scholarly journals Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Constanza L. Andaur Navarro ◽  
Johanna A. A. Damen ◽  
Toshihiko Takada ◽  
Steven W. J. Nijman ◽  
Paula Dhiman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Prediction Model ◽  
Prediction Models ◽  
Supervised Machine Learning ◽  
Model Specification ◽  
Essential Information ◽  
Model Studies ◽  
Completeness Of Reporting ◽  
Complete Reporting

Abstract Background While many studies have consistently found incomplete reporting of regression-based prediction model studies, evidence is lacking for machine learning-based prediction model studies. We aim to systematically review the adherence of Machine Learning (ML)-based prediction model studies to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement. Methods We included articles reporting on development or external validation of a multivariable prediction model (either diagnostic or prognostic) developed using supervised ML for individualized predictions across all medical fields. We searched PubMed from 1 January 2018 to 31 December 2019. Data extraction was performed using the 22-item checklist for reporting of prediction model studies (www.TRIPOD-statement.org). We measured the overall adherence per article and per TRIPOD item. Results Our search identified 24,814 articles, of which 152 articles were included: 94 (61.8%) prognostic and 58 (38.2%) diagnostic prediction model studies. Overall, articles adhered to a median of 38.7% (IQR 31.0–46.4%) of TRIPOD items. No article fully adhered to complete reporting of the abstract and very few reported the flow of participants (3.9%, 95% CI 1.8 to 8.3), appropriate title (4.6%, 95% CI 2.2 to 9.2), blinding of predictors (4.6%, 95% CI 2.2 to 9.2), model specification (5.2%, 95% CI 2.4 to 10.8), and model’s predictive performance (5.9%, 95% CI 3.1 to 10.9). There was often complete reporting of source of data (98.0%, 95% CI 94.4 to 99.3) and interpretation of the results (94.7%, 95% CI 90.0 to 97.3). Conclusion Similar to prediction model studies developed using conventional regression-based techniques, the completeness of reporting is poor. Essential information to decide to use the model (i.e. model specification and its performance) is rarely reported. However, some items and sub-items of TRIPOD might be less suitable for ML-based prediction model studies and thus, TRIPOD requires extensions. Overall, there is an urgent need to improve the reporting quality and usability of research to avoid research waste. Systematic review registration PROSPERO, CRD42019161764.

scholarly journals Completeness of reporting of clinical prediction models developed using supervised machine learning: A systematic review

2021 ◽  
Author(s):  
Constanza L Andaur Navarro ◽  
Johanna AA Damen ◽  
Toshihiko Takada ◽  
Steven WJ Nijman ◽  
Paula Dhiman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Prediction Models ◽  
External Validation ◽  
Supervised Machine Learning ◽  
Model Specification ◽  
Essential Information ◽  
Model Studies ◽  
Completeness Of Reporting ◽  
Complete Reporting

ABSTRACT Objective. While many studies have consistently found incomplete reporting of regression-based prediction model studies, evidence is lacking for machine learning-based prediction model studies. Our aim is to systematically review the adherence of Machine Learning (ML)-based prediction model studies to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement. Study design and setting: We included articles reporting on development or external validation of a multivariable prediction model (either diagnostic or prognostic) developed using supervised ML for individualized predictions across all medical fields (PROSPERO, CRD42019161764). We searched PubMed from 1 January 2018 to 31 December 2019. Data extraction was performed using the 22-item checklist for reporting of prediction model studies (www.TRIPOD-statement.org). We measured the overall adherence per article and per TRIPOD item. Results: Our search identified 24 814 articles, of which 152 articles were included: 94 (61.8%) prognostic and 58 (38.2%) diagnostic prediction model studies. Overall, articles adhered to a median of 38.7% (IQR 31.0-46.4) of TRIPOD items. No articles fully adhered to complete reporting of the abstract and very few reported the flow of participants (3.9%, 95% CI 1.8 to 8.3), appropriate title (4.6%, 95% CI 2.2 to 9.2), blinding of predictors (4.6%, 95% CI 2.2 to 9.2), model specification (5.2%, 95% CI 2.4 to 10.8), and model's predictive performance (5.9%, 95% CI 3.1 to 10.9). There was often complete reporting of source of data (98.0%, 95% CI 94.4 to 99.3) and interpretation of the results (94.7%, 95% CI 90.0 to 97.3). Conclusion. Similar to studies using conventional statistical techniques, the completeness of reporting is poor. Essential information to decide to use the model (i.e. model specification and its performance) is rarely reported. However, some items and sub-items of TRIPOD might be less suitable for ML-based prediction model studies and thus, TRIPOD requires extensions. Overall, there is an urgent need to improve the reporting quality and usability of research to avoid research waste.

scholarly journals Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques

BMJ Open ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. e038832
Author(s):  
Constanza L Andaur Navarro ◽  
Johanna A A G Damen ◽  
Toshihiko Takada ◽  
Steven W J Nijman ◽  
Paula Dhiman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Prediction Model ◽  
Prediction Models ◽  
Reporting Quality ◽  
Risk Of Bias ◽  
Machine Learning Techniques ◽  
Published Data ◽  
Model Studies

IntroductionStudies addressing the development and/or validation of diagnostic and prognostic prediction models are abundant in most clinical domains. Systematic reviews have shown that the methodological and reporting quality of prediction model studies is suboptimal. Due to the increasing availability of larger, routinely collected and complex medical data, and the rising application of Artificial Intelligence (AI) or machine learning (ML) techniques, the number of prediction model studies is expected to increase even further. Prediction models developed using AI or ML techniques are often labelled as a ‘black box’ and little is known about their methodological and reporting quality. Therefore, this comprehensive systematic review aims to evaluate the reporting quality, the methodological conduct, and the risk of bias of prediction model studies that applied ML techniques for model development and/or validation.Methods and analysisA search will be performed in PubMed to identify studies developing and/or validating prediction models using any ML methodology and across all medical fields. Studies will be included if they were published between January 2018 and December 2019, predict patient-related outcomes, use any study design or data source, and available in English. Screening of search results and data extraction from included articles will be performed by two independent reviewers. The primary outcomes of this systematic review are: (1) the adherence of ML-based prediction model studies to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD), and (2) the risk of bias in such studies as assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). A narrative synthesis will be conducted for all included studies. Findings will be stratified by study type, medical field and prevalent ML methods, and will inform necessary extensions or updates of TRIPOD and PROBAST to better address prediction model studies that used AI or ML techniques.Ethics and disseminationEthical approval is not required for this study because only available published data will be analysed. Findings will be disseminated through peer-reviewed publications and scientific conferences.Systematic review registrationPROSPERO, CRD42019161764.

scholarly journals Multivariable Prediction Models for Health Care Spending Using Machine Learning: a Protocol of a Systematic Review

2021 ◽  
Author(s):  
Andrew W. Huang ◽  
Martin Haslberger ◽  
Neto Coulibaly ◽  
Omar Galárraga ◽  
Arman Oganisian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Health Care ◽  
Prediction Model ◽  
Prediction Models ◽  
Risk Of Bias ◽  
Population Characteristics ◽  
Health Care Spending ◽  
Successful Implementation ◽  
Individual Level

Abstract Background With rising cost pressures on health care systems, machine-learning (ML) based algorithms are increasingly used to predict health care costs. Despite their potential advantages, the successful implementation of these methods could be undermined by biases introduced in the design, conduct, or analysis of studies seeking to develop and/or validate ML models. The utility of such models may also be negatively affected by poor reporting of these studies. In this systematic review, we aim to evaluate the reporting quality, methodological characteristics, and risk of bias of ML-based prediction models for individual-level health care spending. Methods We will systematically search PubMed and Embase to identify studies developing, updating, or validating ML-based models to predict an individual’s health care spending for any medical condition, over any time period, and in any setting. We will exclude prediction models of aggregate-level health care spending, models used to infer causality, models using radiomics or speech parameters, models of non-clinically validated predictors (e.g. genomics), and cost-effectiveness analyses without predicting individual-level health care spending. We will extract data based on the CHARMS checklist, previously published research, and relevant recommendations. We will assess the adherence of ML-based studies to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) and examine the inclusion of transparency and reproducibility indicators (e.g. statements on data sharing). To assess the risk of bias, we will apply the Prediction model Risk Of Bias Assessment Tool (PROBAST). Findings will be stratified by study design, ML methods used, population characteristics, and medical field. Discussion Our systematic review will appraise the quality, reporting, and risk of bias of ML-based models for individualized health care cost prediction. This review will provide an overview of the available models and give insights into the strengths and limitations of using ML methods for the prediction of health spending. Trial registration: Not applicable.

scholarly journals Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review

2021 ◽  
Vol 18 (9) ◽  
pp. 4499
Author(s):  
Anil Babu Payedimarri ◽  
Diego Concina ◽  
Luigi Portinale ◽  
Massimo Canonico ◽  
Deborah Seys ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Public Transportation ◽  
Prediction Models ◽  
Positive Impact ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Commercial Activities

Artificial Intelligence (AI) and Machine Learning (ML) have expanded their utilization in different fields of medicine. During the SARS-CoV-2 outbreak, AI and ML were also applied for the evaluation and/or implementation of public health interventions aimed to flatten the epidemiological curve. This systematic review aims to evaluate the effectiveness of the use of AI and ML when applied to public health interventions to contain the spread of SARS-CoV-2. Our findings showed that quarantine should be the best strategy for containing COVID-19. Nationwide lockdown also showed positive impact, whereas social distancing should be considered to be effective only in combination with other interventions including the closure of schools and commercial activities and the limitation of public transportation. Our findings also showed that all the interventions should be initiated early in the pandemic and continued for a sustained period. Despite the study limitation, we concluded that AI and ML could be of help for policy makers to define the strategies for containing the COVID-19 pandemic.

Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review

2021 ◽  
Author(s):  
Nghia H Nguyen ◽  
Dominic Picetti ◽  
Parambir S Dulai ◽  
Vipul Jairath ◽  
William J Sandborn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Risk Prediction ◽  
Statistical Models ◽  
Prediction Models ◽  
Risk Of Bias ◽  
Learning Models ◽  
Bowel Diseases ◽  
Inflammatory Bowel ◽  
Machine Learning Models

Abstract Background and Aims There is increasing interest in machine learning-based prediction models in inflammatory bowel diseases (IBD). We synthesized and critically appraised studies comparing machine learning vs. traditional statistical models, using routinely available clinical data for risk prediction in IBD. Methods Through a systematic review till January 1, 2021, we identified cohort studies that derived and/or validated machine learning models, based on routinely collected clinical data in patients with IBD, to predict the risk of harboring or developing adverse clinical outcomes, and reported its predictive performance against a traditional statistical model for the same outcome. We appraised the risk of bias in these studies using the Prediction model Risk of Bias ASsessment (PROBAST) tool. Results We included 13 studies on machine learning-based prediction models in IBD encompassing themes of predicting treatment response to biologics and thiopurines, predicting longitudinal disease activity and complications and outcomes in patients with acute severe ulcerative colitis. The most common machine learnings models used were tree-based algorithms, which are classification approaches achieved through supervised learning. Machine learning models outperformed traditional statistical models in risk prediction. However, most models were at high risk of bias, and only one was externally validated. Conclusions Machine learning-based prediction models based on routinely collected data generally perform better than traditional statistical models in risk prediction in IBD, though frequently have high risk of bias. Future studies examining these approaches are warranted, with special focus on external validation and clinical applicability.

scholarly journals Development of Heavy Rain Damage Prediction Model Using Machine Learning Based on Big Data

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Changhyun Choi ◽  
Jeonghwan Kim ◽  
Jongsung Kim ◽  
Donghyun Kim ◽  
Younghye Bae ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Prediction Model ◽  
Prediction Models ◽  
Meteorological Data ◽  
Heavy Rain ◽  
Machine Learning Techniques ◽  
Damage Prediction ◽  
Explanatory Variables ◽  
The Republic

Prediction models of heavy rain damage using machine learning based on big data were developed for the Seoul Capital Area in the Republic of Korea. We used data on the occurrence of heavy rain damage from 1994 to 2015 as dependent variables and weather big data as explanatory variables. The model was developed by applying machine learning techniques such as decision trees, bagging, random forests, and boosting. As a result of evaluating the prediction performance of each model, the AUC value of the boosting model using meteorological data from the past 1 to 4 days was the highest at 95.87% and was selected as the final model. By using the prediction model developed in this study to predict the occurrence of heavy rain damage for each administrative region, we can greatly reduce the damage through proactive disaster management.

scholarly journals Polygenic risk prediction models for colorectal cancer: a systematic review

BMC Cancer ◽  
2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Michele Sassano ◽  
Marco Mariani ◽  
Gianluigi Quaranta ◽  
Roberta Pastorino ◽  
Stefania Boccia
Keyword(s):  
Colorectal Cancer ◽  
Risk Factors ◽  
Systematic Review ◽  
Prediction Model ◽  
Risk Prediction ◽  
Genetic Variants ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Discriminatory Accuracy ◽  
Traditional Risk Factors

Abstract Background Risk prediction models incorporating single nucleotide polymorphisms (SNPs) could lead to individualized prevention of colorectal cancer (CRC). However, the added value of incorporating SNPs into models with only traditional risk factors is still not clear. Hence, our primary aim was to summarize literature on risk prediction models including genetic variants for CRC, while our secondary aim was to evaluate the improvement of discriminatory accuracy when adding SNPs to a prediction model with only traditional risk factors. Methods We conducted a systematic review on prediction models incorporating multiple SNPs for CRC risk prediction. We tested whether a significant trend in the increase of Area Under Curve (AUC) according to the number of SNPs could be observed, and estimated the correlation between AUC improvement and number of SNPs. We estimated pooled AUC improvement for SNP-enhanced models compared with non-SNP-enhanced models using random effects meta-analysis, and conducted meta-regression to investigate the association of specific factors with AUC improvement. Results We included 33 studies, 78.79% using genetic risk scores to combine genetic data. We found no significant trend in AUC improvement according to the number of SNPs (p for trend = 0.774), and no correlation between the number of SNPs and AUC improvement (p = 0.695). Pooled AUC improvement was 0.040 (95% CI: 0.035, 0.045), and the number of cases in the study and the AUC of the starting model were inversely associated with AUC improvement obtained when adding SNPs to a prediction model. In addition, models constructed in Asian individuals achieved better AUC improvement with the incorporation of SNPs compared with those developed among individuals of European ancestry. Conclusions Though not conclusive, our results provide insights on factors influencing discriminatory accuracy of SNP-enhanced models. Genetic variants might be useful to inform stratified CRC screening in the future, but further research is needed.

scholarly journals Machine Learning-Based Prediction Model for Papillary Thyroid Carcinoma Recurrence

2020 ◽  
Author(s):  
Young Min Park ◽  
Byung-Joo Lee
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Tumor Size ◽  
Large Scale ◽  
Prediction Models ◽  
Prognostic Significance ◽  
Disease Recurrence ◽  
Machine Learning Techniques ◽  
Papillary Thyroid ◽  
Recurrence Prediction

Abstract Background: This study analyzed the prognostic significance of nodal factors, including the number of metastatic LNs and LNR, in patients with PTC, and attempted to construct a disease recurrence prediction model using machine learning techniques.Methods: We retrospectively analyzed clinico-pathologic data from 1040 patients diagnosed with papillary thyroid cancer between 2003 and 2009. Results: We analyzed clinico-pathologic factors related to recurrence through logistic regression analysis. Among the factors that we included, only sex and tumor size were significantly correlated with disease recurrence. Parameters such as age, sex, tumor size, tumor multiplicity, ETE, ENE, pT, pN, ipsilateral central LN metastasis, contralateral central LNs metastasis, number of metastatic LNs, and LNR were input for construction of a machine learning prediction model. The performance of five machine learning models related to recurrence prediction was compared based on accuracy. The Decision Tree model showed the best accuracy at 95%, and the lightGBM and stacking model together showed 93% accuracy. Conclusions: We confirmed that all machine learning prediction models showed an accuracy of 90% or more for predicting disease recurrence in PTC. Large-scale multicenter clinical studies should be performed to improve the performance of our prediction models and verify their clinical effectiveness.

scholarly journals Risk Prediction Models for Patients With Acute Heart Failure: A Protocol for Systematic Review, Critical Appraisal, and Meta-Analysis

2021 ◽  
Author(s):  
Xuecheng Zhang ◽  
Kehua Zhou ◽  
Jingjing Zhang ◽  
Ying Chen ◽  
Hengheng Dai ◽  
...  
Keyword(s):  
Heart Failure ◽  
Systematic Review ◽  
Prediction Model ◽  
Acute Heart Failure ◽  
Risk Prediction ◽  
Methodological Quality ◽  
Critical Appraisal ◽  
Prediction Models ◽  
Cochrane Library ◽  
Risk Prediction Models

Abstract Background Nearly a third of patients with acute heart failure (AHF) die or are readmitted within three months after discharge, accounting for the majority of costs associated with heart failure-related care. A considerable number of risk prediction models, which predict outcomes for mortality and readmission rates, have been developed and validated for patients with AHF. These models could help clinicians stratify patients by risk level and improve decision making, and provide specialist care and resources directed to high-risk patients. However, clinicians sometimes reluctant to utilize these models, possibly due to their poor reliability, the variety of models, and/or the complexity of statistical methodologies. Here, we describe a protocol to systematically review extant risk prediction models. We will describe characteristics, compare performance, and critically appraise the reporting transparency and methodological quality of risk prediction models for AHF patients. Method Embase, Pubmed, Web of Science, and the Cochrane Library will be searched from their inception onwards. A back word will be searched on derivation studies to find relevant external validation studies. Multivariable prognostic models used for AHF and mortality and/or readmission rate will be eligible for review. Two reviewers will conduct title and abstract screening, full-text review, and data extraction independently. Included models will be summarized qualitatively and quantitatively. We will also provide an overview of critical appraisal of the methodological quality and reporting transparency of included studies using the Prediction model Risk of Bias Assessment Tool(PROBAST tool) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis(TRIPOD statement). Discussion The result of the systematic review could help clinicians better understand and use the prediction models for AHF patients, as well as make standardized decisions about more precise, risk-adjusted management. Systematic review registration : PROSPERO registration number CRD42021256416.

scholarly journals Machine Learning Frameworks in Cancer Detection

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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