Development of childhood asthma prediction models using machine learning approaches

Background: Wheeze is common in early life and often transient. It is difficult to identify which children will experience persistent symptoms and subsequently develop asthma. Machine learning approaches have the potential for better predictive performance and generalisability over existing childhood asthma prediction models. Objective: To apply machine learning approaches for predicting school-age asthma (age 10) in early life (Childhood Asthma Prediction in Early life, CAPE model) and at preschool age (Childhood Asthma Prediction at Preschool age, CAPP model). Methods: Data on clinical symptoms and environmental exposures were collected from children enrolled in the Isle of Wight Birth Cohort (N=1368, ~15% asthma prevalence). Recursive Feature Elimination (RFE) identified the optimal subset of features predictive of school-age asthma for each model. Seven state-of-the-art machine learning classification algorithms were used to develop the models and the results were compared. To optimize the models, training was performed by applying 5-fold cross-validation, imputation and resampling. Predictive performances were evaluated on the test set and externally validated in the Manchester Asthma and Allergy Study (MAAS) cohort. Results: RFE identified eight and 12 predictors for the CAPE and CAPP models, respectively. The best predictive performance was demonstrated by a Support Vector Machine (SVM) algorithm for both the CAPE model (area under the receiver operating curve, AUC=0.71) and CAPP model (AUC=0.82). Both models demonstrated good generalisability in MAAS (CAPE 8YR=0.71, 11YR=0.71, CAPP 8YR=0.83, 11YR=0.79). Conclusion: Using machine learning approaches improved upon the predictive performance of existing regression-based models, with good generalisability and ability to rule in asthma.

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Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

10.31234/osf.io/eqcdx ◽

2019 ◽

Author(s):

Oskar Flygare ◽

Jesper Enander ◽

Erik Andersson ◽

Brjánn Ljótsson ◽

Volen Z Ivanov ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Random Forests ◽

Clinical Utility ◽

Body Dysmorphic Disorder ◽

Prediction Models ◽

Behavioral Therapy ◽

Learning Approach ◽

Learning Approaches ◽

Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

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The future is coming: promising perspectives regarding the use of machine learning in renal transplantation

Brazilian Journal of Nephrology ◽

10.1590/2175-8239-jbn-2018-0047 ◽

2019 ◽

Vol 41 (2) ◽

pp. 284-287

Author(s):

Pedro Guilherme Coelho Hannun ◽

Luis Gustavo Modelli de Andrade

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Graft Function ◽

Statistical Technique ◽

Daily Routine ◽

Learning Approaches ◽

Post Transplantation ◽

Chronic Allograft Rejection ◽

Institutional Experience ◽

Learning Principles

Abstract Introduction: The prediction of post transplantation outcomes is clinically important and involves several problems. The current prediction models based on standard statistics are very complex, difficult to validate and do not provide accurate prediction. Machine learning, a statistical technique that allows the computer to make future predictions using previous experiences, is beginning to be used in order to solve these issues. In the field of kidney transplantation, computational forecasting use has been reported in prediction of chronic allograft rejection, delayed graft function, and graft survival. This paper describes machine learning principles and steps to make a prediction and performs a brief analysis of the most recent applications of its application in literature. Discussion: There is compelling evidence that machine learning approaches based on donor and recipient data are better in providing improved prognosis of graft outcomes than traditional analysis. The immediate expectations that emerge from this new prediction modelling technique are that it will generate better clinical decisions based on dynamic and local practice data and optimize organ allocation as well as post transplantation care management. Despite the promising results, there is no substantial number of studies yet to determine feasibility of its application in a clinical setting. Conclusion: The way we deal with storage data in electronic health records will radically change in the coming years and machine learning will be part of clinical daily routine, whether to predict clinical outcomes or suggest diagnosis based on institutional experience.

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Machine Learning Methods Applied to the Prediction of Pseudo-nitzschia spp. Blooms in the Galician Rias Baixas (NW Spain)

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10040199 ◽

2021 ◽

Vol 10 (4) ◽

pp. 199

Author(s):

Francisco M. Bellas Aláez ◽

Jesus M. Torres Palenzuela ◽

Evangelos Spyrakos ◽

Luis González Vilas

Keyword(s):

Machine Learning ◽

Performance Metrics ◽

Prediction Models ◽

Support Vector ◽

False Alarms ◽

Learning Approaches ◽

Learning Methods ◽

Machine Learning Methods ◽

Rías Baixas ◽

New Algorithms

This work presents new prediction models based on recent developments in machine learning methods, such as Random Forest (RF) and AdaBoost, and compares them with more classical approaches, i.e., support vector machines (SVMs) and neural networks (NNs). The models predict Pseudo-nitzschia spp. blooms in the Galician Rias Baixas. This work builds on a previous study by the authors (doi.org/10.1016/j.pocean.2014.03.003) but uses an extended database (from 2002 to 2012) and new algorithms. Our results show that RF and AdaBoost provide better prediction results compared to SVMs and NNs, as they show improved performance metrics and a better balance between sensitivity and specificity. Classical machine learning approaches show higher sensitivities, but at a cost of lower specificity and higher percentages of false alarms (lower precision). These results seem to indicate a greater adaptation of new algorithms (RF and AdaBoost) to unbalanced datasets. Our models could be operationally implemented to establish a short-term prediction system.

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Forecasting the risk at infractions: an ensemble comparison of machine learning approach

Industrial Management & Data Systems ◽

10.1108/imds-10-2020-0603 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Lei Li ◽

Desheng Wu

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Short Term Memory ◽

Model Performance ◽

Large Data ◽

Support Vector ◽

Learning Approaches ◽

Content Type ◽

Day To Day Operations ◽

Prediction Approach

PurposeThe infraction of securities regulations (ISRs) of listed firms in their day-to-day operations and management has become one of common problems. This paper proposed several machine learning approaches to forecast the risk at infractions of listed corporates to solve financial problems that are not effective and precise in supervision.Design/methodology/approachThe overall proposed research framework designed for forecasting the infractions (ISRs) include data collection and cleaning, feature engineering, data split, prediction approach application and model performance evaluation. We select Logistic Regression, Naïve Bayes, Random Forest, Support Vector Machines, Artificial Neural Network and Long Short-Term Memory Networks (LSTMs) as ISRs prediction models.FindingsThe research results show that prediction performance of proposed models with the prior infractions provides a significant improvement of the ISRs than those without prior, especially for large sample set. The results also indicate when judging whether a company has infractions, we should pay attention to novel artificial intelligence methods, previous infractions of the company, and large data sets.Originality/valueThe findings could be utilized to address the problems of identifying listed corporates' ISRs at hand to a certain degree. Overall, results elucidate the value of the prior infraction of securities regulations (ISRs). This shows the importance of including more data sources when constructing distress models and not only focus on building increasingly more complex models on the same data. This is also beneficial to the regulatory authorities.

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Machine Learning Frameworks in Cancer Detection

E3S Web of Conferences ◽

10.1051/e3sconf/202129701073 ◽

2021 ◽

Vol 297 ◽

pp. 01073

Author(s):

Sabyasachi Pramanik ◽

K. Martin Sagayam ◽

Om Prakash Jena

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Cancer Development ◽

Support Vector ◽

Learning Approaches ◽

Learning Techniques ◽

Fact Finding ◽

Risk Of Cancer

Cancer has been described as a diverse illness with several distinct subtypes that may occur simultaneously. As a result, early detection and forecast of cancer types have graced essentially in cancer fact-finding methods since they may help to improve the clinical treatment of cancer survivors. The significance of categorizing cancer suffers into higher or lower-threat categories has prompted numerous fact-finding associates from the bioscience and genomics field to investigate the utilization of machine learning (ML) algorithms in cancer diagnosis and treatment. Because of this, these methods have been used with the goal of simulating the development and treatment of malignant diseases in humans. Furthermore, the capacity of machine learning techniques to identify important characteristics from complicated datasets demonstrates the significance of these technologies. These technologies include Bayesian networks and artificial neural networks, along with a number of other approaches. Decision Trees and Support Vector Machines which have already been extensively used in cancer research for the creation of predictive models, also lead to accurate decision making. The application of machine learning techniques may undoubtedly enhance our knowledge of cancer development; nevertheless, a sufficient degree of validation is required before these approaches can be considered for use in daily clinical practice. An overview of current machine learning approaches utilized in the simulation of cancer development is presented in this paper. All of the supervised machine learning approaches described here, along with a variety of input characteristics and data samples, are used to build the prediction models. In light of the increasing trend towards the use of machine learning methods in biomedical research, we offer the most current papers that have used these approaches to predict risk of cancer or patient outcomes in order to better understand cancer.

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An integrated review on machine learning approaches for heart disease prediction: Direction towards future research gaps

Bio-Algorithms and Med-Systems ◽

10.1515/bams-2020-0069 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Fathima Aliyar Vellameeran ◽

Thomas Brindha

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Prediction Models ◽

Machine Learning Algorithms ◽

Future Research ◽

Disease Prediction ◽

Learning Approaches ◽

Research Papers ◽

Using Data ◽

Intelligent Methods

Abstract Objectives To make a clear literature review on state-of-the-art heart disease prediction models. Methods It reviews 61 research papers and states the significant analysis. Initially, the analysis addresses the contributions of each literature works and observes the simulation environment. Here, different types of machine learning algorithms deployed in each contribution. In addition, the utilized dataset for existing heart disease prediction models was observed. Results The performance measures computed in entire papers like prediction accuracy, prediction error, specificity, sensitivity, f-measure, etc., are learned. Further, the best performance is also checked to confirm the effectiveness of entire contributions. Conclusions The comprehensive research challenges and the gap are portrayed based on the development of intelligent methods concerning the unresolved challenges in heart disease prediction using data mining techniques.

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Artificial intelligence, machine learning, and deep learning for clinical outcome prediction

Emerging Topics in Life Sciences ◽

10.1042/etls20210246 ◽

2021 ◽

Author(s):

Rowland W. Pettit ◽

Robert Fullem ◽

Chao Cheng ◽

Christopher I. Amos

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Clinical Outcome ◽

Clinical Outcomes ◽

Prediction Models ◽

Learning Approaches ◽

Growth Machine ◽

Broad Concept ◽

Tree Methods ◽

Health Care Records

AI is a broad concept, grouping initiatives that use a computer to perform tasks that would usually require a human to complete. AI methods are well suited to predict clinical outcomes. In practice, AI methods can be thought of as functions that learn the outcomes accompanying standardized input data to produce accurate outcome predictions when trialed with new data. Current methods for cleaning, creating, accessing, extracting, augmenting, and representing data for training AI clinical prediction models are well defined. The use of AI to predict clinical outcomes is a dynamic and rapidly evolving arena, with new methods and applications emerging. Extraction or accession of electronic health care records and combining these with patient genetic data is an area of present attention, with tremendous potential for future growth. Machine learning approaches, including decision tree methods of Random Forest and XGBoost, and deep learning techniques including deep multi-layer and recurrent neural networks, afford unique capabilities to accurately create predictions from high dimensional, multimodal data. Furthermore, AI methods are increasing our ability to accurately predict clinical outcomes that previously were difficult to model, including time-dependent and multi-class outcomes. Barriers to robust AI-based clinical outcome model deployment include changing AI product development interfaces, the specificity of regulation requirements, and limitations in ensuring model interpretability, generalizability, and adaptability over time.

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Prediction of Fluid Viscosity in Multiphase Reservoir Oil System by Machine Learning

Geofluids ◽

10.1155/2021/3223530 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Lihua Shao ◽

Ru Ji ◽

Shuyi Du ◽

Hongqing Song

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Computing Time ◽

Oil Production ◽

Petroleum Engineering ◽

Molar Ratio ◽

Fluid Viscosity ◽

Learning Approaches ◽

Accuracy Comparison ◽

Comparison Results

It is important to realize rapid and accurate prediction of fluid viscosity in a multiphase reservoir oil system for improving oil production in petroleum engineering. This study proposed three viscosity prediction models based on machine learning approaches. The prediction accuracy comparison results show that the random forest (RF) model performs accurately in predicting the viscosity of each phase of the reservoir, with the lowest error percentage and highest R 2 values. And the RF model is tremendously fast in a computing time of 0.53 s. In addition, sensitivity analysis indicates that for a multiphase reservoir system, the viscosity of each phase of the reservoir is determined by different factors. Among them, the viscosity of oil is vital for oil production, which is mainly affected by the molar ratio of gas to oil (MR-GO).

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How accurate are suicide risk prediction models? Asking the right questions for clinical practice

Evidence-Based Mental Health ◽

10.1136/ebmental-2019-300102 ◽

2019 ◽

Vol 22 (3) ◽

pp. 125-128 ◽

Cited By ~ 8

Author(s):

Daniel Whiting ◽

Seena Fazel

Keyword(s):

Machine Learning ◽

Clinical Practice ◽

Suicide Risk ◽

Potential Role ◽

Prediction Models ◽

Learning Approaches ◽

Risk Prediction Models ◽

Self Harm ◽

The Right

Prediction models assist in stratifying and quantifying an individual’s risk of developing a particular adverse outcome, and are widely used in cardiovascular and cancer medicine. Whether these approaches are accurate in predicting self-harm and suicide has been questioned. We searched for systematic reviews in the suicide risk assessment field, and identified three recent reviews that have examined current tools and models derived using machine learning approaches. In this clinical review, we present a critical appraisal of these reviews, and highlight three major limitations that are shared between them. First, structured tools are not compared with unstructured assessments routine in clinical practice. Second, they do not sufficiently consider a range of performance measures, including negative predictive value and calibration. Third, the potential role of these models as clinical adjuncts is not taken into consideration. We conclude by presenting the view that the current role of prediction models for self-harm and suicide is currently not known, and discuss some methodological issues and implications of some machine learning and other analytic techniques for clinical utility.

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