Logistic regression analysis and machine learning for predicting post-stroke gait independence: a retrospective study.

This study investigated whether machine learning (ML) has better predictive accuracy than logistic regression analysis (LR) for gait independence at discharge in subacute stroke patients (n = 843) who could not walk independently at admission. We developed prediction models using LR and five ML algorithms-specifically, the decision tree (DT), support vector machine, artificial neural network, ensemble learning, and k-nearest neighbor methods. Functional Independence Measure sub-items were used to evaluate the ability to walk independently. Model predictive accuracies were evaluated using areas under receiver operating characteristic curves (AUCs) as well as accuracy, precision, recall, F1 score, and specificity. The AUC for DT (0.812) was significantly lower than those for the other algorithms (p < 0.01); however, the AUC for LR (0.895) did not differ significantly from those for the other models (0.893-0.903). Other performance metrics showed no substantial differences between LR and ML algorithms. In conclusion, the DT algorithm had significantly low predictive accuracy, and LR showed no significant difference in predictive accuracy compared with the other ML algorithms. As its predictive accuracy is similar to that of ML, LR can continue to be used for predicting the prognosis of gait independence, with additional advantages of being easily understandable and manually computable.

Design and implementation of a portable snapshot multispectral imaging crop-growth sensor.

The timely and accurate acquisition of crop-growth information is a prerequisite for implementing intelligent crop-growth management, and portable multispectral imaging devices offer reliable tools for monitoring field-scale crop growth. To meet the demand for obtaining crop spectra information over a wide band range and to achieve the real-time interpretation of multiple growth characteristics, we developed a novel portable snapshot multispectral imaging crop-growth sensor (PSMICGS) based on the spectral sensing of crop growth. A wide-band co-optical path imaging system utilizing mosaic filter spectroscopy combined with dichroic mirror beam separation is designed to acquire crop spectra information over a wide band range and enhance the device's portability and integration. Additionally, a sensor information and crop growth monitoring model, coupled with a processor system based on an embedded control module, is developed to enable the real-time interpretation of the aboveground biomass (AGB) and leaf area index (LAI) of rice and wheat. Field experiments showed that the prediction models for rice AGB and LAI, constructed using the PSMICGS, had determination coefficients (R²) of 0.7 and root mean square error (RMSE) values of 1.611 t/ha and 1.051, respectively. For wheat, the AGB and LAI prediction models had R² values of 0.72 and 0.76, respectively, and RMSE values of 1.711 t/ha and 0.773, respectively. In summary, this research provides a foundational tool for monitoring field-scale crop growth, which is important for promoting high-quality and high-yield crops.

Understanding how a personalized risk prediction tool (VALUE-PERSARC) supports informed treatment decisions of soft-tissue sarcomas patients in daily clinical practice - A mixed methods study.

INTRODUCTION: Risk prediction models (RPM) can help soft-tissue sarcoma(STS) patients and clinicians make informed treatment decisions by providing them with estimates of (disease-free) survival for different treatment options. However, it is unknown how RPMs are used in the clinical encounter to support decision-making. This study aimed to understand how a PERsonalised SARcoma Care (PERSARC) RPM is used to support treatment decisions and which barriers and facilitators influence its use in daily clinical practice. METHODS: A convergent mixed-methods design is used to understand how PERSARC is integrated in the clinical encounter in three Dutch sarcoma centers. Data were collected using qualitative interviews with STS patients (n = 15) and clinicians (n = 8), quantitative surveys (n = 50) and audiotaped consultations (n = 30). Qualitative data were analyzed using thematic analysis and integrated with quantitative data through merging guided by the SEIPS model. RESULTS: PERSARC was generally used to support clinicians' proposed treatment plan and not to help patients weigh available treatment options. Use of PERSARC in decision-making was hampered by clinician's doubts about whether there were multiple viable treatment options,the accuracy of risk estimates, and time constraints. On the other hand, use of PERSARC facilitated clinicians to estimate and communicate the expected benefit of adjuvant therapy to patients. CONCLUSION: PERSARC was not used to support informed treatment decision-making in STS patients. Integrating RPMs into clinical consultations requires acknowledgement of their benefits in facilitating clinicians' estimation of the expected benefit of adjuvant therapies and information provision to patients, while also considering concerns regarding RPM quality and treatment options' viability.

Correlation between coronary calcification and cardiac structure in non-dialysis patients with chronic kidney disease.

AIMS: We aim to explore the correlation between coronary artery calcification (CAC) score (CACS) and cardiac structure and function in chronic kidney disease (CKD) patients, create a clinical prediction model for severe CAC associated with cardiac ultrasound indexes. METHODS AND RESULTS: The study included 178 non-dialysis CKD patients who underwent CACS testing and collected general information, serological indices, cardiac ultrasound findings and follow-up on renal function, heart failure (HF) manifestations and re-hospitalization. The mean age of participants in the study cohort was 67.4 years; 59% were male, and 66.9% of patients had varying degrees of comorbid CAC. CKD patients with CACS > 100 were older, predominantly male and had a higher proportion of smoking, diabetes and hypertension (P < 0.05) compared with those with CACS = 0 and 0 < CACS ≤ 100, and had higher brain natriuretic peptide, serum magnesium and fibrinogen levels were also higher (P < 0.05). CACS was positively correlated with left atrial inner diameter (LAD), left ventricular end-diastolic inner diameter (LVDd), left ventricular volume at diastole (LVVd), output per beat (SV) and mitral orifice early diastolic blood flow velocity/early mitral annular diastolic myocardial motion velocity (E/e) (P < 0.05). We tested the associations between varying degrees of CAC and HF and heart valve calcification using multivariable-adjusted regression models. The risk of HF in patients with severe CAC was about 1.95 times higher than that in patients without coronary calcification, and the risk of heart valve calcification was 2.46 times higher than that in patients without coronary calcification. Heart valve calcification and HF diagnosis, LAD and LVDd are essential in predicting severe CAC. During a mean follow-up time of 18.26 ± 10.17 months, 65 (36.52%) patients had a composite renal endpoint event, of which 36 (20.22%) were admitted to renal replacement therapy. Patients with severe CAC had a higher risk of progression of renal function, re-admission due to cardiovascular and renal events and more pronounced symptoms of HF (P < 0.05). CONCLUSIONS: There is a correlation between CACS and cardiac structure and function in non-dialysis CKD patients, which may mainly involve abnormalities in left ventricular structure and cardiac diastolic function. CAC may affect renal prognosis and quality of survival in CKD patients. Based on clinical information, HF, valvular calcification status and indicators related to left ventricular hypertrophy can identify people at risk for severe CAC.

Clinician perspectives and recommendations regarding design of clinical prediction models for deteriorating patients in acute care.

BACKGROUND: Successful deployment of clinical prediction models for clinical deterioration relates not only to predictive performance but to integration into the decision making process. Models may demonstrate good discrimination and calibration, but fail to match the needs of practising acute care clinicians who receive, interpret, and act upon model outputs or alerts. We sought to understand how prediction models for clinical deterioration, also known as early warning scores (EWS), influence the decision-making of clinicians who regularly use them and elicit their perspectives on model design to guide future deterioration model development and implementation. METHODS: Nurses and doctors who regularly receive or respond to EWS alerts in two digital metropolitan hospitals were interviewed for up to one hour between February 2022 and March 2023 using semi-structured formats. We grouped interview data into sub-themes and then into general themes using reflexive thematic analysis. Themes were then mapped to a model of clinical decision making using deductive framework mapping to develop a set of practical recommendations for future deterioration model development and deployment. RESULTS: Fifteen nurses (n = 8) and doctors (n = 7) were interviewed for a mean duration of 42 min. Participants emphasised the importance of using predictive tools for supporting rather than supplanting critical thinking, avoiding over-protocolising care, incorporating important contextual information and focusing on how clinicians generate, test, and select diagnostic hypotheses when managing deteriorating patients. These themes were incorporated into a conceptual model which informed recommendations that clinical deterioration prediction models demonstrate transparency and interactivity, generate outputs tailored to the tasks and responsibilities of end-users, avoid priming clinicians with potential diagnoses before patients were physically assessed, and support the process of deciding upon subsequent management. CONCLUSIONS: Prediction models for deteriorating inpatients may be more impactful if they are designed in accordance with the decision-making processes of acute care clinicians. Models should produce actionable outputs that assist with, rather than supplant, critical thinking.

Research progress on predictive models for malnutrition in cancer patients.

Disease-related malnutrition is a prevalent issue among cancer patients, affecting approximately 40-80% of those undergoing treatment. This condition is associated with numerous adverse outcomes, including extended hospitalization, increased morbidity and mortality, delayed wound healing, compromised muscle function and reduced overall quality of life. Moreover, malnutrition significantly impedes patients' tolerance of various cancer therapies, such as surgery, chemotherapy, and radiotherapy, resulting in increased adverse effects, treatment delays, postoperative complications, and higher referral rates. At present, numerous countries and regions have developed objective assessment models to predict the risk of malnutrition in cancer patients. As advanced technologies like artificial intelligence emerge, new modeling techniques offer potential advantages in accuracy over traditional methods. This article aims to provide an exhaustive overview of recently developed models for predicting malnutrition risk in cancer patients, offering valuable guidance for healthcare professionals during clinical decision-making and serving as a reference for the development of more efficient risk prediction models in the future.

A Clinician's Guide to Understanding Bias in Critical Clinical Prediction Models.

This narrative review focuses on the role of clinical prediction models in supporting informed decision-making in critical care, emphasizing their 2 forms: traditional scores and artificial intelligence (AI)-based models. Acknowledging the potential for both types to embed biases, the authors underscore the importance of critical appraisal to increase our trust in models. The authors outline recommendations and critical care examples to manage risk of bias in AI models. The authors advocate for enhanced interdisciplinary training for clinicians, who are encouraged to explore various resources (books, journals, news Web sites, and social media) and events (Datathons) to deepen their understanding of risk of bias.

Prediction and analysis of dominant factors influencing moisture content during vacuum screening based on machine learning.

The study of the dominant factors influencing moisture content is essential for investigating vacuum filtration mechanisms. In view of the present situation where there is insufficient experimental data and the dominant factors influencing the moisture content of a filter cake have not been identified, in this study a vacuum filtration apparatus was designed and constructed. Quartz sand particles were used as the filtration material. 300 datasets of moisture contents of a filter cake were obtained under different experimental conditions. Multiple Linear Regression, artificial neural network, decision tree, random forest, and extreme gradient boosting were used to establish a prediction model for moisture content during vacuum screening. By comprehensively analyzing the feature importance rankings and the effects of positive and negative correlations, the dominant factors influencing the moisture content of the filter cake during vacuum screening were the particle ratio, screen mesh, and airflow rate. This finding not only provides a scientific basis for the optimization of vacuum screening technology but also points the way for improving screening efficiency in practical applications. It is of significant importance for deepening the understanding of the vacuum screening mechanism and promoting its extensive application.

Validation of the RIETE, Kuijer, and HAS-BLED Models to Assess 3-Month Bleeding Risk in Anticoagulated Patients Diagnosed with Venous Thromboembolic Disease.

OBJECTIVE: To evaluate the discriminative ability and calibration of the RIETE, Kuijer, and HAS-BLED models for predicting 3-month bleeding risk in patients anticoagulated for venous thromboembolism (VTE). METHODS: External validation study of a prediction model based on a retrospective cohort of patients with VTE seen at the Hospital Universitario San Ignacio, Bogotá (Colombia) between July 2021 and June 2023. The calibration of the scales was evaluated using the Hosmer-Lemeshow test and the ratio of observed to expected events (ROE) within each risk category. Discriminatory ability was assessed using the area under the curve (AUC) of a ROC curve. RESULTS: We analyzed 470 patients (median age 65 years, female sex 59.3%) with a diagnosis of deep vein thrombosis in most cases (57.4%), 5.7% bleeding events were observed. Regarding calibration, adequate calibration cannot be ruled out given the limited number of events. The discriminatory ability was limited with an area under the curve (AUC) of 0.48 (CI 0.37-0.59) for Kuijer Score, 0.58 (CI 0.47-0.70) for HAS-BLED and 0.64 (CI 0.51-0.76) for RIETE. CONCLUSION: The Kuijer, HAS-BLED, and RIETE models in patients with VTE generally do not adequately estimate the risk of bleeding at three months, with a low ability to discriminate high-risk patients. Cautious interpretation is recommended until further evidence is available.

Establishment and evaluation of a clinical prediction model for cognitive impairment in patients with cerebral small vessel disease.

BACKGROUND: There are currently no effective prediction methods for evaluating the occurrence of cognitive impairment in patients with cerebral small vessel disease (CSVD). AIMS: To investigate the risk factors for cognitive dysfunction in patients with CSVD and to construct a risk prediction model. METHODS: A retrospective study was conducted on 227 patients with CSVD. All patients were assessed by brain magnetic resonance imaging (MRI), and the Montreal Cognitive Assessment (MoCA) was used to assess cognitive status. In addition, the patient's medical records were also recorded. The clinical data were divided into a normal cognitive function group and a cognitive impairment group. A MoCA score < 26 (an additional 1 point for education < 12 years) is defined as cognitive dysfunction. RESULTS: A total of 227 patients (mean age 66.7 ± 6.99 years) with CSVD were included in this study, of whom 68.7% were male and 100 patients (44.1%) developed cognitive impairment. Age (OR = 1.070; 95% CI = 1.015 ~ 1.128, p < 0.05), hypertension (OR = 2.863; 95% CI = 1.438 ~ 5.699, p < 0.05), homocysteine(HCY) (OR = 1.065; 95% CI = 1.005 ~ 1.127, p < 0.05), lacunar infarct score(Lac_score) (OR = 2.732; 95% CI = 1.094 ~ 6.825, P < 0.05), and CSVD total burden (CSVD_score) (OR = 3.823; 95% CI = 1.496 ~ 9.768, P < 0.05) were found to be independent risk factors for cognitive decline in the present study. The above 5 variables were used to construct a nomogram, and the model was internally validated by using bootstrapping with a C-index of 0.839. The external model validation C-index was 0.867. CONCLUSIONS: The nomogram model based on brain MR images and clinical data helps in individualizing the probability of cognitive impairment progression in patients with CSVD.

A Perovskite Material Screening and Performance Study Based on Asymmetric Convolutional Blocks.

This study introduces an innovative method for identifying high-efficiency perovskite materials using an asymmetric convolution block (ACB). Our approach involves preprocessing extensive data on perovskite oxide materials and developing a precise predictive model. This system is designed to accurately predict key properties such as band gap and stability, thereby eliminating the reliance on traditional feature importance filtering. It exhibited outstanding performance, achieving an accuracy of 96.8% and a recall of 0.998 in classification tasks, and a coefficient of determination (R2) value of 0.993 with a mean squared error (MSE) of 0.004 in regression tasks. Notably, DyCoO3 and YVO3 were identified as promising candidates for photovoltaic applications due to their optimal band gaps. This efficient and precise method significantly advances the development of advanced materials for solar cells, providing a robust framework for rapid material screening.

Establishment and validation of a nomogram for predicting the risk of hip fracture in patients with stroke: A multicenter retrospective study.

PURPOSE: There are currently no models for predicting hip fractures after stroke. This study wanted to investigate the risk factors leading to hip fracture in stroke patients and to establish a risk prediction model to visualize this risk. PATIENTS AND METHODS: We reviewed 439 stroke patients with or without hip fractures admitted to the Affiliated Hospital of Xuzhou Medical University from June 2014 to June 2017 as the training set, and collected 83 patients of the same type from the First Affiliated Hospital of Harbin Medical University and the Affiliated Hospital of Xuzhou Medical University from June 2020 to June 2023 as the testing set. Patients were divided into fracture group and non-fracture group based on the presence of hip fractures. Multivariate logistic regression analysis was used to screen for meaningful factors. Nomogram predicting the risk of hip fracture occurrence were created based on the multifactor analysis, and performance was evaluated using receiver operating characteristic curve (ROC), calibration curves, and decision curve analysis (DCA). A web calculator was created to facilitate a more convenient interactive experience for clinicians. RESULTS: In training set, there were 35 cases (7.9 %) of hip fractures after stroke, while in testing set, this data was 13 cases (15.6 %). In training set, univariate analysis showed significant differences between the two groups in the number of falls, smoking, hypertension, glucocorticoids, number of strokes, Mini-Mental State Examination (MMSE), visual acuity level, National Institute of Health stroke scale (NIHSS), Berg Balance Scale (BBS), and Stop Walking When Talking (SWWT) (P<0.05). Multivariate analysis showed that number of falls [OR=17.104, 95 % CI (3.727-78.489), P = 0.000], NIHSS [OR=1.565, 95 % CI (1.193-2.052), P = 0.001], SWWT [OR=12.080, 95 % CI (2.398-60.851), P = 0.003] were independent risk factors positively associated with new fractures. BMD [OR = 0.155, 95 % CI (0.044-0.546), P = 0.012] and BBS [OR = 0.840, 95 % CI (0.739-0.954), P = 0.007] were negatively associated with new fractures. The area under the curve (AUC) of nomogram were 0.939 (95 % CI: 0.748-0.943) and 0.980 (95 % CI: 0.886-1.000) in training and testing sets, respectively, and the calibration curves showed a high agreement between predicted and actual status with an area under the decision curve of 0.034 and 0.109, respectively. CONCLUSIONS: The number of falls, fracture history, low BBS score, high NIHSS score, and positive SWWT are risk factors for hip fracture after stroke. Based on this, a nomogram with high accuracy was developed and a web calculator (https://stroke.shinyapps.io/DynNomapp/) was created.

External validation of two clinical prediction models for pediatric pneumonia.

OBJECTIVE: To externally validate two prediction models for pediatric radiographic pneumonia. METHODS: We prospectively evaluated the performance of two prediction models (Pneumonia Risk Score [PRS] and CARPE DIEM models) from a prospective convenience sample of children 90 days - 18 years of age from a pediatric emergency department undergoing chest radiography for suspected pneumonia between January 1, 2022, to December 31st, 2023. We evaluated model performance using the original intercepts and coefficients and evaluated for performance changes when performing recalibration and re-estimation procedures. RESULTS: We included 202 patients (median age 3 years, IQR 1-6 years), of whom radiographic pneumonia was found in 92 (41.0%). The PRS model had an area under the receiver operator characteristic curve of 0.72 (95% confidence interval [CI] 0.64-0.79), which was higher than the CARPE DIEM (0.59; 95% CI 0.51-0.67) (P<0.01). Using optimal cutpoints, the PRS model showed higher sensitivity (65.2%, 95% CI 54.6-74.9) and specificity (72.7%, 95% CI 63.4-80.8) compared to the CARPE DIEM model (sensitivity 56.5 [95% CI 45.8-66.8]; specificity 60.9 [95% CI 50.2-69.2]). Recalibration and re-estimation of models improved performance, particularly for the CARPE DIEM model, with gains in sensitivity and specificity, and improved calibration. CONCLUSION: The PRS model demonstrated better performance than the CARPE DIEM model in predicting radiographic pneumonia. Among children with a high rate of pneumonia, these models did not reach a level of performance sufficient to be used independently of clinical judgement. These findings highlight the need for further validation and improvement of models to enhance their utility.

Development of an IVF prediction model for donor oocytes: a retrospective analysis of 10 877 embryo transfers.

STUDY QUESTION: Can we develop a prediction model for the chance of a live birth following the transfer of an embryo created using donated oocytes? SUMMARY ANSWER: Three primary models that included patient, past treatment, and cycle characteristics were developed using Australian data to predict the chance of a live birth following the transfer of an embryo created using donated oocytes; these models were well-calibrated to the population studied, achieved reasonable predictive power and generalizability when tested on New Zealand data. WHAT IS KNOWN ALREADY: Nearly 9% of ART embryo transfer cycles performed globally use embryos created using donated oocytes. This percentage rises to one-quarter and one-half in same-sex couples and women aged over 45 years, respectively. STUDY DESIGN, SIZE, DURATION: This study uses population-based Australian clinical registry data comprising 9384 embryo transfer cycles that occurred between 2015 and 2021 for model development, with an external validation cohort of 1493 New Zealand embryo transfer cycles. PARTICIPANTS/MATERIALS, SETTING, METHODS: Three prediction models were compared that incorporated patient characteristics, but differed in whether they considered use of prior autologous treatment factors and current treatment parameters. We internally validated the models on Australian data using grouped cross-validation and reported several measures of model discrimination and calibration. Variable importance was measured through calculating the change in predictive performance that resulted from variable permutation. The best-performing model was externally validated on data from New Zealand. MAIN RESULTS AND THE ROLE OF CHANCE: The best-performing model had an internal validation AUC-ROC of 0.60 and Brier score of 0.20, and external validation AUC-ROC of 0.61 and Brier score of 0.23. While these results indicate ∼15% less discriminatory ability compared to models assessed on an autologous cohort from the same population the performance of the models was clearly statistically significantly better than random, demonstrated generalizability, and was well-calibrated to the population studied. The most important variables for predicting the chance of a live birth were the oocyte donor age, the number of prior oocyte recipient embryo transfer cycles, whether the transferred embryo was cleavage or blastocyst stage and oocyte recipient age. Of lesser importance were the oocyte-recipient parity, whether donor or partner sperm was used, the number of prior autologous embryo transfer cycles and the number of embryos transferred. LIMITATIONS, REASONS FOR CAUTION: The models had relatively weak discrimination suggesting further features need to be added to improve their predictive power. Variation in donor oocyte cohorts across countries due to differences such as whether anonymous and compensated donation are allowed may necessitate the models be recalibrated prior to application in non-Australian cohorts. WIDER IMPLICATIONS OF THE FINDINGS: These results confirm the well-established importance of oocyte age and ART treatment history as the key prognostic factors in predicting treatment outcomes. One of the developed models has been incorporated into a consumer-facing website (YourIVFSuccess.com.au/Estimator) to allow patients to obtain personalized estimates of their chance of success using donor oocytes. STUDY FUNDING/COMPETING INTEREST(S): This research was funded by the Australian government as part of the Medical Research Future Fund (MRFF) Emerging Priorities and Consumer Driven Research initiative: EPCD000007. L.R. declares personal consulting fees from Abbott and Merck, lecture fees from Abbott, receipt of an educational grant from Merck, past presidency of the Fertility Society of Australia & New Zealand and World Endometriosis Society and being a minor shareholder in Monash IVF Group (ASX:MVF). G.M.C. declares receipt of Australian government grant funding for the research study and the development and maintenance of the YourIVFSuccess website. O.F., J.N., and A.P. report no conflicts of interest. TRIAL REGISTRATION NUMBER: N/A.

Preoperative OCT Characteristics Contributing to Prediction of Postoperative Visual Acuity in Eyes with Macular Hole.

Objectives: To develop a machine learning logistic regression algorithm that can classify patients with an idiopathic macular hole (IMH) into those with good or poor vison at 6 months after a vitrectomy. In addition, to determine its accuracy and the contribution of the preoperative OCT characteristics to the algorithm. Methods: This was a single-center, cohort study. The classifier was developed using preoperative clinical information and the optical coherence tomographic (OCT) findings of 43 eyes of 43 patients who had undergone a vitrectomy. The explanatory variables were selected using a filtering method based on statistical significance and variance inflation factor (VIF) values, and the objective variable was the best-corrected visual acuity (BCVA) at 6 months postoperation. The discrimination threshold of the BCVA was the 0.15 logarithm of the minimum angle of the resolution (logMAR) units. Results: The performance of the classifier was 0.92 for accuracy, 0.73 for recall, 0.60 for precision, 0.74 for F-score, and 0.84 for the area under the curve (AUC). In logistic regression, the standard regression coefficients were 0.28 for preoperative BCVA, 0.13 for outer nuclear layer defect length (ONL_DL), -0.21 for outer plexiform layer defect length (OPL_DL) - (ONL_DL), and -0.17 for (OPL_DL)/(ONL_DL). In the IMH form, a stenosis pattern with a narrowing from the OPL to the ONL of the MH had a significant effect on the postoperative BCVA at 6 months. Conclusions: Our results indicate that (OPL_DL) - (ONL_DL) had a similar contribution to preoperative visual acuity in predicting the postoperative visual acuity. This model had a strong performance, suggesting that the preoperative visual acuity and MH characteristics in the OCT images were crucial in forecasting the postoperative visual acuity in IMH patients. Thus, it can be used to classify MH patients into groups with good or poor postoperative visual acuity, and the classification was comparable to that of previous studies using deep learning.

Quantitative Soil Characterization for Biochar-Cd Adsorption: Machine Learning Prediction Models for Cd Transformation and Immobilization.

Soil pollution with cadmium (Cd) poses serious health and environmental consequences. The study investigated the incubation of several soil samples and conducted quantitative soil characterization to assess the influence of biochar (BC) on Cd adsorption. The aim was to develop predictive models for Cd concentrations using statistical and modeling approaches dependent on soil characteristics. The potential risk linked to the transformation and immobilization of Cd adsorption by BC in the soil could be conservatively assessed by pH, clay, cation exchange capacity, organic carbon, and electrical conductivity. In this study, Long Short-Term Memory (LSTM), Bidirectional Gated Recurrent Unit (BiGRU), and 5-layer CNN Convolutional Neural Networks (CNNs) were applied for risk assessments to establish a framework for evaluating Cd risk in BC amended soils to predict Cd transformation. In the case of control soils (CK), the BiGRU model showed commendable performance, with an R2 value of 0.85, indicating an approximate 85.37% variance in the actual Cd. The LSTM model, which incorporates sequence data, produced less accurate results (R2=0.84), while the 5-layer CNN model had an R2 value of 0.91, indicating that the CNN model could account for over 91% of the variation in actual Cd levels. In the case of BC-applied soils, the BiGRU model demonstrated a strong correlation between predicted and actual values with R2 (0.93), indicating that the model explained 93.21% of the variance in Cd concentrations. Similarly, the LSTM model showed a notable increase in performance with BC-treated soil data. The R2 value for this model stands at a robust R2 (0.94), reflecting its enhanced ability to predict Cd levels with BC incorporation. Outperforming both recurrent models, the 5-layer CNN model attained the highest precision with an R2 value of 0.95, suggesting that 95.58% of the variance in the actual Cd data can be explained by the CNN model's predictions in BC-amended soils. Consequently, this study suggests developing ecological soil remediation strategies that can effectively manage heavy metal pollution in soils for environmental sustainability.

External validation of the Hong Kong Chinese non-laboratory risk models and scoring algorithm for case finding of prediabetes and diabetes mellitus in primary care.

AIMS/INTRODUCTION: Two Hong Kong Chinese non-laboratory-based prediabetes/diabetes mellitus (pre-DM/DM) risk models were developed using logistic regression (LR) and machine learning, respectively. We aimed to evaluate the models' validity in case finding of pre-DM/DM in a Chinese primary care (PC) population. We also evaluated the validity of a risk-scoring algorithm derived from the LR model. MATERIALS AND METHODS: This was a cross-sectional external validation study on Chinese adults, without a prior DM diagnosis, who were recruited from public/private PC clinics in Hong Kong. A total of 1,237 participants completed a questionnaire on the models' predictors. Of that, 919 underwent blood glucose testing. The primary outcome was the models' and the algorithm's sensitivity in finding pre-DM/DM cases. The secondary outcomes were the models' and the algorithm's specificity, positive/negative predictive values, discrimination and calibration. RESULTS: The models' sensitivity were 0.70 (machine learning) and 0.72 (LR). Both showed good external discrimination (area under the receiver operating characteristic curve: machine learning 0.744, LR 0.739). The risks estimated by the models were lower than the observed incidence, indicating poor calibration. Both models were more effective among participants with lower pretest probabilities; that is, age 18-44 years. The algorithm's sensitivity was 0.77 at the cut-off score of ≥16 out of 41. CONCLUSION: This study showed the validity of the models and the algorithm for finding pre-DM/DM cases in a Chinese PC population in Hong Kong. They can facilitate more cost-effective identification of high-risk individuals for blood testing to diagnose pre-DM/DM in PC. Further studies should recalibrate the models for more precise risk estimation in PC populations.

Rapid In Situ Near-Infrared Assessment of Tetrahydrocannabinolic Acid in Cannabis Inflorescences before Harvest Using Machine Learning.

Cannabis is cultivated for therapeutic and recreational purposes where delta-9 tetrahydrocannabinol (THC) is a main target for its therapeutic effects. As the global cannabis industry and research into cannabinoids expands, more efficient and cost-effective analysis methods for determining cannabinoid concentrations will be beneficial to increase efficiencies and maximize productivity. The utilization of machine learning tools to develop near-infrared (NIR) spectroscopy-based prediction models, which have been validated from accurate and sensitive chemical analysis, such as gas chromatography (GC) or liquid chromatography mass spectroscopy (LCMS), is essential. Previous research on cannabinoid prediction models targeted decarboxylated cannabinoids, such as THC, rather than the naturally occurring precursor, tetrahydrocannabinolic acid (THCA), and utilize finely ground cannabis inflorescence. The current study focuses on building prediction models for THCA concentrations in whole cannabis inflorescences prior to harvest, by employing non-destructive screening techniques so cultivators may rapidly characterize high-performing cultivars for chemotype in real time, thus facilitating targeted optimization of crossbreeding efforts. Using NIR spectroscopy and LCMS to create prediction models we can differentiate between high-THCA and even ratio classes with 100% prediction accuracy. We have also developed prediction models for THCA concentration with a R2 = 0.78 with a prediction error average of 13%. This study demonstrates the viability of a portable handheld NIR device to predict THCA concentrations on whole cannabis samples before harvest, allowing the evaluation of cannabinoid profiles to be made earlier, therefore increasing high-throughput and rapid capabilities.

Prediction models of intravenous glucocorticoids therapy response in thyroid eye disease.

Background: Thyroid eye disease (TED) is an autoimmune orbital disease, with intravenous glucocorticoid (IVGC) therapy as the first-line treatment. Due to uncertain response rates and possible side effects, various prediction models have been developed to predict IVGC therapy outcomes. Methods: A thorough search was conducted in PubMed, Embase, and Web of Science databases. Data extraction included publication details, prediction model content, and performance. Statistical analysis was performed using R software, including heterogeneity evaluation, publication bias, subgroup analysis, and sensitivity analysis. Forest plots were utilized for result visualization. Results: Of the 12 eligible studies, 47 prediction models were extracted. All included studies exhibited a low-to-moderate risk of bias. The pooled area under the receiver operating characteristic curve (AUC) and the combined sensitivity and specificity for the models were 0.81, 0.75, and 0.79, respectively. In view of heterogeneity, multiple meta-regression and subgroup analysis were conducted, which showed that marker and modeling types may be the possible causes of heterogeneity (P < 0.001). Notably, imaging metrics alone (AUC = 0.81) or clinical characteristics combined with other markers (AUC = 0.87), incorporating with multivariate regression (AUC = 0.84) or radiomics analysis (AUC = 0.91), yielded robust and reliable prediction outcomes. Conclusion: This meta-analysis comprehensively reviews the predictive models for IVGC therapy response in TED. It underscores that integrating clinical characteristics with laboratory or imaging indicators and employing advanced techniques like multivariate regression or radiomics analysis significantly enhance the efficacy of prediction. Our research findings offer valuable insights that can guide future studies on prediction models for IVGC therapy in TED.

Enhancing shear strength predictions of rocks using a hierarchical ensemble model.

Shear strength (SS) parameters are essential for understanding the mechanical behavior of materials, particularly in geotechnical engineering and rock mechanics. This study proposes a novel hierarchical ensemble model (HEM) to predict SS parameters: cohesion ( C ) and angle of internal friction ( φ ). The HEM addresses the limitations of traditional machine learning models. Its performance was validated using leave-one-out cross-validation (LOOCV) and out-of-bag (OOB) evaluation methods. The model's accuracy was assessed with R-squared correlation (R2), absolute average relative error percentage (AAREP), Taylor diagrams, and quantile-quantile plots. The computational results demonstrated that the proposed HEM outperforms previous studies using the same database. The model predicted φ and C with R2 values of 0.93 and 0.979, respectively. The AAREP values were 1.96% for φ and 4.7% for C . These results indicate that the HEM significantly improves the prediction quality of φ and C , and exhibits strong generalization capability. Sensitivity analysis revealed that σ_3maxσ3max (maximum principal stress) had the greatest impact on modeling both φ and C . According to uncertainty analysis, the LOOCV and OOB had the widest uncertainty bands for the φ and C parameters, respectively.