Performance Of Prediction Models Research Articles

Machine learning methods in near infrared spectroscopy for predicting sensory traits in sweetpotatoes

It has been established that near infrared (NIR) spectroscopy has the potential of estimating sensory traits given the direct spectral responses that these properties have in the NIR region. In sweetpotato, sensory and texture traits are key for improving acceptability of the crop for food security and nutrition. Studies have statistically modelled the levels of NIR spectroscopy sensory characteristics using partial least squares (PLS) regression methods. To improve prediction accuracy, there are many advanced techniques, which could enhance modelling of fresh (wet and un-processed) samples or nonlinear dependence relationships. Performance of different quantitative prediction models for sensory traits developed using different machine learning methods were compared. Overall, results show that linear methods; linear support vector machine (L-SVM), principal component regression (PCR) and PLS exhibited higher mean R2 values than other statistical methods. For all the 27 sensory traits, calibration models using L-SVM and PCR has slightly higher overall R2 (x¯ = 0.33) compared to PLS (x¯ = 0.32) and radial-based SVM (NL-SVM; x¯= 0.30). The levels of orange color intensity were the best predicted by all the calibration models (R2 = 0.87 – 0.89). The elastic net linear regression (ENR) and tree-based methods; extreme gradient boost (XGBoost) and random forest (RF) performed worse than would be expected but could possibly be improved with increased sample size. Lower average R2 values were observed for calibration models of ENR (x¯ = 0.26), XGBoost (x¯ = 0.26) and RF (x¯ = 0.22). The overall RMSE in calibration models was lower in PCR models (X = 0.82) compared to L-SVM (x¯ = 0.86) and PLS (x¯ = 0.90). ENR, XGBoost and RF also had higher RMSE (x¯ = 0.90 – 0.92). Effective wavelengths selection using the interval partial least-squares regression (iPLS), improved the performance of the models but did not perform as good as the PLS. SNV pre-treatment was useful in improving model performance.

Enhancing pneumonia prognosis in the emergency department: a novel machine learning approach using complete blood count and differential leukocyte count combined with CURB-65 score

BackgroundPneumonia poses a major global health challenge, necessitating accurate severity assessment tools. However, conventional scoring systems such as CURB-65 have inherent limitations. Machine learning (ML) offers a promising approach for prediction. We previously introduced the Blood Culture Prediction Index (BCPI) model, leveraging solely on complete blood count (CBC) and differential leukocyte count (DC), demonstrating its effectiveness in predicting bacteremia. Nevertheless, its potential in assessing pneumonia remains unexplored. Therefore, this study aims to compare the effectiveness of BCPI and CURB-65 in assessing pneumonia severity in an emergency department (ED) setting and develop an integrated ML model to enhance efficiency.MethodsThis retrospective study was conducted at a 3400-bed tertiary medical center in Taiwan. Data from 9,352 patients with pneumonia in the ED between 2019 and 2021 were analyzed in this study. We utilized the BCPI model, which was trained on CBC/DC data, and computed CURB-65 scores for each patient to compare their prognosis prediction capabilities. Subsequently, we developed a novel Cox regression model to predict in-hospital mortality, integrating the BCPI model and CURB-65 scores, aiming to assess whether this integration enhances predictive performance.ResultsThe predictive performance of the BCPI model and CURB-65 score for the 30-day mortality rate in ED patients and the in-hospital mortality rate among admitted patients was comparable across all risk categories. However, the Cox regression model demonstrated an improved area under the ROC curve (AUC) of 0.713 than that of CURB-65 (0.668) for in-hospital mortality (p<0.001). In the lowest risk group (CURB-65=0), the Cox regression model outperformed CURB-65, with a significantly lower mortality rate (2.9% vs. 7.7%, p<0.001).ConclusionsThe BCPI model, constructed using CBC/DC data and ML techniques, performs comparably to the widely utilized CURB-65 in predicting outcomes for patients with pneumonia in the ED. Furthermore, by integrating the CURB-65 score and BCPI model into a Cox regression model, we demonstrated improved prediction capabilities, particularly for low-risk patients. Given its simple parameters and easy training process, the Cox regression model may be a more effective prediction tool for classifying patients with pneumonia in the emergency room.

Satellite-based soybean yield prediction in Argentina: A comparison between panel regression and deep learning methods

The accurate prediction of soybean yield is vital for global food market stabilization and food security. Recent advancements in remote sensing technology have significantly amplified interest in leveraging satellite-based methods for predicting crop yield. These methods offer in-season yield estimates. By utilizing this timely information, decision-makers can formulate strategic, well-informed choices that preemptively mitigate potential food price hikes, ultimately bolstering food security. While simple regression models have been widely utilized for satellite-based yield prediction, researchers have recently begun to explore the use of deep learning algorithms. This study compares the performance of panel regression and deep learning models for in-season soybean yield prediction at the Department (county-equivalent) level in Argentina. Data sources include the latest soybean land use products and MODIS bi-weekly vegetation index products. Results indicate that deep learning models significantly outperform panel regression. Deep learning Long Short-Term Memory (LSTM) models, which incorporate attention mechanism and a series of peak NDVI images, generate more accurate and time-sensitive predictions. Among competing LSTM models, the one with attention mechanism applied to the entire growing season's NDVI data yields the highest prediction accuracy, with a Root Mean Square Error (RMSE) of 505.78 kg/ha and Normalized Root Mean Square Error (NRMSE) of 0.0726. The LSTM model with attention on the three highest NDVI images attains a satisfactory prediction accuracy (RMSE = 627.28 kg/ha, NRMSE = 0.089) six weeks prior to harvest. This study presents a robust model for predicting crop yields, promoting sustainable production of soybeans and facilitating knowledgeable choices among farmers and policymakers.

Examining the performance of Laguerre-based and nonlinear predictive control models for wheeled mobile robots when facing obstacles

This focuses on the problem of controlling the formation of a team of rotating robots that are not homonomic and are not able to interact in a barrier environment. The virtual structure formation control strategy is obtained here to calculate each path of the robot separately while the reference path of the virtual center of the formation is generated by artificial potential fields. In this presentation, we will introduce a new control algorithm that utilizes Model predictive control and nonlinear system dynamics. Three traditional model prediction control controllers (MPC): logger-based MPC, nonlinear MPC, and traditional MPC are used to apply the control algorithm for the nonlinear system. Model predictive control is used to calculate the torques required to track the path by using the dynamic model of the Rotary moving robot. A proposed control rule is the most effective approach to solving the formation and tracking problems. Model Predictive Control will be introduced through the introduction of basic ideas and terms. Traditional MPC, logger-based MPC, and non-linear MPC are among the three MPC methods to be offered. The discrete time display of the power plant under control is taken into account by developing MPC methods for linear or nonlinear systems for practical implementation. The model predictor controller optimizes a cost function to calculate an optimal control sequence at any sampling point. While ignoring the rest, the system only achieves the first control action of this sequence. During subsequent sampling, the optimization problem is resolvable with the use of updated process measurements and a different horizon. The cost function formula is determined by the controller’s purpose. An error function often plays a role in determining the objective by revealing the difference between the desired and actual response. The shape of the formation can be preserved by the law of control by utilizing information from other robots, which can be assured through a new cost function. In the current conditions of disruption, the proposed controllers can also achieve the goal of tracking the path and maintaining formation, according to the results.

Abstract PO4-07-01: Deep learning guided radiation therapy machine selection via a multi-headed dose prediction model

Abstract Background: Accelerated Partial Breast Irradiation (APBI) is gaining popularity for the treatment of appropriately selected breast cancer patients in lieu of whole breast irradiation regimens due to its comparable clinical outcomes and improved patient convenience and quality-of-life. Currently, several machines for delivery of APBI are available such as CyberKnife, Elekta Unity, Varian Ethos and Varian TrueBeam. Each treatment machine yields a unique distribution of radiation dose, so a given modality may afford particular advantages or liabilities based on a patient’s anatomy. Existing methods for identification of an ideal modality that best suits a patient’s unique anatomical features are generally lacking, manual, and resource intensive. To overcome this limitation and personalize modality selection, we propose a deep learning (DL) based multi-modality dose prediction model. The model takes a patient’s computed tomography (CT) scan, segmented planning target volumes (PTVs) and segmented organs at risk (OARs) as input, and outputs predicted dose distributions for all trained modalities. Differences may then be evaluated between each radiation treatment modality’s corresponding dose distribution and dose volumetric histograms (DVH) to select the optimal modality for each patient. Methods: Our dataset contains 16 partial breast patients, each with 4 unique treatment plans generated for 4 distinct modalities – CyberKnife, Elekta Unity, Varian Ethos and Varian Truebeam. 10, 2, and 4 patients were dedicated to the train, validation and test datasets respectively. Our model’s architecture is a U-net with a shared encoder and 4 independent dose decoders, in which each decoder predicts dose for one modality. Each training patient’s CT scan, PTVs, OARs, and a PTV distance map are input to the model during training, and the shared encoder shares the input data’s latent representations with each of the 4 decoders. The 4 distinct dose distributions from each training patient are used as labels for the 4 corresponding decoders to supervise model training. The mean squared error, mean squared log error, and DVH losses are minimized during model training. The model trained until validation loss converged. The 4 dose distributions were predicted in ~ 1 minute per test patient, yielding 16 dose distributions in the test dataset. Model performance across the test dataset was evaluated by calculation of the mean absolute percent error (MAPE) of dose differences within the patient’s body between the model’s predicted doses and label doses, relative to patient prescription dose. Results: The performance of the multi-modal dose prediction model was evaluated in terms of dose differences throughout each patient’s body across the 16 test dose distributions as compared to the actual planned doses. The average MAPE of the multi-modal dose predictions was 1.27 ± 0.38% of a patient’s prescription dose as compared to the actual planned dose across the test dataset. For a prescription dose of 3000 cGy, that corresponds to an average MAPE of 38.1 ± 11.4 cGy. Conclusion: Our multi-modality dose prediction model’s novelty lies in its rapid provision and comparison of accurate and detailed dosimetric data for each treatment modality. This model can empower treating radiation oncologists to make informed decisions in radiation therapy machine selection on a patient-by-patient basis, potentially optimizing radiation treatment plans by reduction of delivered dose to organs at risk. Table. Citation Format: Austen Maniscalco, Ezek Mathew, David Parsons, Asal Rahimi, Mona Arbab, Prasanna Alluri, Xingzhe Li, Mu-Han Lin, Steve Jiang, Dan Nguyen. Deep learning guided radiation therapy machine selection via a multi-headed dose prediction model [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-07-01.

Abstract PO3-08-01: Decision curve analysis to compare breast cancer risk predictions for a polygenic integrated clinical risk model with those of a gold standard

Abstract Risk-based guidelines have been developed to provide advice on options that are available to women who are at increased risk of breast cancer. We and others are focused on improving risk prediction models for general population use. For breast cancer, polygenic risk has been identified as important for risk prediction, especially when combined with clinical risk factors such as family history, anthropometric measures, breast imaging measures, and hormonal and reproductive risk factors. When comparing the performance of risk prediction models, the AUC is most often used but it focuses on performance in terms of distinguishing between affected and unaffected individuals and does not take into account differences in the specificity (true negative rate) and sensitivity (true positive rate) of the models being compared. In contrast, decision curve analysis takes sensitivity and specificity into account and enables the comparison of the net benefit of risk prediction tools at the clinical risk thresholds that are used to guide clinical management decisions. Herein, we use decision curve analysis to compare breast cancer risk prediction guidelines for BRISK and the Breast Cancer Risk Assessment Tool (BCRAT) in the UK Biobank, and for BRISK and IBIS version 7 in the Nurses’ Health Study. We evaluated the net benefit at the 5-year risk thresholds (1.67% and 3%) that are used to guide clinical management decisions around risk-reducing medication and at the remaining lifetime risk of 20% that is used to guide supplemental MRI surveillance. In the UK Biobank, BRISK showed a net benefit over BCRAT at the 5-year risk thresholds of 1.67% and 3.0%. The AUCs were 0.649 (95% CI = 0.640, 0.695) for BRISK and 0.567 (95% CI = 0.556, 0.577) for BCRAT and showed that, overall, BRISK discriminated between affected and unaffected women better than BCRAT (P &amp;lt; 0.001). In the Nurses’ Health Study, BRISK showed a net benefit over IBIS at the remaining lifetime risk threshold of 20%. The AUCs were 0.647; 95% CI = 0.627, 0.668 for BRISK and 0.571; 95% CI = 0.546, 0.595 for IBIS, a statistically significant improvement for BRISK over IBIS (P &amp;lt; 0.0001). We also looked at sensitivity, specificity, positive predictive value and negative predictive value even though these risk models are one-step removed from breast cancer diagnosis by screening/risk-reduction options. The positive predictive values are higher for the BRISK (27.1%) model compared to BCRAT (5.5%) in the UK Biobank dataset and higher for BRISK (15.4%) compared to IBISv7 (14.0%) in the Nurses’ dataset. We have shown the application of a statistical tool that can be used to directly compare risk models at clinically relevant thresholds. We have shown that BRISK, which comprises polygenic risk and clinical risk factors, shows higher net benefit at both 1.67% and 3% thresholds compared to BCRAT and at 20% remaining lifetime risk for IBIS version 7. In breast cancer risk prediction, a tool with high specificity (and therefore a necessary trade-off for low sensitivity) is preferred because we do not want to incorrectly classify women as not being at high risk and deny them the opportunity for intervention in the form of more frequent screening, alternative modes of screening or risk-reducing medication. This improvement in general population risk stratification that BRISK can provide has the potential to have a clinically significant effect on women’s health. Citation Format: Erika Spaeth, Bernard A Rosner, Gill Dite. Decision curve analysis to compare breast cancer risk predictions for a polygenic integrated clinical risk model with those of a gold standard [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-08-01.

USER PERCEPTION TOWARDS PREDICTIVE ANALYTICS IN CREDIT RISK MANAGEMENT

This comprehensive review explores the dynamic landscape of predictive analytics in credit risk management within the banking sector. Anchored in a qualitative research design, the study synthesizes existing literature and real-world case studies to provide a multifaceted understanding of predictive analytics' role in modern banking. The review identifies key trends, highlighting the integration of predictive analytics across diverse banking operations, the transition to advanced machine learning algorithms, the democratization of predictive analytics tools, and the growing emphasis on ethical and regulatory compliance. It underscores the effectiveness of predictive analytics, showcasing its ability to enhance risk assessment precision, decision-making agility, and overall banking performance. Comparative analyses reveal the varying performance of predictive models across contexts, emphasizing the importance of tailored model selection. However, challenges such as data quality, model interpretability, talent scarcity, ethical considerations, and implementation costs pose significant hurdles. Looking forward, predictive analytics promises to be an indispensable tool for mitigating credit risk in the banking sector, offering refined risk assessments, smarter decisions, and enhanced resilience. The insights from this review provide valuable guidance for banking professionals, regulators, and researchers navigating the evolving landscape of predictive analytics in banking.

The adverse inpatient medication event and frailty (AIME-frail) risk prediction model

Medication harm affects between 5 and 15% of hospitalised patients, with approximately half of the harm events considered preventable through timely intervention. The Adverse Inpatient Medication Event (AIME) risk prediction model was previously developed to guide a systematic approach to patient prioritisation for targeted clinician review, but frailty was not tested as a candidate predictor variable. To evaluate the predictive performance of an updated AIME model, incorporating a measure of frailty, when applied to a new multisite cohort of hospitalised adult inpatients. A retrospective cohort study was conducted at two tertiary Australian hospitals on patients discharged between 1st January and April 31, 2020. Data were extracted from electronic medical records (EMRs) and clinical coding databases. Medication harm was identified using ICD-10 Y-codes and confirmed by senior pharmacist review of medical records. The Hospital Frailty Risk Score (HFRS) was calculated for each patient. Logistic regression analysis was used to construct a modified AIME model. Candidate variables of the original AIME model, together with new variables including HFRS were tested. Performance of the final model was reported using area under the curve (AUC) and decision curve analysis (DCA). A total of 4089 patient admissions were included, with a mean age±standard deviation (SD) of 64 years (±19 years), 2050 patients (50%) were males, and mean HFRS was 6.2 (±5.9). 184 patients (4.5%) experienced one or more medication harm events during hospitalisation. The new AIME-Frail risk model incorporated 5 of the original variables: length of stay (LOS), anti-psychotics, antiarrhythmics, immunosuppressants, and INR greater than 3, as well as 5 new variables: HFRS, anticoagulants, antibiotics, insulin, and opioid use. The AUC was 0.79 (95% CI: 0.76-0.83) which was superior to the original model (AUC=0.70, 95% CI: 0.65-0.74) with a sensitivity of 69%, specificity of 81%, positive predictive value of 0.14 (95% CI: 0.10-0.17) and negative predictive value of 0.98 (95% CI: 0.97-0.99). The DCA identified the model as having potential clinical utility between the probability thresholds of 0.05-0.4. The inclusion of a frailty measure improved the predictive performance of the AIME model. Screening inpatients using the AIME-Frail tool could identify more patients at high-risk of medication harm who warrant timely clinician review.

Late-xerostomia prediction model based on 18F-FDG PET image biomarkers of the main salivary glands

Background and purposeRecently, a comprehensive xerostomia prediction model was published, based on baseline xerostomia, mean dose to parotid glands (PG) and submandibular glands (SMG). Previously, PET imaging biomarkers (IBMs) of PG were shown to improve xerostomia prediction. Therefore, this study aimed to explore the potential improvement of the additional PET-IBMs from both PG and SMG to the recent comprehensive xerostomia prediction model (i.e., the reference model). Materials and methodsTotally, 540 head and neck cancer patients were split into training and validation cohorts. PET-IBMs from the PG and SMG, were selected using bootstrapped forward selection based on the reference model. The IBMs from both the PG and SMG with the highest selection frequency were added to the reference model, resulting in a PG-IBM model and a SMG-IBM model which were combined into a composite model. Model performance was assessed using the area under the curve (AUC). Likelihood ratio test compared the predictive performance between the reference model and models including IBMs. ResultsThe final selected PET-IBMs were 90th percentile of the PG SUV and total energy of the SMG SUV. The additional two PET-IBMs in the composite model improved the predictive performance of the reference model significantly. The AUC of the reference model and the composite model were 0.67 and 0.69 in the training cohort, and 0.71 and 0.73 in the validation cohort, respectively. ConclusionThe composite model including two additional PET-IBMs from PG and SMG improved the predictive performance of the reference xerostomia model significantly, facilitating a more personalized prediction approach.

Development and Validation of a Multimodality Model Based on Whole-Slide Imaging and Biparametric MRI for Predicting Postoperative Biochemical Recurrence in Prostate Cancer.

Purpose To develop and validate a machine learning multimodality model based on preoperative MRI, surgical whole-slide imaging (WSI), and clinical variables for predicting prostate cancer (PCa) biochemical recurrence (BCR) following radical prostatectomy (RP). Materials and Methods In this retrospective study (September 2015 to April 2021), 363 male patients with PCa who underwent RP were divided into training (n = 254; median age, 69 years [IQR, 64-74 years]) and testing (n = 109; median age, 70 years [IQR, 65-75 years]) sets at a ratio of 7:3. The primary end point was biochemical recurrence-free survival. The least absolute shrinkage and selection operator Cox algorithm was applied to select independent clinical variables and construct the clinical signature. The radiomics signature and pathomics signature were constructed using preoperative MRI and surgical WSI data, respectively. A multimodality model was constructed by combining the radiomics signature, pathomics signature, and clinical signature. Using Harrell concordance index (C index), the predictive performance of the multimodality model for BCR was assessed and compared with all single-modality models, including the radiomics signature, pathomics signature, and clinical signature. Results Both radiomics and pathomics signatures achieved good performance for BCR prediction (C index: 0.742 and 0.730, respectively) on the testing cohort. The multimodality model exhibited the best predictive performance, with a C index of 0.860 on the testing set, which was significantly higher than all single-modality models (all P ≤ .01). Conclusion The multimodality model effectively predicted BCR following RP in patients with PCa and may therefore provide an emerging and accurate tool to assist postoperative individualized treatment. Keywords: MR Imaging, Urinary, Pelvis, Comparative Studies Supplemental material is available for this article. © RSNA, 2024.

A thermoplastic clay constitutive model with temperature dependent evolution of stress anisotropy

This paper presents a thermomechanical constitutive model that captures temperature dependent evolutions of preconsolidation stress and stress anisotropy in normally consolidated and lightly overconsolidated saturated clays. Following a non-associative flow rule, the model was formulated to account for the rate of evolution of stress anisotropy as a function of temperature. A temperature-dependent rotational hardening parameter was introduced and calibrated employing a simple optimization algorithm for four different clays. The developed model was further implemented in a finite element (FE) analysis software for use in boundary value problems. Success of such numerical implementation and predictive performance of the constitutive model was further verified through FE simulations of drained and undrained triaxial tests on saturated clays at reference and elevated temperature. FEA results obtained from these simulations agreed very well with test data reported in the literature.

Interpretable Prediction of SARS-CoV-2 Epitope-Specific TCR Recognition Using a Pre-Trained Protein Language Model.

The emergence of the novel coronavirus, designated as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has posed a significant threat to public health worldwide. There has been progress in reducing hospitalizations and deaths due to SARS-CoV-2. However, challenges stem from the emergence of SARS-CoV-2 variants, which exhibit high transmission rates, increased disease severity, and the ability to evade humoral immunity. Epitope-specific T-cell receptor (TCR) recognition is key in determining the T-cell immunogenicity for SARS-CoV-2 epitopes. Although several data-driven methods for predicting epitope-specific TCR recognition have been proposed, they remain challenging due to the enormous diversity of TCRs and the lack of available training data. Self-supervised transfer learning has recently been proven useful for extracting information from unlabeled protein sequences, increasing the predictive performance of fine-tuned models, and using a relatively small amount of training data. This study presents a deep-learning model generated by fine-tuning pre-trained protein embeddings from a large corpus of protein sequences. The fine-tuned model showed markedly high predictive performance and outperformed the recent Gaussian process-based prediction model. The output attentions captured by the deep-learning model suggested critical amino acid positions in the SARS-CoV-2 epitope-specific TCRβ sequences that are highly associated with the viral escape of T-cell immune response.

MiR-301b-3p can be Used as a Potential Marker for the Diagnosis of Lung Adenocarcinoma.

The involvement of aberrantly expressed miR-301b-3p has been discovered in diverse human tumors. Our study was primarily centered around the role of miR-301b-3p in diagnosing lung adenocarcinoma (LUAD). We used the TCGA database to download the TCGA-LUAD dataset and selected miR-301b-3p as the object of our study by differential expression analysis of miRNAs combined with previous studies. The LUAD diagnostic model was constructed utilizing machine learning based on miR-301b-3p expression. The predictive performance of the diagnostic model was found to be excellent by ROC curves combined with the clinical information of the dataset samples. GSEA, GO, and KEGG enrichment analyses demonstrated that miR-301b-3p may mediate the cell cycle by regulating the expression of hormones. Subsequently, combined with tumor immunity and mutation analysis, it was found that patients in the low-expression group had better immune infiltration, indicating that their response rate to immunotherapy may be relatively high. Finally, a mouse xenograft model was constructed to verify how miR-301b-3p affected LUAD progression in mice. The results illustrated that overexpressed miR-301b-3p could cause faster tumor growth in mice. On the contrary, the growth of LUAD could be impeded by the downregulated miR-301b-3p expression. It was suggested that miR-301b-3p had a crucial part in LUAD progression. Overall, the diagnostic performance of the LUAD diagnostic model constructed based on miR-301b-3p is great, and the model can be used as a potential diagnostic marker for LUAD to provide new ideas for clinical diagnosis.

Artificial Intelligence in Ship Trajectory Prediction

Maritime traffic is increasing more and more, creating more complex navigation environments for ships. Ship trajectory prediction based on historical AIS data is a vital method of reducing navigation risks and enhancing the efficiency of maritime traffic control. At present, employing machine learning or deep learning techniques to construct predictive models based on AIS data has become a focal point in ship trajectory prediction research. This paper systematically evaluates various trajectory prediction methods, spanning classical machine learning approaches and emerging deep learning techniques, to uncover their respective merits and drawbacks. In this work, a variety of studies were investigated that applied different algorithms in ship trajectory prediction, including regression models (RMs), artificial neural networks (ANNs), Kalman filtering (KF), and random forests (RFs) in machine learning, along with deep learning such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), gate recurrent unit (GRU) networks, and sequence-to-sequence (Seq2seq) networks. The performance of predictive models based on different algorithms in trajectory prediction tasks was graded and analyzed. Among the existing studies, deep learning methods exhibit significant performance and considerable potential application value for maritime traffic systems, which can be assessed by future work on ship trajectory prediction research.

Internal quality evaluation of ‘Fuji’ apples during storage based on bulk optical properties or diffuse reflection and transmission spectra

The purpose of this study is to compare the performance of prediction models based either on the bulk optical properties (BOP) or with models based on the diffuse reflection and transmission spectra to assess the soluble solids content (SSC) or fruit firmness (FF) of ‘Fuji’ apples. Diffuse reflection and transmission spectra of 240 apples in 500–1000 nm were acquired using a self-constructed single integrating sphere system, from which the absorption coefficient (μa) and reduced scattering coefficient (μ′s) were determined. The relationships of μa, μ′s, and the reflection and transmission spectra with the SSC and FF were analyzed, and detection models were established using partial least squares regression coupled with characteristic wavelength selection. Results showed that neither the SSC nor the FF changed obviously during 80-days storage, and the μa and μ′s showed no monotonic increase or decrease pattern. The SSC prediction model based on μa spectra combined with the baseline correction method performed the best (R2p = 0.79, RMSEP = 0.62), while the model based on μ′s spectra was the best for FF (R2p = 0.67, RMSEP = 0.35 N). Overall, the accuracy of the model based on BOP in predicting internal quality was better than that based on reflection and transmission spectra from the same measurement.

Development of interpretable machine learning models for prediction of acute kidney injury after noncardiac surgery: a retrospective cohort study.

Early identification of patients at high-risk of postoperative acute kidney injury (AKI) can facilitate the development of preventive approaches. This study aimed to develop prediction models for postoperative AKI in noncardiac surgery using machine learning algorithms. The authors also evaluated the predictive performance of models that included only preoperative variables or only important predictors. Adult patients undergoing noncardiac surgery were retrospectively included in the study (76457 patients in the discovery cohort and 11910 patients in the validation cohort). AKI was determined using the KDIGO criteria. The prediction model was developed using 87 variables (56 preoperative variables and 31 intraoperative variables). A variety of machine learning algorithms were employed to develop the model, including logistic regression, random forest, extreme gradient boosting, and gradient boosting decision trees. The performance of different models was compared using the area under the receiver operating characteristic curve (AUROC). Shapley Additive Explanations (SHAP) analysis was employed for model interpretation. The patients in the discovery cohort had a median age of 52 years (IQR: 42-61 years), and 1179 patients (1.5%) developed AKI after surgery. The gradient boosting decision trees algorithm showed the best predictive performance using all available variables, or only preoperative variables. The AUROCs were 0.849 (95% CI: 0.835-0.863) and 0.828 (95% CI: 0.813-0.843), respectively. The SHAP analysis showed that age, surgical duration, preoperative serum creatinine, and gamma-glutamyltransferase, as well as American Society of Anesthesiologists physical status III were the most important five features. When gradually reducing the features, the AUROCs decreased from 0.852 (including the top 40 features) to 0.839 (including the top 10 features). In the validation cohort, the authors observed a similar pattern regarding the models' predictive performance. The machine learning models the authors developed had satisfactory predictive performance for identifying high-risk postoperative AKI patients. Furthermore, the authors found that model performance was only slightly affected when only preoperative variables or only the most important predictive features were included.

Predictive modeling for cow's milk allergy remission by low-dose oral immunotherapy in young children

BackgroundThe effectiveness of slow low-dose oral immunotherapy (SLOIT) for cow's milk (CM) allergy has been reported. Most OIT studies have discussed the target populations over 4 years old. Furthermore, no predicting modeling is reported for CM allergy remission by CM-SLOIT under 4 years of age. ObjectiveWe sought to develop a predictive model for CM allergy remission by SLOIT after 3 years in young children who started CM-SLOIT under 4 years of age. MethodsWe included young children with cow's milk allergy or cow's milk sensitization (development modeling set with 120 children and validation modeling set with 71 children). We did logistic regression analysis to develop the models. We calculated the area under the receiver operating curves (ROC-AUCs) to evaluate the predictive modeling performance. ResultsThe model (CM-sIgE before SLOIT + age at beginning SLOIT + serum TARC before starting SLOIT + CM-sIgE titer one year after OIT) showed good discrimination with the ROC-AUC of 0.83 (95% CI:0.76–0.91) on internal validation. Applying the model to the validation set gave good discrimination (ROC-AUC = 0.89, 95% CI:0.80–0.97) and a reasonable calibration (intraclass correlation coefficient = 0.88, 95% CI:0.62–0.97). ConclusionWe developed and validated predictive modeling for determining the remission rate of CM allergy at 3 years after SLOIT under 4 years of age in children with CM allergy. This predictive model is highly accurate and can support CM allergy management. (226 words)

Machine Learning-Based Prediction of Stability in High-Entropy Nitride Ceramics

The field of materials science has experienced a transformative shift with the emergence of high-entropy materials (HEMs), which possess a unique combination of properties that traditional single-phase materials lack. Among these, high-entropy nitrides (HENs) stand out for their exceptional mechanical strength, thermal stability, and resistance to extreme environments, making them highly sought after for applications in aerospace, defense, and energy sectors. Central to the design of these materials is their entropy forming ability (EFA), a measure of a material’s propensity to form a single-phase, disordered structure. This study introduces the application of the sure independence screening and sparsifying operator (SISSO), a machine learning technique, to predict the EFA of HEN ceramics. By utilizing a rich dataset curated from theoretical computational data, SISSO has been trained to identify the most critical features contributing to EFA. The model’s strong interpretability allows for the extraction of complex mathematical expressions, providing deep insights into the material’s composition and its impact on EFA. The predictive performance of the SISSO model is meticulously validated against theoretical benchmarks and compared with other machine learning methodologies, demonstrating its superior accuracy and reliability. This research not only contributes to the growing body of knowledge on HEMs but also paves the way for the efficient discovery and development of new HEN materials with tailored properties for advanced technological applications.

Benchmarking clinical risk prediction algorithms with ensemble machine learning for the noninvasive diagnosis of liver fibrosis in NAFLD.

Ensemble machine-learning methods, like the superlearner, combine multiple models into a single one to enhance predictive accuracy. Here we explore the potential of the superlearner as a benchmarking tool for clinical risk prediction, illustrating the approach to identifying significant liver fibrosis among patients with NAFLD. We used 23 demographic/clinical variables to train superlearner(s) on data from the NASH-clinical research network observational study (n = 648) and validated models with data from the FLINT trial (n = 270) and National Health and Nutrition Examination Survey (NHANES) participants with NAFLD (n = 1244). Comparing the superlearner's performance to existing models (Fibrosis-4 [FIB-4], NAFLD fibrosis score, Forns, AST to Platelet Ratio Index [APRI], BARD, and Steatosis-Associated Fibrosis Estimator [SAFE]), it exhibited strong discriminative ability in the FLINT and NHANES validation sets, with AUCs of 0.79 (95% CI: 0.73-0.84) and 0.74 (95% CI: 0.68-0.79) respectively. Notably, the SAFE score performed similarly to the superlearner, both of which outperformed FIB-4, APRI, Forns, and BARD scores in the validation data sets. Surprisingly, the superlearner derived from 12 base models matched the performance of one with 90 base models. Overall, the superlearner, being the "best-in-class" machine-learning predictor, excelled in detecting fibrotic NASH, and this approach can be used to benchmark the performance of conventional clinical risk prediction models.

Battery degradation stage detection and life prediction without accessing historical operating data

Degradation stage detection and life prediction are important for battery health management and safe reuse. This study first proposes a method of detecting whether a battery has entered a rapid degradation stage without accessing historical operating data. In addition, to alleviate the burden of extensive training data, an effective method of selecting training data with high physical similarity to the test data is developed improving the model's overall performance. Six physics-informed features are extracted from an equivalent circuit model using the relaxation voltage. Unsupervised learning is employed to analyze the internal physics similarity of batteries. The relationship between features and degradation stages or battery life is described by the Gaussian process-based machine learning model. The proposed method is validated using 65 batteries of two types. The results demonstrate that the detection accuracy of the degradation stage exceeds 90 %, and the performance of the life prediction model achieves an improvement of up to 53.56 % in terms of the root mean square error compared to that of the benchmark. Both detection and prediction can be independent of historical data, showing promise in assessing whether a battery can be used in the second life and predicting battery life in real time.