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Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review
Background
A comprehensive overview of artificial intelligence (AI) for cardiovascular disease (CVD) prediction and a screening tool of AI models (AI-Ms) for independent external validation are lacking. This systematic review aims to identify, describe, and appraise AI-Ms of CVD prediction in the general and special populations and develop a new independent validation score (IVS) for AI-Ms replicability evaluation.
Methods
PubMed, Web of Science, Embase, and IEEE library were searched up to July 2021. Data extraction and analysis were performed for the populations, distribution, predictors, algorithms, etc. The risk of bias was evaluated with the prediction risk of bias assessment tool (PROBAST). Subsequently, we designed IVS for model replicability evaluation with five steps in five items, including transparency of algorithms, performance of models, feasibility of reproduction, risk of reproduction, and clinical implication, respectively. The review is registered in PROSPERO (No. CRD42021271789).
Results
In 20,887 screened references, 79 articles (82.5% in 2017–2021) were included, which contained 114 datasets (67 in Europe and North America, but 0 in Africa). We identified 486 AI-Ms, of which the majority were in development (
n
= 380), but none of them had undergone independent external validation. A total of 66 idiographic algorithms were found; however, 36.4% were used only once and only 39.4% over three times. A large number of different predictors (range 5–52,000, median 21) and large-span sample size (range 80–3,660,000, median 4466) were observed. All models were at high risk of bias according to PROBAST, primarily due to the incorrect use of statistical methods. IVS analysis confirmed only 10 models as “recommended”; however, 281 and 187 were “not recommended” and “warning,” respectively.
Conclusion
AI has led the digital revolution in the field of CVD prediction, but is still in the early stage of development as the defects of research design, report, and evaluation systems. The IVS we developed may contribute to independent external validation and the development of this field.
Journal Article
Reliability of Moyer’s and Tanaka Johnston’s prediction methods in a non-Caucasian heterogeneous population – a cross-sectional study
Introduction : Mixed dentition analyses are used to determine possible tooth-size and arch-length discrepancies during the transition from primary to permanent dentition. Prediction methods using a probability table or linear regression equation use the sum of the mesiodistal widths of mandibular permanent incisors to predict the mesiodistal width of unerupted permanent teeth. Racial and sexual variations and sexual dimorphism in tooth size have been reported. The objective of this study is to validate Moyer’s and Tanaka Johnston’s mixed dentition analyses in a contemporary South Indian population. Materials and methods : 100 pairs of permanent dentition models belonging equally to both sexes with an age range of 12–21 years comprised the sample in which both analyses were done. The predicted width of permanent canines and premolars was compared to the actual width in the study models. Results : There was a statistically significant difference between the two values for Moyer’s analysis in the mandibular teeth of females ( p =0.04), 95% CI −0.605 to −0.969. There was a statistically significant difference between the two values for Tanaka Johnston’s analysis of maxillary teeth ( p =0.001), 95% CI 0.863 to 1.370. Conclusions : Moyer’s analysis shows a statistically significant underestimation in the mandibular arch of females. Tanaka Johston’s analysis shows a statistically highly significant overestimation in the maxilla. Both analyses cannot be reliably applied to the South Indian population.
Journal Article
Accident and hazard prediction models for highway–rail grade crossings: a state-of-the-practice review for the USA
Highway–rail grade crossings (HRGCs) are one of the most dangerous segments of the transportation network. Every year numerous accidents are recorded at HRGCs between highway users and trains, between highway users and traffic control devices, and solely between highway users. These accidents cause fatalities, severe injuries, property damage, and release of hazardous materials. Researchers and state Departments of Transportation (DOTs) have addressed safety concerns at HRGCs in the USA by investigating the factors that may cause accidents at HRGCs and developed certain accident and hazard prediction models to forecast the occurrence of accidents and crossing vulnerability. The accident and hazard prediction models are used to identify the most hazardous HRGCs that require safety improvements. This study provides an extensive review of the state-of-the-practice to identify the existing accident and hazard prediction formulae that have been used over the years by different state DOTs. Furthermore, this study analyzes the common factors that have been considered in the existing accident and hazard prediction formulae. The reported performance and implementation challenges of the identified accident and hazard prediction formulae are discussed in this study as well. Based on the review results, the US DOT Accident Prediction Formula was found to be the most commonly used formula due to its accuracy in predicting the number of accidents at HRGCs. However, certain states still prefer customized models due to some practical considerations. Data availability and data accuracy were identified as some of the key model implementation challenges in many states across the country.
Journal Article
Making investment decisions in stock markets using a forecasting-Markowitz based decision-making approaches
Purpose
This study aims to make investment decisions in stock markets using forecasting-Markowitz based decision-making approaches.
Design/methodology/approach
The authors’ approach offers the use of time series prediction methods including autoregressive, autoregressive moving average and artificial neural network, rather than calculating the expected rate of return based on distribution.
Findings
The results show that using time series prediction methods has a significant effect on improving investment decisions and the performance of the investments.
Originality/value
In this study, in contrast to previous studies, the alteration in the Markowitz model started with the investment expected rate of return. For this purpose, instead of considering the distribution of returns and determining the expected returns, time series prediction methods were used to calculate the future return of each asset. Then, the results of different time series methods replaced the expected returns in the Markowitz model. Finally, the overall performance of the method, as well as the performance of each of the prediction methods used, was examined in relation to nine stock market indices.
Journal Article
Long-memory recursive prediction error method for identification of continuous-time fractional models
This paper deals with recursive continuous-time system identification using fractional-order models. Long-memory recursive prediction error method is proposed for recursive estimation of all parameters of fractional-order models. When differentiation orders are assumed known, least squares and prediction error methods, being direct extensions to fractional-order models of the classic methods used for integer-order models, are compared to our new method, the long-memory recursive prediction error method. Given the long-memory property of fractional models, Monte Carlo simulations prove the efficiency of our proposed algorithm. Then, when the differentiation orders are unknown, two-stage algorithms are necessary for both parameter and differentiation-order estimation. The performances of the new proposed recursive algorithm are studied through Monte Carlo simulations. Finally, the proposed algorithm is validated on a biological example where heat transfers in lungs are modeled by using thermal two-port network formalism with fractional models.
Journal Article
Application of machine learning in predicting survival outcomes involving real-world data: a scoping review
Background
Despite the interest in machine learning (ML) algorithms for analyzing real-world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common scenario in clinical practice, is less explored. ML models are capable of algorithmically learning from large, complex datasets and can offer advantages in predicting time-to-event data. We reviewed the recent applications of ML for survival analysis using RWD in healthcare.
Methods
PUBMED and EMBASE were searched from database inception through March 2023 to identify peer-reviewed English-language studies of ML models for predicting time-to-event outcomes using the RWD. Two reviewers extracted information on the data source, patient population, survival outcome, ML algorithms, and the Area Under the Curve (AUC).
Results
Of 257 citations, 28 publications were included. Random survival forests (
N
= 16, 57%) and neural networks (
N
= 11, 39%) were the most popular ML algorithms. There was variability across AUC for these ML models (median 0.789, range 0.6–0.950). ML algorithms were predominately considered for predicting overall survival in oncology (
N
= 12, 43%). ML survival models were often used to predict disease prognosis or clinical events (
N
= 27, 96%) in the oncology, while less were used for treatment outcomes (
N
= 1, 4%).
Conclusions
The ML algorithms, random survival forests and neural networks, are mainly used for RWD to predict survival outcomes such as disease prognosis or clinical events in the oncology. This review shows that more opportunities remain to apply these ML algorithms to inform treatment decision-making in clinical practice. More methodological work is also needed to ensure the utility and applicability of ML models in survival outcomes.
Journal Article
Verification of the CNOSSOS traffic noise prediction model – a case study
Pursuant to Commission Directive (EU) 2015/996 of 19 May 2015, which establishes common methods for noise assessment under Directive 2002/49/EC of the European Parliament and of the Council, EU Member States have been required to use the CNOSSOS method for noise mapping since the beginning of 2019. Consequently, from that year, the standardised CNOSSOS noise prediction method has been used to assess noise generated by moving vehicles. This method introduced a specific vehicle classification that differs from the classification proposed in the traffic noise prediction method recommended until 2019, i.e. the French NMPB-Routes-96 method. This paper compares the relationships between sound power level as a function of vehicle velocity according to the classifications adopted in the two traffic noise prediction methods. In addition, using the determined corrections to the sound power level, the CNOSSOS noise prediction method is verified by comparing measured and calculated equivalent sound levels.
Journal Article
Diagnostic Applications of AI in Sports: A Comprehensive Review of Injury Risk Prediction Methods
This review provides a comprehensive analysis of the transformative role of artificial intelligence (AI) in predicting and preventing sports injuries across various disciplines. By exploring the application of machine learning (ML) and deep learning (DL) techniques, such as random forests (RFs), convolutional neural networks (CNNs), and artificial neural networks (ANNs), this review highlights AI’s ability to analyze complex datasets, detect patterns, and generate predictive insights that enhance injury prevention strategies. AI models improve the accuracy and reliability of injury risk assessments by tailoring prevention strategies to individual athlete profiles and processing real-time data. A literature review was conducted through searches in PubMed, Google Scholar, Science Direct, and Web of Science, focusing on studies from 2014 to 2024 and using keywords such as ‘artificial intelligence’, ‘machine learning’, ‘sports injury’, and ‘risk prediction’. While AI’s predictive power supports both team and individual sports, its effectiveness varies based on the unique data requirements and injury risks of each, with team sports presenting additional complexity in data integration and injury tracking across multiple players. This review also addresses critical issues such as data quality, ethical concerns, privacy, and the need for transparency in AI applications. By shifting the focus from reactive to proactive injury management, AI technologies contribute to enhanced athlete safety, optimized performance, and reduced human error in medical decisions. As AI continues to evolve, its potential to revolutionize sports injury prediction and prevention promises further advancements in athlete health and performance while addressing current challenges.
Journal Article
Agricultural Product Price Forecasting Methods: A Review
Agricultural price prediction is a hot research topic in the field of agriculture, and accurate prediction of agricultural prices is crucial to realize the sustainable and healthy development of agriculture. It explores traditional forecasting methods, intelligent forecasting methods, and combination model forecasting methods, and discusses the challenges faced in the current research landscape of agricultural commodity price prediction. The results of the study show that: (1) The use of combined models for agricultural product price forecasting is a future development trend, and exploring the combination principle of the models is a key to realize accurate forecasting; (2) the integration of the combination of structured data and unstructured variable data into the models for price forecasting is a future development trend; and (3) in the prediction of agricultural product prices, both the accuracy of the values and the precision of the trends should be ensured. This paper reviews and analyzes the methods of agricultural product price prediction and expects to provide some help for the development of research in this field.
Journal Article