Quantifying Global Black Carbon Aging Responses to Emission Reductions Using a Machine Learning-based Climate Model

BackgroundThe machine learning models with dose factors and the deep learning models with dose distribution matrix have been used to building lung toxics models for radiotherapy and achieve promising results. However, few studies have integrated clinical features into deep learning models. This study aimed to explore the role of three-dimension dose distribution and clinical features in predicting radiation pneumonitis (RP) in esophageal cancer patients after radiotherapy and designed a new hybrid deep learning network to predict the incidence of RP.MethodsA total of 105 esophageal cancer patients previously treated with radiotherapy were enrolled in this study. The three-dimension (3D) dose distributions within the lung were extracted from the treatment planning system, converted into 3D matrixes and used as inputs to predict RP with ResNet. In total, 15 clinical factors were normalized and converted into one-dimension (1D) matrixes. A new prediction model (HybridNet) was then built based on a hybrid deep learning network, which combined 3D ResNet18 and 1D convolution layers. Machine learning-based prediction models, which use the traditional dosiomic factors with and without the clinical factors as inputs, were also constructed and their predictive performance compared with that of HybridNet using tenfold cross validation. Accuracy and area under the receiver operator characteristic curve (AUC) were used to evaluate the model effect. DeLong test was used to compare the prediction results of the models.ResultsThe deep learning-based model achieved superior prediction results compared with machine learning-based models. ResNet performed best in the group that only considered dose factors (accuracy, 0.78 ± 0.05; AUC, 0.82 ± 0.25), whereas HybridNet performed best in the group that considered both dose factors and clinical factors (accuracy, 0.85 ± 0.13; AUC, 0.91 ± 0.09). HybridNet had higher accuracy than that of Resnet (p = 0.009).ConclusionBased on prediction results, the proposed HybridNet model could predict RP in esophageal cancer patients after radiotherapy with significantly higher accuracy, suggesting its potential as a useful tool for clinical decision-making. This study demonstrated that the information in dose distribution is worth further exploration, and combining multiple types of features contributes to predict radiotherapy response.

Machine learning provides a flexible technique to predict the survival of patients who are admitted to hospital as emergency admissions. Mortality prediction is a central component of emergency patient quality of care and this can act as an indicator of severity to determine who needs prioritized care. Machine learning-based models, as opposed to human-crafted severity score systems, allow for much more complex and updateable models to be developed based on a larger set of input data attributes. While various studies of machine learning-based predictive models for predicting inpatient mortality have been carried out there is little literature on performance maintenance of these models. Determining the performance maintenance of these models over time determines how reliably they can be utilized into the future and for how long. The best performing model in this study achieve’s an AUC of 0.86 upon training and is able to maintain a similarly high AUC of 0.845 as of the end of the period of performance maintenance evaluation nine months later. This is the first paper that the authors are aware of to consider and measure relative performance maintenance of machine learning-based models for emergency admission mortality prediction.

The machine learning-based car-following models are widely adopted to control the longitudinal movements of automated vehicles, such as Google Car and Apple Car, by mimicking the human drivers’ car-following maneuver. However, like human drivers, the models easily produce unsafe maneuvers for automated vehicles and has low robustness, especially in uncommon situations. To improve the machine learning-based car-following models, this paper proposes to combine the machine learning models with the kinematics-based car-following models that can overcome the shortcomings of machine learning models, using an optimal combination prediction method, which is called the combination car-following model in the paper. The selected kinematics-based car-following model is the Gipps model that has an intrinsic crash-avoidance mechanism, and the used machine learning-based models are the Back-Propagation Neural Networks (BPNN) model and Random Forest (RF) model, producing the two CCF models, the Gipps-RF model and Gipps-BPNN model. The real vehicle trajectory data sets are applied to calibrate and validate the proposed models, and simulations are conducted to evaluate the model performances. The results display that the proposed CCF models can enhance safety level and robustness of the car-following control of automated vehicles. Both the two CCF models have better performance than the BPNN and RF car-following models in reducing congestion, stabilizing traffic, and avoiding crashes, especially the Gipps-BPNN model.

ABSTRACTBackgroundNeonatal intestinal diseases often have an insidious onset and can lead to poor outcomes if not identified early. Early assessment of abnormal bowel function is critical for timely intervention and improving prognosis, underscoring the clinical importance of reducing mortality related to these conditions through rapid diagnosis and treatment. Bowel sounds (BSs), produced by intestinal contractions, are a key physiological indicator reflecting intestinal function. However, manual clinical assessment of BSs has limitations in terms of consistency and interpretative accuracy, which restricts its clinical application. This study aims to develop an machine learning-based diagnostic model for neonatal intestinal diseases using BS analysis and to compare its diagnostic accuracy with that of manual clinical assessment.Methods and analysisThis diagnostic study employs a cross-sectional design. The case group includes neonates diagnosed with intestinal diseases (using clinical diagnosis as the gold standard), such as neonatal necrotising enterocolitis (NEC), food protein-induced allergic proctocolitis, and other intestinal conditions (eg, intestinal obstruction, midgut volvulus, congenital megacolon). The control group will be established using frequency matching, stratified by gestational age and postnatal age. Based on the distribution of each stratum in the case group, neonates without intestinal diseases who were hospitalised during the same period will be randomly selected in proportion from the corresponding strata. BSs will be collected using a 3M stethoscope (Littmann 3200). The study will occur in two phases. In the first phase (July 2024 to July 2025), participants from West China Second University Hospital will be randomly divided into a training cohort (for model development with 10-fold cross-validation) and an internal validation cohort in a 7:3 ratio. The second phase (July 2025 to July 2026) will involve external validation, with patients from Sichuan Provincial Children’s Hospital and Shenzhen Children’s Hospital. Clinical diagnosis will serve as the gold standard, and diagnostic outcomes between the machine learning-based model and manual clinical assessment by physicians of varying experience levels will be compared.Ethics and disseminationEthical approval has been obtained from the Medical Ethics Committee of West China Second University Hospital (Registration No.: 2023SCHH0021), Sichuan Provincial Children’s Hospital (Registration No.: 2021YFC2701704) and the Shenzhen Children’s Hospital (Registration No.: 2021015). Written informed consent will be collected from all participants prior to BS collection. Study findings will be disseminated through conferences and publications in peer-reviewed journals.Trial registration numberChiCTR2400086713.

Empirical evaluation of performance degradation of machine learning-based predictive models – A case study in healthcare information systems

Machine Learning-based traffic prediction models for Intelligent Transportation Systems

Development of learning-based predictive models for radiation-induced atrial fibrillation in non-small cell lung cancer patients by integrating patient-specific clinical, dosimetry, and diagnostic information

BackgroundHaemorrhagic transformation (HT) is a severe complication after ischaemic stroke, but identifying patients at high risks remains challenging. Although numerous prediction models have been developed for HT following thrombolysis, thrombectomy, or spontaneous occurrence, a comprehensive summary is lacking. This study aimed to review and compare traditional and machine learning-based HT prediction models, focusing on their development, validation, and diagnostic accuracy.MethodsPubMed and Ovid-Embase were searched for observational studies or randomised controlled trials related to traditional or machine learning-based models. Data were extracted according to Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist and risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Performance data for prediction models that were externally validated at least twice and showed low risk of bias were meta-analysed.ResultsA total of 100 studies were included, with 67 focusing on model development and 33 on model validation. Among 67 model development studies, 44 were traditional model studies involving 47 prediction models (with National Institutes of Health Stroke Scale score being the most frequently used predictor in 35 models), and 23 studies focused on machine learning prediction models (with support vector machines being the most common algorithm, used in 10 models). The 33 validation studies externally validated 34 traditional prediction models. Regarding study quality, 26 studies were assessed as having a low risk of bias, 11 as unclear, and 63 as high risk of bias. Meta-analysis of 15 studies validating eight models showed a pooled area under the receiver operating characteristic curve of approximately 0.70 for predicting HT.ConclusionWhile significant progress has been made in developing HT prediction models, both traditional and machine learning-based models still have limitations in methodological rigour, predictive accuracy, and clinical applicability. Future models should undergo more rigorous validation, adhere to standardised reporting frameworks, and prioritise predictors that are both statistically significant and clinically meaningful. Collaborative efforts across research groups are essential for validating these models in diverse populations and improving their broader applicability in clinical practice.Systematic review registrationInternational Prospective Register of Systematic Reviews (CRD42022332816).

Development and Validation of a Machine-Learning Model to Predict POD24 Risk of Follicular Lymphoma

Review of machine learning-based surrogate models of groundwater contaminant modeling

Precise prediction of hospital stay duration is essential for maximizing resource utilization during surgery. Existing lumbar spinal stenosis (LSS) surgery prediction models lack accuracy and generalizability. Machine learning can improve accuracy by considering preoperative factors. This study aimed to develop and validate a machine learning-based model for estimating hospital stay duration following decompression surgery for LSS. Data from 848 patients who underwent decompression surgery for LSS at three hospitals were examined. Twelve prediction models, using 79 preoperative variables, were developed for postoperative hospital stay estimation. The top five models were chosen. Fourteen models predicted prolonged hospital stay (≥14 days), and the most accurate model was chosen. Models were validated using a randomly divided training sample (70%) and testing cohort (30%). The top five models showed moderate linear correlations (0.576-0.624) between predicted and measured values in the testing sample. The ensemble of these models had moderate prediction accuracy for final length of stay (linear correlation 0.626, absolute mean error 2.26 days, standard deviation 3.45 days). The c5.0 decision tree model was the top predictor for prolonged hospital stay, with accuracies of 89.63% (training) and 87.2% (testing). Key predictors for longer stay included JOABPEQ social life domain, facility, history of vertebral fracture, diagnosis, and Visual Analogue Scale (VAS) of low back pain. A machine learning-based model was developed to predict postoperative hospital stay after LSS decompression surgery, using data from multiple hospital settings. Numerical prediction of length of stay was not very accurate, although favorable prediction of prolonged stay was accomplished using preoperative factors. The JOABPEQ social life domain score was the most important predictor.

Machine Learning-Based Diagnostic Prediction Model Using T1-Weighted Striatal Magnetic Resonance Imaging for Early-Stage Parkinson's Disease Detection.

Peptide hormones serve as genome-encoded signal transduction molecules that play essential roles in multicellular organisms, and their dysregulation can lead to various health problems. In this study, we propose a method for predicting hormonal peptides with high accuracy. The dataset used for training, testing, and evaluating our models consisted of 1174 hormonal and 1174 non-hormonal peptide sequences. Initially, we developed similarity-based methods utilizing BLAST and MERCI software. Although these similarity-based methods provided a high probability of correct prediction, they had limitations, such as no hits or prediction of limited sequences. To overcome these limitations, we further developed machine and deep learning-based models. Our logistic regression-based model achieved a maximum AUROC of 0.93 with an accuracy of 86% on an independent/validation dataset. To harness the power of similarity-based and machine learning-based models, we developed an ensemble method that achieved an AUROC of 0.96 with an accuracy of 89.79% and a Matthews correlation coefficient (MCC) of 0.8 on the validation set. To facilitate researchers in predicting and designing hormone peptides, we developed a web-based server called HOPPred. This server offers a unique feature that allows the identification of hormone-associated motifs within hormone peptides. The server can be accessed at: https://webs.iiitd.edu.in/raghava/hoppred/.

Postoperative pain is a relevant and unresolved problem in clinical practice. In order to reduce the occurrence of severe postoperative pain, preventive, multi-professional and target group-specific pain management should be implemented. Risk assessment models based on machine learning and artificial intelligence are a resource-efficient way to identify the target group. The aim of this study was to develop a risk assessment model for early predicting poor postoperative pain outcomes that achieves good results without the need of additional, non-routine data collection. The various machine learning-based models were developed by using electronic medical records from over 70.000 in- and outpatient cases and 807 modelling features. The GBM (gradient boost machine) algorithm performed best with an area under the receiver operating characteristic curve (AUROC) of 0.82 on hold-out test data. Despite the excellent result, further research is needed to determine the modelt's performance in clinical practice.

Neuroanatomical prediction of individual anxiety problems level using machine learning models: A population-based cohort study of young adults.