign in / Sign up

Export Citation Format

Share Document