Traditional Statistical Methods Research Articles

A machine learning framework to adjust for learning effects in medical device safety evaluation.

Traditional methods for medical device post-market surveillance often fail to accurately account for operator learning effects, leading to biased assessments of device safety. These methods struggle with non-linearity, complex learning curves, and time-varying covariates, such as physician experience. To address these limitations, we sought to develop a machine learning (ML) framework to detect and adjust for operator learning effects. A gradient-boosted decision tree ML method was used to analyze synthetic datasets that replicate the complexity of clinical scenarios involving high-risk medical devices. We designed this process to detect learning effects using a risk-adjusted cumulative sum method, quantify the excess adverse event rate attributable to operator inexperience, and adjust for these alongside patient factors in evaluating device safety signals. To maintain integrity, we employed blinding between data generation and analysis teams. Synthetic data used underlying distributions and patient feature correlations based on clinical data from the Department of Veterans Affairs between 2005 and 2012. We generated 2494 synthetic datasets with widely varying characteristics including number of patient features, operators and institutions, and the operator learning form. Each dataset contained a hypothetical study device, Device B, and a reference device, Device A. We evaluated accuracy in identifying learning effects and identifying and estimating the strength of the device safety signal. Our approach also evaluated different clinically relevant thresholds for safety signal detection. Our framework accurately identified the presence or absence of learning effects in 93.6% of datasets and correctly determined device safety signals in 93.4% of cases. The estimated device odds ratios' 95% confidence intervals were accurately aligned with the specified ratios in 94.7% of datasets. In contrast, a comparative model excluding operator learning effects significantly underperformed in detecting device signals and in accuracy. Notably, our framework achieved 100% specificity for clinically relevant safety signal thresholds, although sensitivity varied with the threshold applied. A machine learning framework, tailored for the complexities of post-market device evaluation, may provide superior performance compared to standard parametric techniques when operator learning is present. Demonstrating the capacity of ML to overcome complex evaluative challenges, our framework addresses the limitations of traditional statistical methods in current post-market surveillance processes. By offering a reliable means to detect and adjust for learning effects, it may significantly improve medical device safety evaluation.

Deep learning-based high-frequency jump test for detecting stock market manipulation: evidence from China’s securities market

PurposeThis study aims to tackle the critical issue of detecting stock market manipulation, which undermines the integrity and stability of financial markets globally. Even enhanced with machine learning, traditional statistical methods often struggle to analyze high-frequency trading data effectively due to inherent noise and the limited availability of publicly known manipulation cases. This leads to poor model generalization and a tendency toward over-fitting. Focusing on China's securities market, our study introduces an innovative approach that employs deep learning-based high-frequency jump tests to overcome these challenges and to develop a more effective method for identifying manipulative activities.Design/methodology/approachWe employed the “Jump Variation – Time-of-Day” (JV-TOD) non-parametric technique for jump tests on high-frequency data, coupled with the synthetic minority over-sampling technique (SMOTE) algorithm for re-balancing sample data. Our approach trains a deep neural network (DNN) on refined data to enhance its ability to identify manipulation patterns accurately.FindingsOur results show that the deep neural network model, calibrated with high-frequency price jump data, identifies manipulation behavior more specifically and accurately than traditional models. The model achieved an accuracy rate of 94.64%, an F1-score of 95.26% and a recall rate of 95.88%, significantly outperforming traditional models. These results demonstrate the effectiveness of our approach in mitigating over-fitting and improving the robustness of market manipulation detection.Practical implicationsThe proposed model provides regulatory entities and financial institutions with a more efficient tool to monitor and counteract market manipulation, thereby improving market fairness and investor protection.Originality/valueBy integrating the JV-TOD jump test with deep learning, this study proposed a new approach to market manipulation detection. The innovation is in its capacity to detect subtle manipulation signals that traditional methods typically overlook. Our model, which is trained on jump test data enhanced by the SMOTE algorithm, excels at learning complex manipulation patterns. This enhances both detection accuracy and robustness. In contrast to existing methods that are challenged by the noisy and intricate nature of high-frequency data, our approach shows enhanced performance in identifying nuanced market manipulations, offering a more effective and reliable method for detecting market manipulation.

High-resolution modeling and projection of heat-related mortality in Germany under climate change

Abstract Heat is a leading cause of premature deaths during summer months. Understanding the relationship between high temperatures and excess mortality is crucial for designing effective prevention and adaptation plans. Traditional statistical methods have been repeatedly applied to analyze heat-related mortality. However, spatially and temporally high-resolution analyses are challenging due to fragmented data archives across different agglomeration levels, especially for mortality data. We propose a neural network-based model to estimate heat-related mortality. This approach allows for high temporal and spatial resolution estimations, such as regional heat risk during specific heatwaves. Using Germany as a case study, we calculated heat-related excess mortality rates at the district level and visualized the dynamics of local health risks within a heatwave. Overall, we estimated a total of 48,000 heat-related deaths in Germany over the last decade (2014-2023), with most occurring during specific heatwave events. For example, in 2023, the heatwave from July 7-14 contributed approximately 28% (1100 deaths) to the total of about 3,900 heat-related deaths for the entire year. This estimation is consistent with previously published reports from the Robert Koch Institute (RKI), considering the resolution differences in the input data. In addition, we combined our model with shared socio-economic pathways (SSPs) of future climate change, assuming a static population and baseline mortality rate. The results suggest that heat-related risk in Germany could further increase by a factor of 2.5 (SSP245) to 9 (SSP370) without adaptation to extreme heat. Our approach is a valuable tool for the monitoring of regional heat-related risks as well as scenario-based risks projections for the future. The results of the model can help develop climate-driven public health strategies, aiding in the identification of local risks during heatwaves and in long-term resilience planning. Key messages • High-resolution modeling of heat-related mortality helps us to develop targeted adaptation and prevention plans. • The majority of heat-related mortality occurs during heatwaves in summer.

Predicting diabetes prognosis using machine learning techniques in a Hungarian clinical database

Abstract Background Chronic conditions such as type 2 diabetes mellitus have great impact on patients’ quality of life. Although clinical databases provide a perfect base for research aimed at improving diabetes care, analyzing such databases requires extensive pre-processing, mainly due to the large amount of unstructured data. Our study aimed to present the steps of generating a dataset from a large clinical database, and to apply machine learning-based analytical techniques regarding. Methods Data of the Clinical Center of University of Debrecen was used. To structure the unstructured data, regular expressions and natural language processing methods were used. The main machine learning models were as follows: Gradient Boosting Machines to predict the risk of complication development; Long Short-Term Memory Networks to forecast future health outcomes. All analysis and procedures were done using Python. Results The database contains approximately 1600 tables, with a total size of 1.9 terabytes, where the largest table is 21.07 gigabytes with approximately 44.64 million rows. The final dataset consisted of 40,332 patients. Most variables originate from the unstructured data, including complications and comorbidities of diabetes, as well as physical and laboratory parameters. Related to laboratory parameters, the number of measurements and the median value for every half-year were available. The diagnosis time of the complications’ occurrence is also presented. Machine learning methods were more accurate compared with traditional statistical methods in predicting the prognosis (p < 0.05). Discussion Our research highlights the importance of clinical data in chronic disease management. There are challenges in pre-processing and managing datasets, but machine learning-based methods are very efficient not only in extracting useful information from unstructured data but also in predicting the prognosis and identifying potential intervention points for better care. Key messages • Natural language processing can be used to obtain useful information from unstructured clinical data. • Using machine learning techniques on clinical data could improve diabates care.

Machine learning model using cardiovascular magnetic resonance to predict cardiovascular events in asymptomatic patients with known CAD

Abstract Background Despite declining rates, recurrent cardiovascular events continue to pose significant mortality and morbidity risks in patients with obstructive coronary artery disease (CAD). Traditional prognostic assessments in these patients rely on limited clinical and imaging data. Although several studies have shown the strong prognostic value of stress cardiovascular magnetic resonance (CMR) in asymptomatic patients with known CAD, no dedicated score using CMR was established. Machine learning (ML) methods present an opportunity to leverage a wider array of variables to address this gap. Purpose This study sought to investigate the feasibility and accuracy of ML using stress CMR and clinical data to predict major adverse cardiovascular events (MACE) over a 10-year period in patients with known obstructive CAD, comparing its performance with traditional statistical methods. Methods Between 2009 and 2011, consecutive asymptomatic patients with known obstructive CAD referred for vasodilator stress CMR in two independent centres were followed for the occurrence of MACE defined by cardiovascular mortality or recurrent non-fatal myocardial infarction (MI). Known obstructive CAD was defined by a history of percutaneous coronary intervention, coronary artery bypass surgery, or MI. The first center (N=866) was designated as the derivation cohort for variable selection and model building. The second center (N=224) was used as the validation cohort to evaluate the performance of the final ML-model. Various feature selection methods (Random Forest [RF], XGBoost, and Boruta) were employed to identify robust and informative variables. A final RF model was trained on the derivation cohort and evaluated on the validation cohort. Performance was compared with a standard generalized logistic regression model (GLM), utilizing the receiver operating characteristics (ROC) and the precision-recall (PR) curves as well as the area under the curves (AUC). Results In the derivation cohort (age 67±7 years, LVEF 43±8%, 81% male), 243 (28%) out of 866 patients encountered MACE during the 10-year follow-up period while in the validation cohort (age 67±7 years, LVEF 43±7%, 84% male), 53 (24%) out of 224 patients experienced MACE. Through feature selection method in the derivation cohort, one clinical and four CMR variables emerged as key predictors: number of ischemic segments, number of late gadolinium enhancement (LGE) segments, left ventricular ejection fraction, left ventricular end-diastolic diameter indexed, and age (Figure 1). Using those 5 variables, the RF model significantly outperformed the GLM, demonstrating superior predictive accuracy (AUROC: 0.89 vs. 0.73; PRAUC: 0.78 vs. 0.58, all p<0.001) in the validation cohort (Figure 2). Conclusions A supervised machine learning model using RF, integrating age and stress-CMR data, outperformed traditional statistical methods in predicting 10-year MACE for asymptomatic patients with known CAD.Figure1:Variable selectionFigure2:Models performance on validatio

How far should adjacent pan traps be placed for bee sampling?

AbstractAmong several methods of active and passive sampling bees (Hymenoptera: Apoidea), pan traps, also known as bee bowls, are commonly utilized for their efficiency and as a means of avoiding collector bias. When comparing pan‐trap samples using traditional statistical methods, the assumption of independence among samples needs to be met. To determine the necessary distance between pan traps to obtain independent samples, we investigated spatial dependence in bee catches using pan traps at four sites. At each site, a regular grid of pan traps was laid out, with each sample having two colors of fluorescent trap (i.e. blue and yellow). These trap pairs were separated by 10 m, with 100–109 points in the grid. In addition, five points within the grid were chosen randomly, and an additional 40 traps were placed at 2 m and 4 m from the five sample points to sample bees at a minimum 2 m trap distance. Traps collected bees for five consecutive days and bees were counted for each point and each trap color. Bee count data were analyzed using geostatistics to determine the spatial dependency in trap catches for yellow traps, blue traps, and both colors combined. The highest value of the range parameter of the semivariogram found across the four sites and pan trap colors was 17 m for blue traps. From this, we recommend a conservative minimum distance of 17 m between adjacent pan traps to obtain spatially independent samples for optimizing sampling plans when independent samples are necessary.

Comparison of machine learning and traditional methods for prediction of adverse clinical events after percutaneous coronary intervention

Abstract Background Accurate prediction of clinical outcomes following percutaneous coronary intervention (PCI) is essential for mitigating risk and periprocedural planning. Traditional risk models have demonstrated modest predictive value. Machine learning (ML) models offer an alternative risk stratification that may provide improved predictive accuracy. We performed a systematic review and meta-analysis comparing ML models and traditional risk scores for prediction of clinical outcomes after PCI. Methods This study was reported according to PRISMA guidelines. PubMed, EMBASE, Web of Science and Cochrane databases were searched until 1st November, 2023 for studies comparing ML models with traditional statistical methods for event prediction after PCI. The primary outcome was comparative discrimination measured by C-statistics with 95% confidence intervals between ML models and traditional methods in estimating the risk of all-cause mortality, major bleeding and the composite outcome major adverse cardiovascular events (MACE). Results Thirty-four models were included across 13 observational studies (4105916 patients). The summary C-statistic all ML models across all clinical endpoints was 0.81 (95% CI, 0.78-0.85), compared to traditional methods 0.79 (95% CI, 0.74-0.85). The difference in C-statistic was 0.02 (95% CI, 0.002-0.04, p=0.56). Of included studies, only 1 model was externally validated and 10 models were calibrated. Conclusion ML models did not outperform traditional risk scores in the discrimination of all-cause mortality, major bleeding or MACE following PCI. While integration of ML algorithms into electronic healthcare systems may improve periprocedural risk stratification, immediate implementation in the clinical setting remains uncertain. Further research is required to overcome methodological and validation limitations.

Effects of neighbourhood characteristics on the distribution of vacant houses in Toda city, a satellite city of Tokyo metropolis of japan

As Japan faces a growing crisis of vacant housing, this study investigates the underexplored relationship between vacant house distribution and neighbourhood characteristics in Toda City, a satellite city of the Tokyo metropolis located in Saitama Prefecture. We selected 10 community features, including living convenience, housing supply, and policy support, to explore their influence on vacancy rates. Using a comprehensive dataset primarily derived from the PLATEAU data, we employed a two-step method combining global and local Moran’s I analyses to assess the spatial distribution normality of vacant houses. We then compared traditional statistical data analysis with spatial data analysis through multiscale geographically weighted regression to evaluate their effectiveness and identify the factors most closely associated with vacancy rates. Our findings reveal that spatial data analysis provides superior insights compared to traditional statistical methods. Both approaches consistently indicate a significant positive correlation between the supermarket area ratio and vacancy rates, and a significant negative correlation between road adjacency and vacancy rates. This study demonstrates the feasibility of using spatial data to analyse neighbourhood characteristics in the context of declining vacancy rates in satellite cities. The insights gained offer theoretical support for selecting data and methodologies in future research and policymaking that are aimed at mitigating the growth of vacant houses.

Machine learning for prediction of all-cause mortality in acute coronary syndrome

Abstract Background Acute coronary syndrome (ACS) remains one of the leading causes of death globally. Accurate and reliable mortality risk prediction of ACS patients is essential for developing targeted treatment strategies and improve prognostication. Traditional models for risk stratification such as the Global Registry of Acute Coronary Events (GRACE) and the Thrombolysis In Myocardial Infarction (TIMI) risk scores offer moderate discriminative value, and do not incorporate contemporary predictors of ACS prognosis. Machine learning (ML) models have emerged as an alternate method that may offer improved risk assessment. This study aims to compare machine learning models with traditional risk scores for predicting all-cause mortality in patients with ACS. Methods PubMed, EMBASE, Web of Science and Cochrane databases were searched until 1st November 2023 for studies comparing ML models with traditional statistical methods for event prediction of ACS patients. The primary outcome was comparative discrimination measured by C-statistics with 95% confidence intervals between ML models and traditional methods in estimating the risk of all-cause mortality. Results Ten studies were included (239627 patients). The summary C-statistic of all ML models across all endpoints was 0.89 (95% CI, 0.86-0.92), compared to traditional methods 0.82 (95% CI, 0.79-0.85). The difference in C-statistic between all ML models and traditional methods was 0.07 (p<0.05). Three models undertook external validation, and calibration was inconsistently reported. Conclusion ML models demonstrated superior discrimination of all-cause mortality for ACS patients compared to traditional risk scores. Despite outperforming well-established prognostic tools such as the GRACE and TIMI scores, current clinical applications of ML approaches remain uncertain, particularly in view of the methodological limitations of existing models and the need for greater validation.

Artificial intelligence reveals past climate extremes by reconstructing historical records

The understanding of recent climate extremes and the characterization of climate risk require examining these extremes within a historical context. However, the existing datasets of observed extremes generally exhibit spatial gaps and inaccuracies due to inadequate spatial extrapolation. This problem arises from traditional statistical methods used to account for the lack of measurements, particularly prevalent before the mid-20th century. In this work, we use artificial intelligence to reconstruct observations of European climate extremes (warm and cold days and nights) by leveraging Earth system model data from CMIP6 through transfer learning. Our method surpasses conventional statistical techniques and diffusion models, showcasing its ability to reconstruct past extreme events and reveal spatial trends across an extensive time span (1901-2018) that is not covered by most reanalysis datasets. Providing our dataset to the climate community will improve the characterization of climate extremes, resulting in better risk management and policies.

Course Success Prediction and Early Identification of At-Risk Students Using Explainable Artificial Intelligence

Artificial Intelligence (AI) is increasingly used in online education platforms to provide valuable insights into students’ performance and success. However, the complexity of AI models makes it challenging for educators to interpret the specific factors that influence whether a student is going to pass or fail. Utilizing the Open University Learning Analytics Dataset (OULAD), this study employs various machine learning and deep learning techniques for predicting students’ success, along with SHapley Additive exPlanations (SHAP) as an Explainable Artificial Intelligence (XAI) technique, to understand the key factors behind success or failure. Unlike traditional statistical methods that explore variable relationships, this AI-driven approach uses advanced deep learning techniques to identify patterns and insights, allowing for a better understanding of the factors influencing student success. Additionally, this study focuses on identifying students at risk of failure using XAI techniques, specifically SHAP, to interpret model outputs by breaking down how specific factors contribute to a student’s success. This method enables targeted interventions to support their success. Results reveal that student engagement and registration timelines are critical factors affecting performance. The customized models achieve up to 94% accuracy for the designed tasks, outperforming traditional approaches. This study contributes to the use of AI in education and offers practical insights not only for educators but also for administrators and policymakers to enhance the quality and effectiveness of online learning.

Sarcopenia screening based on the assessment of gait with inertial measurement units: a systematic review

BackgroundGait variables assessed by inertial measurement units (IMUs) show promise as screening tools for aging-related diseases like sarcopenia. The main aims of this systematic review were to analyze and synthesize the scientific evidence for screening sarcopenia based on gait variables assessed by IMUs, and also to review articles that investigated which gait variables assessed by IMUs were related to sarcopenia.MethodsSix electronic databases (PubMed, SportDiscus, Web of Science, Cochrane Library, Scopus and IEEE Xplore) were searched for journal articles related to gait, IMUs and sarcopenia. The search was conducted until December 5, 2023. Titles, abstracts and full-length texts for studies were screened to be included.ResultsA total of seven articles were finally included in this review. Despite some methodological variability among the included studies, IMUs demonstrated potential as effective tools for detecting sarcopenia when coupled with artificial intelligence (AI) models, which outperformed traditional statistical methods in classification accuracy. The findings suggest that gait variables related to the stance phase such as stance duration, double support time, and variations between feet, are key indicators of sarcopenia.ConclusionsIMUs could be useful tools for sarcopenia screening based on gait analysis, specifically when artificial intelligence is used to process the recorded data. However, more development and research in this field is needed to provide an effective screening tool for doctors and health systems.

Incorporating driving behavior into vehicle fuel consumption prediction: methodology development and testing

This study is a significant endeavor involving the development and testing of a comprehensive methodology to incorporate driving behavior into the analysis and prediction of vehicle fuel consumption. It underscores the crucial importance of understanding how different driving behavior affect fuel efficiency. The framework we present is a theoretical construct and a practical tool. It provides a robust, multi-step process for linking driving behavior to fuel consumption, leveraging both traditional statistical methods and advanced machine learning techniques to derive actionable insights. To test the framework, we used a naturalistic data that includes about 5408 different road users in a mixed traffic environment and urban settings in Germany. We applied a microscopic fuel consumption model to calibrate the framework and an unsupervised clustering algorithm to classify the behavior of the driver interacting with each other and with vulnerable road users. The framework includes developing Linear regression model as a baseline, which yields an R-squared of 0.511 and a Mean Squared Error (MSE) of 0.031, indicating moderate predictive accuracy. The final step includes choosing Random Forest as a better model, which achieves a higher R-squared of 0.956 and a lower MSE of 0.003. We also found that conservative and aggressive driving leads to significantly higher and more discrepancy in fuel consumption than normal driving behavior. These insights can promote more efficient driving practices, leading to significant fuel savings and environmental benefits.

Innovations and Challenges in Economic Census Methodology: A Study by the Bureau of Statistics

The Bureau of Statistics has demonstrated a forward-looking strategic approach in its economic census. By leveraging dual innovations in technology and management, and incorporating modern technologies such as big data, cloud computing, and the Internet of Things, it has deepened the reform of the census methodology. Additionally, the Bureau has built a multi-dimensional collaborative network that enhances international cooperation, departmental coordination, and public participation. This approach not only addresses the limitations of traditional statistical methods in a complex economic environment but also improves data quality and census efficiency, providing an accurate and reliable foundation for national economic decision-making.

Research progress on correlative prediction factors and prediction models of endometriosis associated ovarian carcinoma.

Endometriosis is a common benign disease in women of childbearing age, with a malignant change rate of about 1%. Endometriosis associated ovarian cancer (EAOC), which usually occurs in the ovaries, is a serious threat to women's health. Early identification of high-risk groups of EMs malignant transformation is of great significance for the prevention and treatment of EAOC. However, there is still a lack of specific and sensitive prediction factors. In recent years, scholars at home and abroad have used traditional statistical methods and machine learning to explore EAOC related prediction factors and prediction models. This paper mainly reviews and evaluates the diagnosis and prediction model of EAOC. Studies were identified by searching the CNKI, PubMed and Web of Science Core Collection, (WOSCC) till 2023, Data which met the inclusion criteria of clinical studies were evaluated about the quality. This paper analyzes and summarizes the prediction factors and prediction models in the literature. After screening, 7 relevant studies were finally obtained. Prediction factors included: age, menstruation, menopausal status, course of disease, infertility associated with endometriosis, history of single estrogen use during menopause, serological indexes: human epididymis protein 4, carbohydrate antigen 125(CA125), ovarian malignancy risk algorithm, indications for ultrasound examination: cyst shape, structure and blood flow signal, etc. Prediction models: Alignment diagram, Multivariate logistic regression model, Gail model, Gradient Boosting Decision Tree and Lasso-logistics regression. Related models were in good agreement with the actual situation, and have good sensitivity and specificity. The relevant prediction factors and prediction models were summarized to provide reference and new thinking for the research of prediction models in the field of EAOC, in order to develop standardized long-term management strategies for high-risk groups of EAOC and realize the advance of the diagnosis threshold of patients with EAOC.

OrthoMortPred: Predicting one-year mortality following orthopedic hospitalization

ObjectivePredicting mortality risk following orthopedic surgery is crucial for informed decision-making and patient care. This study aims to develop and validate a machine learning model for predicting one-year mortality risk after orthopedic hospitalization and to create a personalized risk prediction tool for clinical use. MethodsWe analyzed data from 3,132 patients who underwent orthopedic procedures at the Central Lisbon University Hospital Center from 2021 to 2023. Using the LightGBM algorithm, we developed a predictive model incorporating various clinical and administrative variables. We employed SHAP (SHapley Additive exPlanations) values for model interpretation and created a personalized risk prediction tool for individual patient assessment. ResultsOur model achieved an accuracy of 93% and an area under the ROC curve of 0.93 for predicting one-year mortality. Notably, ’EMERGENCY ADMISSION DATE TIME’ emerged as the most influential predictor, followed by age and pre-operative days. The model demonstrated robust performance across different patient subgroups and outperformed traditional statistical methods. The personalized risk prediction tool provides clinicians with real-time, patient-specific risk assessments and insights into contributing factors. ConclusionOur study presents a highly accurate model for predicting one-year mortality following orthopedic hospitalization. The significance of ’EMERGENCY ADMISSION DATE TIME’ as the primary predictor highlights the importance of admission timing in patient outcomes. The accompanying personalized risk prediction tool offers a practical means of implementing this model in clinical settings, potentially improving risk stratification and patient care in orthopedic practice.

The Role of Machine Learning in Enhancing Particulate Matter Estimation: A Systematic Literature Review

As urbanization and industrial activities accelerate globally, air quality has become a pressing concern, particularly due to the harmful effects of particulate matter (PM), notably PM2.5 and PM10. This review paper presents a comprehensive systematic assessment of machine learning (ML) techniques for estimating PM concentrations, drawing on studies published from 2018 to 2024. Traditional statistical methods often fail to account for the complex dynamics of air pollution, leading to inaccurate predictions, especially during peak pollution events. In contrast, ML approaches have emerged as powerful tools that leverage large datasets to capture nonlinear, intricate relationships among various environmental, meteorological, and anthropogenic factors. This review synthesizes findings from 32 studies, demonstrating that ML techniques, particularly ensemble learning models, significantly enhance estimation accuracy. However, challenges remain, including data quality, the need for diverse and balanced datasets, issues related to feature selection, and spatial discontinuity. This paper identifies critical research gaps and proposes future directions to improve model robustness and applicability. By advancing the understanding of ML applications in air quality monitoring, this review seeks to contribute to developing effective strategies for mitigating air pollution and protecting public health.

A Novel Neural Network Prediction Model for Mineral Content Based on the Data of Elemental Logging and Well Logging

The mineral content of shale plays an important role in the study of reservoir characteristics. Obtaining high-precision mineral content data from a simple and straightforward method is helpful for the study of reservoir properties. Due to the complex and varied elemental combination of minerals and the lithological diversity in different formations, there are limitations in the traditional statistical methods for predicting mineral content. In this study, a multi-source fusion dataset is constructed first using a detailed workflow to process multi-source data from elemental logging and conventional well logging from the Funing shale formations in China. A hybrid neural network model consisting of a back-propagation artificial neural network (BP-ANN) and gated recurrent unit (GRU) structure, with a custom constraint loss function, is developed to predict the mineral contents. The results show that the predictive performance of this hybrid multi-source model performs statistically significantly better than that of the BP-ANN and GRU models using single source data. In addition, the accuracy of the hybrid model is further improved by using the custom constraint loss function. This method provides high-resolution distributions of mineral content compared to the X-ray powder diffraction testing method. It will help enhance the understanding of the formation.

Using Machine Learning Algorithms to Predict Postoperative Anterior Bone Loss Following Anterior Cervical Disc Replacement.

Machine learning model. This study aimed to develop and validate a machine learning (ML) model to predict moderate-severe anterior bone loss (ABL) following anterior cervical disc replacement (ACDR). A retrospective review of patients undergoing ACDR or Hybrid surgery (HS) at a single center was performed. Patients diagnosed as C3-7 single- or multi-level cervical disc degenerative diseases (CDDD) with more than 2years of follow-up and complete pre- and postoperative radiological imaging were included. An ML-based algorithm was developed to predict moderate-severe ABL based on perioperative demographic, clinical, and radiographic parameters. Model performance was evaluated in terms of discrimination and overall performance. A total of 339 ACDR segments were included (61.65% female, mean age 45.65 ± 8.03years). During a follow-up period of 45.65 ± 8.03months, 103 (30.38%) segments developed moderate-severe ABL. The model demonstrated good discrimination and overall performance according to precision (moderate-severe ABL: 0.71 ± 0.07, none-mild ABL: 0.73 ± 0.08), recall (moderate-severe ABL: 0.69 ± 0.08, none-mild ABL: 0.75 ± 0.07), F1-score (moderate-severe ABL: 0.70 ± 0.08, none-mild ABL: 0.74 ± 0.07), and area under the curve (AUC) (0.74 ± 0.10). The most important predictive features were higher height change, higher post-segmental angle, and longer operation time. Utilizing a ML approach, this study successfully identified risk factors and accurately predicted the development of moderate-severe ABL following ACDR, demonstrating robust discrimination and overall performance. By overcoming the limitations of traditional statistical methods, ML can enhance discovery, clinical decision-making, and intraoperative techniques.

Machine learning approaches for risk prediction after percutaneous coronary intervention: a systematic review and meta-analysis.

Accurate prediction of clinical outcomes following percutaneous coronary intervention (PCI) is essential for mitigating risk and peri-procedural planning. Traditional risk models have demonstrated a modest predictive value. Machine learning (ML) models offer an alternative risk stratification that may provide improved predictive accuracy. This study was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis guidelines. PubMed, EMBASE, Web of Science, and Cochrane databases were searched until 1 November 2023 for studies comparing ML models with traditional statistical methods for event prediction after PCI. The primary outcome was comparative discrimination measured by C-statistics with 95% confidence intervals (CIs) between ML models and traditional methods in estimating the risk of all-cause mortality, major bleeding, and the composite outcome major adverse cardiovascular events (MACE). Thirty-four models were included across 13 observational studies (4 105 916 patients). For all-cause mortality, the pooled C-statistic for top-performing ML models was 0.89 (95%CI, 0.84-0.91), compared with 0.86 (95% CI, 0.80-0.93) for traditional methods (P = 0.54). For major bleeding, the pooled C-statistic for ML models was 0.80 (95% CI, 0.77-0.84), compared with 0.78 (95% CI, 0.77-0.79) for traditional methods (P = 0.02). For MACE, the C-statistic for ML models was 0.83 (95% CI, 0.75-0.91), compared with 0.71 (95% CI, 0.69-0.74) for traditional methods (P = 0.007). Out of all included models, only one model was externally validated. Calibration was inconsistently reported across all models. Prediction Model Risk of Bias Assessment Tool demonstrated a high risk of bias across all studies. Machine learning models marginally outperformed traditional risk scores in the discrimination of MACE and major bleeding following PCI. While integration of ML algorithms into electronic healthcare systems has been hypothesized to improve peri-procedural risk stratification, immediate implementation in the clinical setting remains uncertain. Further research is required to overcome methodological and validation limitations.