Development Of Predictive Models Research Articles

Key influences on dysglycemia across Fujian's urban-rural divide.

Screening and treatment of dysglycemia (prediabetes and diabetes) represent significant challenges in advancing the Healthy China initiative. Identifying the crucial factors contributing to dysglycemia in urban-rural areas is essential for the implementation of targeted, precise interventions. Data for 26,157 adults in Fujian Province, China, were collected using the Social Factors Special Survey Form through a multi-stage random sampling method, wherein 18 variables contributing to dysglycemia were analyzed with logistic regression and the random forest model. Investigating urban-rural differences and critical factors in dysglycemia prevalence in Fujian, China, with the simultaneous development of separate predictive models for urban and rural areas. The detection rate of dysglycemia among adults was 35.26%, with rates of 34.1% in urban areas and 35.8% in rural areas. Common factors influencing dysglycemia included education, age, BMI, hypertension, and dyslipidemia. For rural residents, higher income (OR = 0.80, 95% CI [0.74, 0.87]), average sleep quality (OR = 0.89, 95% CI [0.80, 0.99]), good sleep quality (OR = 0.89, 95% CI [0.80, 1.00]), and high physical activity (PA) (OR = 0.87, 95% CI [0.79, 0.96]) emerged as protective factors. Conversely, a daily sleep duration over 8 hours (OR = 1.46, 95% CI [1.03, 1.28]) and middle income (OR = 1.12, 95% CI [1.03, 1.22]) were specific risk factors. In urban areas, being male (OR = 1.14, 95% CI [1.02, 1.26]), cohabitation (OR = 1.18, 95% CI [1.02, 1.37]), and central obesity (OR = 1.35, 95% CI [1.19, 1.53]) were identified as unique risk factors. Using logistic regression outcomes, a random forest model was developed to predict dysglycemia, achieving accuracies of 75.35% (rural) and 76.95% (urban) with ROC areas of 0.77 (rural) and 0.75 (urban). This study identifies key factors affecting dysglycemia in urban and rural Fujian residents, including common factors such as education, age, BMI, hypertension, and dyslipidemia. Notably, rural-specific protective factors are higher income and good sleep quality, while urban-specific risk factors include being male and central obesity. These findings support the development of targeted prevention and intervention strategies for dysglycemia, tailored to the unique characteristics of urban and rural populations.

Development and validation of a COVID-19 vaccination prediction model based on self-reporting results in Chinese older adults from September 2022 to November 2022: A nationwide cross-sectional study

ABSTRACT It was common to see that older adults were reluctant to be vaccinated for coronavirus disease 2019 (COVID-19) in China. There is a lack of practical prediction models to guide COVID-19 vaccination program. A nationwide, self-reported, cross-sectional survey was conducted from September 2022 to November 2022, including people aged 60 years or older. Stratified random sampling was used to divide the dataset into derivation, validation, and test datasets at a ratio of 6:2:2. Least absolute shrinkage and selection operator and multivariable logistic regression were used for variable screening and model construction. Discrimination and calibration were assessed primarily by area under the receiver operating characteristic curve (AUC) and calibration curve. A total of 35057 samples (53.65% males and mean age of 69.64 ± 7.24 years) were finally selected, which constitutes 93.73% of the valid samples. From 33 potential predictors, 19 variables were screened and included in the multivariable logistic regression model. The mean AUC in the validation dataset was 0.802, with sensitivity, specificity, and accuracy of 0.732, 0.718 and 0.729 respectively, which were similar to the parameters in the test dataset of 0.755, 0.715 and 0.720, respectively, and the mean AUC in the test dataset was 0.815. There were no significant differences between the model predicted values and the actual observed values for calibration in these groups. The prediction model based on self-reported characteristics of older adults was developed that could be useful for predicting the willingness for COVID-19 vaccines, as well as providing recommendations in improving vaccine acceptance.

Accuracy and transportability of machine learning models for adolescent suicide prediction with longitudinal clinical records

Machine Learning models trained from real-world data have demonstrated promise in predicting suicide attempts in adolescents. However, their transportability, namely the performance of a model trained on one dataset and applied to different data, is largely unknown, hindering the clinical adoption of these models. Here we developed different machine learning-based suicide prediction models based on real-world data collected in different contexts (inpatient, outpatient, and all encounters) with varying purposes (administrative claims and electronic health records), and compared their cross-data performance. The three datasets used were the All-Payer Claims Database in Connecticut, the Hospital Inpatient Discharge Database in Connecticut, and the Electronic Health Records data provided by the Kansas Health Information Network. We included 285,320 patients among whom we identified 3389 (1.2%) suicide attempters and 66% of the suicide attempters were female. Different machine learning models were evaluated on source datasets where models were trained and then applied to target datasets. More complex models, particularly deep long short-term memory neural network models, did not outperform simpler regularized logistic regression models in terms of both local and transported performance. Transported models exhibited varying performance, showing drops or even improvements compared to their source performance. While they can achieve satisfactory transported performance, they are usually upper-bounded by the best performance of locally developed models, and they can identify additional new cases in target data. Our study uncovers complex transportability patterns and could facilitate the development of suicide prediction models with better performance and generalizability.

Development and validation of early prediction models for new-onset functional impairment of patients with trauma at hospital discharge.

Early identification of individuals at risk of functional impairment after trauma is crucial for the timely clinical decision-making and intervention to improve reintegration into the society. This study aimed to develop and validate models for predicting new-onset functional impairment after trauma using predictors that are routinely collected within 2 days of hospital admission. In this multicenter retrospective cohort study of acute care hospitals in Japan, we identified adult patients with trauma with independence in carrying out activities of daily living before hospitalization, treated in the intensive or high-dependency care unit, and survived for at least 2 days between April 2008 and September 2023. The primary outcome was functional impairment defined as Barthel Index ≤60 at hospital discharge. In the internal validation data set (between April 2008 and August 2022), using the routinely collected 129 candidate predictors within 2 days of admission, we trained and tuned the four conventional and machine learning models with repeated random subsampling cross-validation. We measured the performance of these models in the temporal validation data set (between September 2022 and September 2023). We also computed the importance of each predictor variable in our model. We identified 8,529 eligible patients. Functional impairment at discharge was observed in 41% of the patients (n = 3,506/8,529). In the temporal validation data set, all four models showed moderate discrimination ability, with areas under the curve above 0.79, and extreme gradient boosting showing the best performance (0.83). In the variable importance analyses, age was the most important predictor, followed by consciousness, severity score, cervical spinal cord injury, mild dementia, and serum albumin level at admission. We successfully developed early prediction models for patients with trauma with new-onset functional impairment at discharge that achieved high predictive performance using routinely collected data within 2 days of hospital admission. Prognostic/Epidemiological; Level II.

Development of Predictive Models for Tempering Behavior in Low-Carbon Bainitic Steel Using Integrated Tempering Parameters

Low-carbon bainitic steels are known for their excellent combination of strength and toughness, making them suitable for various industrial applications. Understanding the tempering behavior of these steels is crucial for optimizing their mechanical properties through heat treatment. This study presents predictive models for tempering behavior based on empirical data, which is fundamental for understanding the thermal stability and transformation kinetics of the steel. Through integrated tempering parameters, we established predictive models that integrate tempering temperature and time, yielding a robust framework for predicting hardness. The equivalent tempering kinetic curves and nomographs plotted in this study allow for the direct determination of hardness under various tempering conditions, facilitating the optimization of tempering parameters. The nomogram approach provides a practical method for adjusting tempering parameters to achieve desired mechanical properties efficiently. The accuracy of the predictive models was validated through statistical tests, demonstrating a high correlation between predicted and experimental values.

Quay Crane Performance Improvement and Lifecycle Extension: Retrofit Determination – A Case Study

Quay cranes (QCs) are critical equipment in Container Terminals and their performance determines the port operation efficiency in meeting operational Key Performance Indicators (KPIs). Considering 30-year lifespan of QCs and deteriorating reliability of the electrical control system, planning for timely retrofit of the QCs is critical. The paper aims to present a case study on how the performance of a batch of QCs, having more than 50% remaining lifespan, were analyzed and concluded to be retrofitted to address their deteriorating performance, high maintenance cost and obsolescence. The study methodology is based on quantitative data and employed a prospective longitudinal panel research design over sixteen years. eleven years of pre-retrofit and five years of post-retrofit operations, maintenance and financial data were collected. Convenient sampling technique was selected in this study as the QCs identified as samples were having reliability issues. The Correlation Analysis indicates strong positive correlation of more than 90% between Mean Time Between Failure, Mean Move Between Failure and Reliability KPIs. These KPIs have moderate positive correlations of about 50% with Availability KPI. These 4 KPIs have strong negative correlation of between -50% and -85% with Mean Time to Repair, Maintenance and Breakdown Repair Cost indicating performance degradation but increased maintenance and repair costs as shown by Trend Analysis. The 11-year preretrofit data has identified parameters to determine the QC retrofit prediction. The KPIs, obsolescence status, maintenance and breakdown repair cost can be used as the inputs to the future QC Retrofit Prediction Model development.

Machine learning-based prediction of construction and demolition waste generation in developing countries: a case study.

Data is needed for making informed decisions regarding managing waste in the time of construction and demolition phases of buildings. However, data availability is very limited in most developing countries in the area of waste generation. The objective of this study is to employ an artificial intelligence (AI)-based approach to develop a reliable model for forecasting monthly construction and demolition waste (C&DW) generation in the case study of Tehran, Iran. We have trained different prediction models using various AI algorithms, including multilayer perceptron neural network, radial basis function neural network, support vector machines, and adaptive neuro-fuzzy inference system (ANFIS). According to the findings, all employed AI algorithms demonstrated high prediction performance for C&DW forecasting models. The ANFIS model, with R2 = 0.96 and RMSE = 0.04209, was identified as the model that better represented the observed values of C&DW generation. The better efficiency of the ANFIS model could be due to its effective enhancement of neural networks to model subjective variables based on fuzzy logic capabilities. The developed prediction model can be employed as an efficient tool for policy and decision-making for C&DW management by predicting waste quantities in the future.

Predict progression free survival and overall survival using objective response rate for anti—PD1/PDL1 therapy development

In oncology anti—PD1 / PDL1 therapy development for solid tumors, objective response rate (ORR) is commonly used clinical endpoint for early phase study decision making, while progression free survival (PFS) and overall survival (OS) are widely used for late phase study decision making. Developing predictive models to late phase outcomes such as median PFS (mPFS) and median OS (mOS) based on early phase clinical outcome ORR could inform late phase study design optimization and probability of success (POS) evaluation. In existing literature, there are ORR / mPFS / mOS association and surrogacy investigations with limited number of included clinical trials. In this paper, without establishing surrogacy, we attempt to predict late phase survival (mPFS and mOS) based on early efficacy ORR and optimize late phase trial design for anti—PD1 / PDL1 therapy development. In order to include adequate number of eligible clinical trials, we built a comprehensive quantitative clinical trial landscape database (QLD) by combining information from different sources such as clinicaltrial.gov, publications, company press releases for relevant indications and therapies. We developed a generalizable algorithm to systematically extract structured data for scientific accuracy and completeness. Finally, more than 150 late phase clinical trials were identified for ORR / mPFS (ORR / mOS) predictive model development while existing literature included at most 50 trials. A tree-based machine learning regression model has been derived to account for ORR / mPFS (ORR / mOS) relationship heterogeneity across tumor type, stage, line of therapy, treatment class and borrow strength simultaneously when homogeneity persists. The proposed method ensures that the predictive model is robust and have explicit structure for clinical interpretation. Through cross validation, the average predictive mean square error of the proposed model is competitive to random forest and extreme gradient boosting methods and outperforms commonly used additive or interaction linear regression models. An example application of the proposed ORR / mPFS (ORR / mOS) predictive model on late phase trial POS evaluation for anti—PD1 / PDL1 combination therapy was illustrated.

Development of Accident Prediction Model under Mixed Traffic Conditions: A Case Study

Development of Accident Prediction Model under Mixed Traffic Conditions: A Case Study

Deep learning model based on contrast-enhanced ultrasound for predicting vessels encapsulating tumor clusters in hepatocellular carcinoma.

To establish and validate a non-invasive deep learning (DL) model based on contrast-enhanced ultrasound (CEUS) to predict vessels encapsulating tumor clusters (VETC) patterns in hepatocellular carcinoma (HCC). This retrospective study included consecutive HCC patients with preoperative CEUS images and available tissue specimens. Patients were randomly allocated into the training and test cohorts. CEUS images were analyzed using the ResNet-18 convolutional neural network for the development and validation of the VETC predictive model. The predictive value for postoperative early recurrence (ER) of the proposed model was further evaluated. A total of 242 patients were enrolled finally, including 195 in the training cohort (54.6 ± 11.2 years, 178 males) and 47 in the test cohort (55.1 ± 10.6 years, 40 males). The DL model (DL signature) achieved favorable performance in both the training cohort (area under the receiver operating characteristics curve [AUC]: 0.92, 95% confidence interval [CI]: 0.88-0.96) and test cohort (AUC: 0.90, 95% CI: 0.82-0.99). The stratified analysis demonstrated good discrimination of DL signature regardless of tumor size. Moreover, the DL signature was found independently correlated with postoperative ER (hazard ratio [HR]: 1.99, 95% CI: 1.29-3.06, p = 0.002). C-indexes of 0.70 and 0.73 were achieved when the DL signature was used to predict ER independently and combined with clinical features. The proposed DL signature provides a non-invasive and practical method for VETC-HCC prediction, and contributes to the identification of patients with high risk of postoperative ER. This DL model based on contrast-enhanced US displayed an important role in non-invasive diagnosis and prognostication for patients with VETC-HCC, which was helpful in individualized management. Preoperative biopsy to determine VETC status in HCC patients is limited. The contrast-enhanced DL model provides a non-invasive tool for the prediction of VETC-HCC. The proposed deep-learning signature assisted in identifying patients with a high risk of postoperative ER.

Transport characteristics of heavy metals in the soil-atmosphere-wheat system in farming areas and development of multiple linear regression predictive model

Heavy metal accumulation in agricultural products has become a major concern. Previous studies have focused on the transport of heavy metals from the soil and their accumulation in crops. However, recent studies revealed that wheat leaves, ears, and awns can also transport and accumulate heavy metals. Wheat grains can be influenced by two sources of heavy metals: soil contamination and atmospheric deposition. To comprehend the transport characteristics of heavy metals in soil, atmospheric deposition, and wheat, 37 samples each for wheat rhizosphere soil, wheat roots, stems, leaves, and grains were collected. Fifteen samples of atmospheric dry deposition and atmospheric wet deposition were collected from Linshu County (northern area), China. Based on the test data, the characteristics of heavy metals and their distribution in the study area were analyzed. Migration patterns of heavy metals in crops from different sources were investigated using Pearson correlation and redundancy analysis. Finally, a predictive model for heavy metals in wheat grains was developed using multiple linear regression analysis. Significant disparities in the distribution of heavy metals existed among wheat roots, stems, leaves, and grains. The coefficient of variation of heavy metals in atmospheric deposition was relatively high, indicating discernible spatial patterns influenced by human activities. Notably, a positive correlation was observed between the concentration of heavy metals in wheat grains and atmospheric deposition of Hg, Cd, and Pb. Conversely, Zn and Ni levels in wheat grains were significantly negatively associated with soil Zn, Ni, pH, and OM content. The contribution of heavy metal elements from different sources varied in their impact on the grain's heavy metal content. Specifically, atmospheric deposition was the primary source of Hg and Pb in wheat grains, while Cd, Ni, Cu, and Zn were predominantly derived from soil. Using a multiple linear regression model, we could accurately predict Hg, Pb, Cd, Ni, Zn, and As concentrations in crop grains. This model can facilitate quantitative evaluation of ecological risk of heavy metals accumulation in crops in the study area.

Development and Validation of an Interpretable Machine Learning Model for Early Prognosis Prediction in ICU Patients with Malignant Tumors and Hyperkalemia.

This study aims to develop and validate a machine learning (ML) predictive model for assessing mortality in patients with malignant tumors and hyperkalemia (MTH). We extracted data on patients with MTH from the Medical Information Mart for Intensive Care-IV, version 2.2 (MIMIC-IV v2.2) database. The dataset was split into a training set (75%) and a validation set (25%). We used the Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify potential predictors, which included clinical laboratory indicators and vital signs. Pearson correlation analysis tested the correlation between predictors. In-hospital death was the prediction target. The Area Under the Curve (AUC) and accuracy of the training and validation sets of 7 ML algorithms were compared, and the optimal 1 was selected to develop the model. The calibration curve was used to evaluate the prediction accuracy of the model further. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) enhanced model interpretability. 496 patients with MTH in the Intensive Care Unit (ICU) were included. After screening, 17 clinical features were included in the construction of the ML model, and the Pearson correlation coefficient was <0.8, indicating that the correlation between the clinical features was small. eXtreme Gradient Boosting (XGBoost) outperformed other algorithms, achieving perfect scores in the training set (accuracy: 1.000, AUC: 1.000) and high scores in the validation set (accuracy: 0.734, AUC: 0.733). The calibration curves indicated good predictive calibration of the model. SHAP analysis identified the top 8 predictive factors: urine output, mean heart rate, maximum urea nitrogen, minimum oxygen saturation, minimum mean blood pressure, maximum total bilirubin, mean respiratory rate, and minimum pH. In addition, SHAP and LIME performed in-depth individual case analyses. This study demonstrates the effectiveness of ML methods in predicting mortality risk in ICU patients with MTH. It highlights the importance of predictors like urine output and mean heart rate. SHAP and LIME significantly enhanced the model's interpretability.

Developing a Research Network of Early Warning Systems for Infectious Diseases Transmission Between China and Australia.

This article offers a thorough review of current early warning systems (EWS) and advocates for establishing a unified research network for EWS in infectious diseases between China and Australia. We propose that future research should focus on improving infectious disease surveillance by integrating data from both countries to enhance predictive models and intervention strategies. The article highlights the need for standardized data formats and terminologies, improved surveillance capabilities, and the development of robust spatiotemporal predictive models. It concludes by examining the potential benefits and challenges of this collaborative approach and its implications for global infectious disease surveillance. This is particularly relevant to the ongoing project, early warning systems for Infectious Diseases between China and Australia (NetEWAC), which aims to use seasonal influenza as a case study to analyze influenza trends, peak activities, and potential inter-hemispheric transmission patterns. The project seeks to integrate data from both hemispheres to improve outbreak predictions and develop a spatiotemporal predictive modeling system for seasonal influenza transmission based on socio-environmental factors.

Development and interpretation of a multimodal predictive model for prognosis of gastrointestinal stromal tumor

Gastrointestinal stromal tumor (GIST) is the most common mesenchymal original tumor in gastrointestinal (GI) tract and is considered to have varying malignant potential. With the advancement of computer science, radiomics technology and deep learning had been applied in medical researches. It’s vital to construct a more accurate and reliable multimodal predictive model for recurrence-free survival (RFS) aiding for clinical decision-making. A total of 254 patients underwent surgery and pathologically diagnosed with GIST in The First Hospital of China Medical University from 2019 to 2022 were included in the study. Preoperative contrast enhanced computerized tomography (CE-CT) and hematoxylin/eosin (H&E) stained whole slide images (WSI) were acquired for analysis. In the present study, we constructed a sum of 11 models while the multimodal model (average C-index of 0.917 on validation set in 10-fold cross validation) performed the best on external validation cohort with an average C-index of 0.864. The multimodal model also reached statistical significance when validated in the external validation cohort (n = 42) with a p-value of 0.0088 which pertained to the recurrence-free survival (RFS) comparison between the high and low groups using the optimal threshold on the predictive score. We also explored the biological significance of radiomics and pathomics features by visualization and quantitative analysis. In the present study, we constructed a multimodal model predicting RFS of GIST which was prior over unimodal models. We also proposed hypothesis on the correlation between morphology of tumor cell and prognosis.

Multi-modality risk prediction of cardiovascular diseases for breast cancer cohort in the All of Us Research Program.

This study leverages the rich diversity of the All of Us Research Program (All of Us)'s dataset to devise a predictive model for cardiovascular disease (CVD) in breast cancer (BC) survivors. Central to this endeavor is the creation of a robust data integration pipeline that synthesizes electronic health records (EHRs), patient surveys, and genomic data, while upholding fairness across demographic variables. We have developed a universal data wrangling pipeline to process and merge heterogeneous data sources of the All of Us dataset, address missingness and variance in data, and align disparate data modalities into a coherent framework for analysis. Utilizing a composite feature set including EHR, lifestyle, and social determinants of health (SDoH) data, we then employed Adaptive Lasso and Random Forest regression models to predict 6 CVD outcomes. The models were evaluated using the c-index and time-dependent Area Under the Receiver Operating Characteristic Curve over a 10-year period. The Adaptive Lasso model showed consistent performance across most CVD outcomes, while the Random Forest model excelled particularly in predicting outcomes like transient ischemic attack when incorporating the full multi-model feature set. Feature importance analysis revealed age and previous coronary events as dominant predictors across CVD outcomes, with SDoH clustering labels highlighting the nuanced impact of social factors. The development of both Cox-based predictive model and Random Forest Regression model represents the extensive application of the All of Us, in integrating EHR and patient surveys to enhance precision medicine. And the inclusion of SDoH clustering labels revealed the significant impact of sociobehavioral factors on patient outcomes, emphasizing the importance of comprehensive health determinants in predictive models. Despite these advancements, limitations include the exclusion of genetic data, broad categorization of CVD conditions, and the need for fairness analyses to ensure equitable model performance across diverse populations. Future work should refine clinical and social variable measurements, incorporate advanced imputation techniques, and explore additional predictive algorithms to enhance model precision and fairness. This study demonstrates the liability of the All of Us's diverse dataset in developing a multi-modality predictive model for CVD in BC survivors risk stratification in oncological survivorship. The data integration pipeline and subsequent predictive models establish a methodological foundation for future research into personalized healthcare.

Development and validation of a diagnostic prediction model for children with pertussis

To develop and validate a diagnostic prediction model based on blood parameters for predicting the pertussis in children. A retrospective study of 477 children with suspected pertussis at Zigong First People’s Hospital was performed between January 2020 and December 2021. The patients were randomly divided into training cohort and validation cohort. Stepwise regression and R software was performed to develop and validate the model. Stepwise regression analysis showed that white blood cell (WBC), hematocrit (HCT), lymphocyte (LYMPH), C-reactive protein (CRP) and platelet distribution width to mean platelet volume ratio (PDW-MPV-R) were found to be independent factors associated with pertussis. The model containing WBC, CRP and PDW-MPV-R had the best performance. The area under curve (ROC, 0.77 for the training cohort and 0.80 for the validation cohort) of the model indicated satisfactory discriminative ability. The sensitivity and specificity of the model were 72.1% and 72.6% in training cohort and 74% and 72.1%, respectively, in validation cohort. Based on the ROC analysis, calibration plots, and decision curve analysis, we concluded that the model exhibited excellent performance. A model based on blood parameters is sufficiently accurate to predict the probability of pertussis in children, and may provide some reference for clinical decisions.

Artificial intelligence and machine learning for anaphylaxis algorithms.

Anaphylaxis is a severe, potentially life-threatening allergic reaction that requires rapid identification and intervention. Current management includes early recognition, prompt administration of epinephrine, and immediate medical attention. However, challenges remain in accurate diagnosis, timely treatment, and personalized care. This article reviews the integration of artificial intelligence and machine learning in enhancing anaphylaxis management. Artificial intelligence and machine learning can analyze vast datasets to identify patterns and predict anaphylactic episodes, improve diagnostic accuracy through image and biomarker analysis, and personalize treatment plans. Artificial intelligence-powered wearable devices and decision support systems can facilitate real-time monitoring and early intervention. The ethical considerations of artificial intelligence use, including data privacy, transparency, and bias mitigation, are also discussed. Future directions include the development of predictive models, enhanced diagnostic tools, and artificial intelligence-driven educational resources. By leveraging artificial intelligence and machine learning, healthcare providers can improve the management of anaphylaxis, ensuring better patient outcomes and advancing personalized medicine.

Development and validation of a risk scoring tool for predicting incident reversible cognitive frailty among community-dwelling older adults: A prospective cohort study.

Reversible cognitive frailty (RCF) is an ideal target to prevent asymptomatic cognitive impairment and dependency. This study aimed to develop and validate prediction models for incident RCF. A total of 1230 older adults aged ≥60 years from China Health and Retirement Longitudinal Study 2011-2013 survey were included as the training set. The modified Poisson regression and three machine learning algorithms including eXtreme Gradient Boosting, support vector machine and random forest were used to develop prediction models. All models were evaluated internally with fivefold cross-validation, and evaluated externally using a temporal validation method through the China Health and Retirement Longitudinal Study 2013-2015 survey. The incidence of RCF was 27.4% in the training set and 27.5% in the external validation set. A total of 13 important predictors were selected to develop the model, including age, education, contact with their children, medical insurance, vision impairment, heart diseases, medication types, self-rated health, pain locations, loneliness, self-medication, night-time sleep and having running water. All models showed acceptable or approximately acceptable discrimination (AUC 0.683-0.809) for the training set, but fair discrimination (AUC 0.568-0.666) for the internal and external validation. For calibration, only modified Poisson regression and eXtreme Gradient Boosting were acceptable in the training set. All models had acceptable overall prediction performance and clinical usefulness. Older adults were divided into three groups by the risk scoring tool constructed based on modified Poisson regression: low risk (≤24), median risk (24-29) and high risk (>29). This risk tool could assist healthcare providers to predict incident RCF among older adults in the next 2 years, facilitating early identification of a high-risk population of RCF. Geriatr Gerontol Int 2024; ••: ••-••.

Development and External Validation of a Machine Learning–based Fall Prediction Model for Nursing Home Residents: A Prospective Cohort Study

ObjectivesTo develop and externally validate a machine learning–based fall prediction model for ambulatory nursing home residents. The focus is on predicting fall occurrences within 6 months after baseline assessment through a binary classification task, aiming to provide staff with an effective and user-friendly fall-risk assessment tool. DesignProspective cohort study. Setting and ParticipantsA total of 864 older residents living in 4 nursing homes between May 2022 and March 2023 in China. MethodsPotential fall-risk predictors were collected through in-person interviews and assessments of anthropometric and physical function. Participants were followed for 6 months, with falls recorded by trained nurses. Seven machine learning algorithms, including Logistic Regression (LR), Gradient Boosting Machine (GBM), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Neural Networks (NN), and Decision Tree (DT), were used to develop prediction models. Performance was evaluated using the area under the receiver operating characteristic curve (ROC-AUC) and Precision-Recall curve (PR-AUC), with calibration assessed via a calibration curve. Feature importance was visualized using SHapley Additive exPlanations (SHAP). ResultsThe 6 selected predictors were balance, grip strength, fatigue, fall history, age, and comorbidity. The ROC-AUC for the models ranged from 0.710 to 0.750, PR-AUC from 0.415 to 0.473, sensitivity from 0.704 to 0.914, and specificity from 0.511 to 0.687 in the validation cohort. The LR model was converted into a nomogram. Conclusions and ImplicationsThe machine learning–based fall-prediction models effectively identified nursing home residents at high risk of falls. The developed nomogram can be integrated into clinical practice to enhance fall risk assessment protocols, ultimately improving patient safety and care in nursing homes.

Development of a cross-latitude applicable predictive model for solar radiation acquisition potential of urban residential blocks

Existing research has revealed that predictive models can be developed based on urban morphology indicators to assess the potential of solar radiation acquisition (SRA) on building surfaces and provide much faster results compared to simulation software. However, such existing models can only be utilized in specific cities or regions, which restricts their popularity. To fill this scientific gap, this study develops a cross-latitude predictive model for assessing the SRA of buildings. Building solar radiation acquisition capacity, selected key morphology indicators, and cosine of latitude (cosφ) are employed as dependent and independent variables in the model to discuss the interaction between the urban form and the SRA based on the analysis of sixty sample urban residential blocks across nine cities in China with varying latitudes. The obtained results are indicative of relatively high prediction accuracy for the solar radiation acquisition capacity of buildings (with an adjusted R2 of 0.931 for roofs, and 0.962 for exterior walls). As a result, the proposed model has the potential to serve as a valuable tool for incorporating solar radiation considerations into sustainable urban design practices.