Development Of Predictive Models Research Articles

A Predictive Model for Graft Failure in Femtosecond Laser-Assisted Penetrating Keratoplasty Among Chinese Patients: A 2-Year Study.

Graft failure is a major challenge in femtosecond laser-assisted penetrating keratoplasty (Fs-PKP). This study focuses on the development and validation of a clinical predictive model aimed at identifying the risk of graft failure in individuals undergoing Fs-PKP in China, offering a tailored approach to improve surgical outcomes. This retrospective cohort study at Nanjing First Hospital involved 238 patients and followed the TRIPOD statement. The cohort was divided into a training set (n = 166) and a validation set (n = 72) in a 7:3 ratio. It analyzed 23 predictor variables related to recipient, donor, and surgical factors, defining graft failure as "visually significant and irreversible corneal stromal edema, haze, or scarring." A comprehensive nomogram was created using univariate and multivariate Cox regression analyses and assessed by concordance index (C-index), time-dependent receiver operating characteristics (ROC) curve, calibration plots, and decision curve analysis (DCA). Five critical risk factors were identified: recipients' history of systemic autoimmune disorders, ocular trauma, prior penetrating keratoplasty (PKP) history, donors' diabetes history, and the endothelial cell density of the donor cornea. The nomogram showed a C-index of 0.72 (95% CI 0.65-0.79) in the training group and 0.66 (95% CI 0.55-0.76) in the validation group, indicating robust predictive accuracy. Time-dependent ROC curves, calibration plots, and DCA consistently validated the model's reliability, predictive power, and clinical utility across both training and validation cohorts. Our study developed and validated a model incorporating five key factors, enhancing preoperative prediction and management for Chinese patients with Fs-PKP graft failure.

An Appraisal of the Quality of Development and Reporting of Predictive Models in Neurosurgery: A Systematic Review

An Appraisal of the Quality of Development and Reporting of Predictive Models in Neurosurgery: A Systematic Review

Novel decision tree models predict the overall survival of patients with submandibular gland cancer.

While the accurate prediction of the overall survival (OS) in patients with submandibular gland cancer (SGC) is paramount for informed therapeutic planning, the development of reliable survival prediction models has been hindered by the rarity of SGC cases. The purpose of this study is to identify key prognostic factors for OS in SGC patients using a large database and construct decision tree models to aid the prediction of survival probabilities in 12, 24, 60 and 120 months. We performed a retrospective cohort study using the Surveillance, Epidemiology and End Result (SEER) program. Demographic and peri-operative predictor variables were identified. The outcome variables overall survival at 12-, 24-, 60, and 120 months. The C5.0 algorithm was utilized to establish the dichotomous decision tree models, with the depth of tree limited within 4 layers. To evaluate the performances of the novel models, the receiver operator characteristic (ROC) curves were generated, and the metrics such as accuracy rate, and area under ROC curve (AUC) were calculated. A total of 1,705, 1,666, 1,543, and 1,413 SGC patients with a follow up of 12, 24, 60 and 120 months and exact survival status were identified from the SEER database. Predictor variables of age, sex, surgery, radiation, chemotherapy, tumor histology, summary stage, metastasis to distant lymph node, and marital status exerted substantial influence on overall survival. Decision tree models were then developed, incorporating these vital prognostic indicators. Favorable consistency was presented between the predicted and actual survival statuses. For the training dataset, the accuracy rates for the 12-, 24-, 60- and 120-month survival models were 0.866, 0.767, 0.737 and 0.797. Correspondingly, the AUC values were 0.841, 0.756, 0.725, and 0.774 for the same time points. Based on the most important predictor variables identified using the large, SEER database, decision tree models were established that predict OS of SGC patients. The models offer a more exhaustive evaluation of mortality risk and may lead to more personalized treatment strategies.

Development and evaluation of machine learning models for predicting large-for-gestational-age newborns in women exposed to radiation prior to pregnancy

IntroductionThe correlation between radiation exposure before pregnancy and abnormal birth weight has been previously proven. However, for large-for-gestational-age (LGA) babies in women exposed to radiation before becoming pregnant, there is no prediction model yet.Material and methodsThe data were collected from the National Free Preconception Health Examination Project in China. A sum of 455 neonates (42 SGA births and 423 non-LGA births) were included. A training set (n = 319) and a test set (n = 136) were created from the dataset at random. To develop prediction models for LGA neonates, conventional logistic regression (LR) method and six machine learning methods were used in this study. Recursive feature elimination approach was performed by choosing 10 features which made a big contribution to the prediction models. And the Shapley Additive Explanation model was applied to interpret the most important characteristics that affected forecast outputs.ResultsThe random forest (RF) model had the highest average area under the receiver-operating-characteristic curve (AUC) for predicting LGA in the test set (0.843, 95% confidence interval [CI]: 0.714–0.974). Except for the logistic regression model (AUC: 0.603, 95%CI: 0.440–0.767), other models’ AUCs displayed well. Thereinto, the RF algorithm’s final prediction model using 10 characteristics achieved an average AUC of 0.821 (95% CI: 0.693–0.949).ConclusionThe prediction model based on machine learning might be a promising tool for the prenatal prediction of LGA births in women with radiation exposure before pregnancy.

Predicting severity of acute appendicitis with machine learning methods: a simple and promising approach for clinicians

BackgroundsAcute Appendicitis (AA) is one of the most common surgical emergencies worldwide. This study aims to investigate the predictive performances of 6 different Machine Learning (ML) algorithms for simple and complicated AA.MethodsData regarding operated AA patients between 2012 and 2022 were analyzed retrospectively. Based on operative findings, patients were evaluated under two groups: perforated AA and none-perforated AA. The features that showed statistical significance (p < 0.05) in both univariate and multivariate analysis were included in the prediction models as input features. Five different error metrics and the area under the receiver operating characteristic curve (AUC) were used for model comparison.ResultsA total number of 1132 patients were included in the study. Patients were divided into training (932 samples), testing (100 samples), and validation (100 samples) sets. Age, gender, neutrophil count, lymphocyte count, Neutrophil to Lymphocyte ratio, total bilirubin, C-Reactive Protein (CRP), Appendix Diameter, and PeriAppendicular Liquid Collection (PALC) were significantly different between the two groups. In the multivariate analysis, age, CRP, and PALC continued to show a significant difference in the perforated AA group. According to univariate and multivariate analysis, two data sets were used in the prediction model. K-Nearest Neighbors and Logistic Regression algorithms achieved the best prediction performance in the validation group with an accuracy of 96%.ConclusionThe results showed that using only three input features (age, CRP, and PALC), the severity of AA can be predicted with high accuracy. The developed prediction model can be useful in clinical practice.

Optimized landslide susceptibility prediction based on SBAS-InSAR: case study of the Jiuzhaigou Ms7.0 earthquake

Earthquake-induced landslides can cause severe surface damage and casualties, posing a serious threat to the overall ecological environment and social stability. Traditional landslide susceptibility prediction (LSP) techniques often suffer from low effectiveness and precision, necessitating the exploration of remote sensing technology. However, this research in this area is limited, and the development of high-performance prediction models remains a pressing scientific issue. This study focuses on the Ms7.0 earthquake in Jiuzhaigou on 8 August 2017. To investigate the optimal integration of remote sensing technology with traditional LSP techniques, the study applies collaborative factor analysis and contingency matrix methods to create four new coupling models (SVM-I, SVM-II, RF-I, RF-II), followed by a comprehensive performance evaluation of these models. The results indicate that the integration of SAR-derived surface deformation data significantly enhances the accuracy of Landslide Susceptibility Mapping (LSM). Comparing the model performance with the receiver operating characteristic curve and landslide density, the reliability and prediction performance of the RF-I model are outstanding, reflecting that the improved method based on the InSAR collaborative machine learning model with shape variables along the slope direction can optimize the accuracy of the LSM, and has better performance and robustness in earthquake landslide susceptibility evaluation.

Development and validation of a risk prediction model for invasiveness of pure ground-glass nodules based on a systematic review and meta-analysis

BackgroundAssessing the aggressiveness of pure ground glass nodules early on significantly aids in making informed clinical decisions.ObjectiveDeveloping a predictive model to assess the aggressiveness of pure ground glass nodules in lung adenocarcinoma is the study’s goal.MethodsA comprehensive search for studies on the relationship between computed tomography(CT) characteristics and the aggressiveness of pure ground glass nodules was conducted using databases such as PubMed, Embase, Web of Science, Cochrane Library, Scopus, Wanfang, CNKI, VIP, and CBM, up to December 20, 2023. Two independent researchers were responsible for screening literature, extracting data, and assessing the quality of the studies. Meta-analysis was performed using Stata 16.0, with the training data derived from this analysis. To identify publication bias, Funnel plots and Egger tests and Begg test were employed. This meta-analysis facilitated the creation of a risk prediction model for invasive adenocarcinoma in pure ground glass nodules. Data on clinical presentation and CT imaging features of patients treated surgically for these nodules at the Third Affiliated Hospital of Kunming Medical University, from September 2020 to September 2023, were compiled and scrutinized using specific inclusion and exclusion criteria. The model’s effectiveness for predicting invasive adenocarcinoma risk in pure ground glass nodules was validated using ROC curves, calibration curves, and decision analysis curves.ResultsIn this analysis, 17 studies were incorporated. Key variables included in the model were the largest diameter of the lesion, average CT value, presence of pleural traction, and spiculation. The derived formula from the meta-analysis was: 1.16×the largest lesion diameter + 0.01 × the average CT value + 0.66 × pleural traction + 0.44 × spiculation. This model underwent validation using an external set of 512 pure ground glass nodules, demonstrating good diagnostic performance with an ROC curve area of 0.880 (95% CI: 0.852–0.909). The calibration curve indicated accurate predictions, and the decision analysis curve suggested high clinical applicability of the model.ConclusionWe established a predictive model for determining the invasiveness of pure ground-glass nodules, incorporating four key radiological indicators. This model is both straightforward and effective for identifying patients with a high likelihood of invasive adenocarcinoma.

A Data-Driven Approach to Predicting Recreational Activity Participation Using Machine Learning

ABSTRACT Purpose: With the popularity of recreational activities, the study aimed to develop prediction models for recreational activity participation and explore the key factors affecting participation in recreational activities. Methods: A total of 12,712 participants, excluding individuals under 20, were selected from the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2018. The mean age of the sample was 46.86 years (±16.97), with a gender distribution of 6,721 males and 5,991 females. The variables included demographic, physical-related variables, and lifestyle variables. This study developed 42 prediction models using six machine learning methods, including logistic regression, Support Vector Machine (SVM), decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). The relative importance of each variable was evaluated by permutation feature importance. Results: The results illustrated that the LightGBM was the most effective algorithm for predicting recreational activity participation (accuracy: .838, precision: .783, recall: .967, F1-score: .865, AUC: .826). In particular, prediction performance increased when the demographic and lifestyle datasets were used together. Next, as the result of the permutation feature importance based on the top models, education level and moderate-vigorous physical activity (MVPA) were found to be essential variables. Conclusion: These findings demonstrated the potential of a data-driven approach utilizing machine learning in a recreational discipline. Furthermore, this study interpreted the prediction model through feature importance analysis to overcome the limitation of machine learning interpretability.

Multivariate Hydrological Modeling Based on Long Short-Term Memory Networks for Water Level Forecasting

In the Department of Chocó, flooding poses a recurrent and significant challenge due to heavy rainfall and the dense network of rivers characterizing the region. However, the lack of adequate infrastructure to prevent and predict floods exacerbates this situation. The absence of early warning systems, the scarcity of meteorological and hydrological monitoring stations, and deficiencies in urban planning contribute to the vulnerability of communities to these phenomena. It is imperative to invest in flood prediction and prevention infrastructure, including advanced monitoring systems, the development of hydrological prediction models, and the construction of hydraulic infrastructure, to reduce risk and protect vulnerable communities in Chocó. Additionally, raising public awareness of the associated risks and encouraging the adoption of mitigation and preparedness measures throughout the population are essential. This study introduces a novel approach for the multivariate prediction of hydrological variables, specifically focusing on water level forecasts for two hydrological stations along the Atrato River in Colombia. The model, utilizing a specialized type of recurrent neural network (RNN) called the long short-term memory (LSTM) network, integrates data from hydrological variables, such as the flow, precipitation, and level. With a model architecture featuring four inputs and two outputs, where flow and precipitation serve as inputs and the level serves as the output for each station, the LSTM model is adept at capturing the complex dynamics and cross-correlations among these variables. Validation involves comparing the LSTM model’s performance with linear and nonlinear Autoregressive with Exogenous Input (NARX) models, considering factors such as the estimation error and computational time. Furthermore, this study explores different scenarios for water level prediction, aiming to utilize the proposed approach as an effective flood early warning system.

Descriptors-based machine-learning prediction of cetane number using quantitative structure–property relationship

The physicochemical properties of liquid alternative fuels are important but difficult to measure/predict, especially when complex surrogate fuels are concerned. In the present work, machine learning is used to develop quantitative structure–property relationship models. The fuel chemical structure is represented by molecular descriptors, allowing the linking of important features of the fuel composition and key properties of fuel utilization. Feature selection is employed to select the most relevant features that describe the chemical structure of the fuel and several machine learning algorithms are tested to construct interpretable models. The effectiveness of the methodology is demonstrated through the development of accurate and interpretable predictive models for cetane numbers, with a focus on understanding the link between molecular structure and fuel properties. In this context, matrix-based descriptors and descriptors related to the number of atoms in the molecule are directly linked with the cetane number of hydrocarbons. Furthermore, the results showed that molecular connectivity indices play a role in the cetane number for aromatic molecules. Also, the methodology is extended to predict the cetane number of ester and ether molecules, leveraging the design of alternative fuels towards fully sustainable fuel utilization.

Development and validation of a LASSO prediction model for cisplatin induced nephrotoxicity: a case-control study in China

BackgroundEarly identification of high-risk individuals with cisplatin-induced nephrotoxicity (CIN) is crucial for avoiding CIN and improving prognosis. In this study, we developed and validated a CIN prediction model based on general clinical data, laboratory indications, and genetic features of lung cancer patients before chemotherapy.MethodsWe retrospectively included 696 lung cancer patients using platinum chemotherapy regimens from June 2019 to June 2021 as the traing set to construct a predictive model using Absolute shrinkage and selection operator (LASSO) regression, cross validation, and Akaike’s information criterion (AIC) to select important variables. We prospectively selected 283 independent lung cancer patients from July 2021 to December 2022 as the test set to evaluate the model’s performance.ResultsThe prediction model showed good discrimination and calibration, with AUCs of 0.9217 and 0.8288, sensitivity of 79.89% and 45.07%, specificity of 94.48% and 94.81%, in the training and test sets respectively. Clinical decision curve analysis suggested that the model has value for clinical use when the risk threshold ranges between 0.1 and 0.9. Precision-Recall (PR) curve shown in recall interval from 0.5 to 0.75: precision gradually declines with increasing Recall, up to 0.9.ConclusionsPredictive models based on laboratory and demographic variables can serve as a beneficial complementary tool for identifying high-risk populations with CIN.

Pancreatic and cardiometabolic complications of severe hypertriglyceridaemia.

This review endeavours to explore the aetiopathogenesis and impact of severe hypertriglyceridemia (SHTG) and chylomicronaemia on cardiovascular, and pancreatic complications and summarizes the novel pharmacological options for management. SHTG, although rare, presents significant diagnostic and therapeutic challenges. Familial chylomicronaemia syndrome (FCS), is the rare monogenic form of SHTG, associated with increased acute pancreatitis (AP) risk, whereas relatively common multifactorial chylomicronaemia syndrome (MCS) leans more towards cardiovascular complications. Despite the introduction and validation of the FCS Score, FCS continues to be underdiagnosed and diagnosis is often delayed. Longitudinal data on disease progression remains scant. SHTG-induced AP remains a life-threatening concern, with conservative treatment as the cornerstone while blood purification techniques offer limited additional benefit. Conventional lipid-lowering medications exhibit minimal efficacy, underscoring the growing interest in novel therapeutic avenues, that is, antisense oligonucleotides (ASO) and short interfering RNA (siRNA) targeting apolipoprotein C3 (ApoC3) and angiopoietin-like protein 3 and/or 8 (ANGPTL3/8). Despite advancements in understanding the genetic basis and pathogenesis of SHTG, diagnostic and therapeutic challenges persist. The rarity of FCS and the heterogenous phenotype of MCS underscore the need for the development of predictive models for complications and tailored personalized treatment strategies. The establishment of national and international registries is advocated to augment disease comprehension and identify high-risk individuals.

Satellite Tracking Reveals the Speed Up of the Lacustrine Algal Bloom Drift in Response to Climate Change.

Satellite evidence indicates a global increase in lacustrine algal blooms. These blooms can drift with winds, resulting in significant changes of the algal biomass spatial distribution, which is crucial in bloom formation. However, the lack of long-term, large-scale observational data has limited our understanding of bloom drift. Here, we have developed a novel method to track the drift using multi-source remote sensing satellites and presented a comprehensive bloom drift data set for four typical lakes: Lake Taihu (China, 2011-2021), Lake Chaohu (China, 2011-2020), Lake Dianchi (China, 2003-2021), and Lake Erie (North America, 2003-2021). We found that blooms closer to the water surface tend to drift faster. Higher temperatures and lower wind speeds bring blooms closer to the water surface, therefore accelerating drift and increasing biomass transportation. Under ongoing climate change, algal blooms are increasingly likely to spread over larger areas and accumulate in downwind waters, thereby posing a heightened risk to water resources. Our research greatly improves the understanding of algal bloom dynamics and provides new insights into the driving factors behind the global expansion of algal blooms. Our bloom-drift-tracking methodology also paves the way for the development of high-precision algal bloom prediction models.

Comparison of Bayesian approaches for developing prediction models in rare disease: application to the identification of patients with Maturity-Onset Diabetes of the Young

BackgroundClinical prediction models can help identify high-risk patients and facilitate timely interventions. However, developing such models for rare diseases presents challenges due to the scarcity of affected patients for developing and calibrating models. Methods that pool information from multiple sources can help with these challenges.MethodsWe compared three approaches for developing clinical prediction models for population screening based on an example of discriminating a rare form of diabetes (Maturity-Onset Diabetes of the Young - MODY) in insulin-treated patients from the more common Type 1 diabetes (T1D). Two datasets were used: a case-control dataset (278 T1D, 177 MODY) and a population-representative dataset (1418 patients, 96 MODY tested with biomarker testing, 7 MODY positive). To build a population-level prediction model, we compared three methods for recalibrating models developed in case-control data. These were prevalence adjustment (“offset”), shrinkage recalibration in the population-level dataset (“recalibration”), and a refitting of the model to the population-level dataset (“re-estimation”). We then developed a Bayesian hierarchical mixture model combining shrinkage recalibration with additional informative biomarker information only available in the population-representative dataset. We developed a method for dealing with missing biomarker and outcome information using prior information from the literature and other data sources to ensure the clinical validity of predictions for certain biomarker combinations.ResultsThe offset, re-estimation, and recalibration methods showed good calibration in the population-representative dataset. The offset and recalibration methods displayed the lowest predictive uncertainty due to borrowing information from the fitted case-control model. We demonstrate the potential of a mixture model for incorporating informative biomarkers, which significantly enhanced the model’s predictive accuracy, reduced uncertainty, and showed higher stability in all ranges of predictive outcome probabilities.ConclusionWe have compared several approaches that could be used to develop prediction models for rare diseases. Our findings highlight the recalibration mixture model as the optimal strategy if a population-level dataset is available. This approach offers the flexibility to incorporate additional predictors and informed prior probabilities, contributing to enhanced prediction accuracy for rare diseases. It also allows predictions without these additional tests, providing additional information on whether a patient should undergo further biomarker testing before genetic testing.

Fire4CAST – a new integrative epidemiological forecasting model for the accurate prediction of infection risk and effective control of fire blight in Pyrus orchards

AbstractFire blight disease, caused by Erwinia amylovora is present worldwide and affects over 40 countries in Europe where it is listed as a quarantine or regulated pest often due to ineffective control strategies maladapted to the respective production systems. In Portugal, the disease was confirmed in 2010 and the occurrence of disease outbreaks in new production areas has risen over the years. The disease affects the national production of apple and pear fruits, with greater impact on the national pear variety ‘Rocha’, widely exported to European countries and Brazil. The mild temperatures and high relative humidity promote the progression of the disease during winter, revealing the potential activity of the bacterium in the latency period (LP) of the host. Infection alert risk using the established predictive models Maryblight TM, Cougarblight and BIS98 was put in place in 2013 by Centro Operativo e Tecnológico Hortofrutícola Nacional (COTHN). These attempts to control the spread of this disease, showed low accuracy for the Portuguese epidemiological reality. Within the framework of project Fire4CAST we developed a new epidemiological model to predict fire blight disease using a systems biology approach integrating microbiological, cytological and genomic pathogen data with phenological host development and climatic variables. The presence of E. amylovora was monitored in orchards with fire blight history using standard laboratory tests. Simultaneously, the implementation of immune-flow cytometry (IFCM) highlighted the viability of E. amylovora populations prevailing during winter and early spring, long before bloom risk period. The integration of the whole data set allowed the development of the Fire4CAST predictive model, able to monitor the expected infection date (EID) and to define accurate outbreak alarms. Fire4CAST model enabled the detection of outbreak risk during winter based on rules that consider climatic data variables, which were validated by effective presence of live and active E. amylovora populations and real-time quantitative PCR (qPCR) data, accomplishing a precision rate of 83%. Field application of Fire4CAST can hopefully guide the implementation of successful control strategies, leading to more sustainable pome chain production areas.

Predictive models as a clinical decision support tool for genetic cancer risk assessment among Mexican patients at risk of hereditary colorectal cancer.

10617 Background: Hereditary cancer syndromes, including Lynch syndrome (LS) with an increased risk of colorectal cancer (CRC), have prompted the development of clinical criteria and predictive models (PM) to identify at-risk individuals. However, these criteria have been validated with limited representation from the Latino population. Several factors may influence CRC penetrance, potentially leading to variations in PM performance across populations. Given that CRC ranks as the third most prevalent cancer worldwide, the increasing incidence of early-onset disease highlights the need for expanding Genetic Cancer Risk Assessment (GCRA) services. Methods: This prospective study included Mexican patients who were diagnosed with CRC and met NCCN criteria for GCRA. Participants were enrolled at two referral centers in Mexico, the Instituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran, Mexico City and at the Hospital Zambrano Hellion TecSalud, Nuevo Leon. Participants' carrier risk was estimated using the PREMM5 model. Amsterdam II and revised Bethesda criteria, immunohistochemistry (IHC) for DNA mismatch repair (MMR) proteins in tumor tissue and multigene panel testing results were reviewed. For this analysis, only pathogenic and likely pathogenic (P/LP) variants in probands were reported. Results: From April 2016 to October 2023, 194 patients were included, with a median age at diagnosis of 45.0 (range 17-82) years, and 49.5% were women. P/LP variants were identified in 37.6% (n=73). 27.5% (n=50) were carriers of P/LP variants associated with LS (29 MLH1, 13 MSH2, 6 MSH6, and 2 PMS2). 11.8% (n=23) were carriers of P/LP variants in other genes (7 APC, 4 ATM, 3 CHEK2, 2 BRCA1, 2 TP53, 2 STK11, 1 PALB2, 1 BRIP1, and 1 MUTYH). Overall, carriers of PVs were diagnosed with CRC at a younger age than non-carriers (42 vs 46 years, p=0.034). The Amsterdam II criteria proved to be accurate (Sen 55%, Spe 91%, PPV 0.69, NPV 0.85), while the Bethesda criteria (Sen 98%, Spe 18%, PPV 0.29, NPV 0.96) and IHC for MMR proteins (Sen 94%, Spe 67%, PPV 0.61, NPV 0.95) were found to be highly sensitive. The area under the ROC curve for PREMM5 was 0.82 with a mean score 28.5 (2.1 – 50) in patients with LS and 6.0 (0.9 – 50) in non-carriers. Carriers of PVs had a higher rate of multiple primary malignancies than non-carriers (41.0% vs 19.0%, p=0.0014), and self-reported family history of CRC (65.7% vs 24.8%, p&lt;0.001). Conclusions: Our results showed the PREMM5 model and IHC for MMR proteins effectively prioritized patients eligible for germline genetic testing in resource-limited settings. The performance of PREMM5 in this Mexican cohort was similar to that reported in the validation study of this model in other populations. Considering the spectrum of identified P/LP variants, the multigene panel test should be used in the comprehensive evaluation of Mexican patients with CRC.

Optimizing High-Entropy Alloys using Deep Neural Networks

Exceptional mechanical properties of multi-principal element alloys have been typically achieved through manual trial-and-error approaches. The advances in artificial neural networks (ANN) have recently allowed for the development of predictive alloy design ANN models that take into account a large variety of prior experimental findings. However, when mechanical performance is considered, prior processing protocols (e.g. annealing, cold working) are almost impossible to track across different research groups and material classes. In this work, we propose to reverse the use of the yield stress point, setting it as an explicit input (instead of predictive output) parameter that is then used for the quantification of the material state, towards predicting material properties, such as the Ultimate Tensile Strength (UTS). This novel approach specifically addresses the challenge of predicting and improving ductility, a common issue in existing methods focused on yield stress, hardness, and phases. By combining available, validated databases of eleven elements and 734 total materials, we develop an ANN that receives as input the compositions out of 11 possible elements and the "observed" yield stress. Then, we utilize this model to perform predictions for 5-element concentrated alloys with exceptional UTS. We show that this ANN modeling strategy leads to exceptional confidence scores, and then, we use this ANN model for the design of HEAs, which we further discuss.

Enhancing oncology research through data sharing and re-use: A five-year review of Vivli's global data platform.

e13670 Background: The last decade has seen a paradigm shift in clinical research with the accessibility of trial data, facilitating secondary analyses and the development of novel predictive models. Vivli a non-profit entity established in 2018, epitomizes this movement with its global platform (vivli.org) dedicated to the facilitating access to and re-use of individual participant data from completed clinical trials for use by researchers worldwide. Methods: Commencing with approximately 2,500 trials from 15 members, Vivli has expanded over five years to encompass more than 7,000 trials involving 3.6 million participants across 50 members who contribute trial data from various sectors (industry, academic, government and non-profit foundation). Results: Oncology trials constitute 11% of the Vivli data pool of 7,000 trials, constituting 36% of Vivli’s research proposals due to the therapeutic area's high-impact potential. Researchers accessing data in the Vivli platform have produced a significant body of scientific work, with 74 oncology-focused publications emerging from their analyses. Notably, Roche's data has been a key resource, denoted by the 28% of all Vivli proposals requesting access to Roche data. Fifty-eight publications have resulted from secondary analysis of Roche oncology data. The motivation for secondary analyses in oncology have been predominantly for new research into treatment effectiveness or safety (39%). Other reasons include confirmation of existing research (15%), individual participant data (IPD) meta-analysis (14%), statistical method (10%), clinical trial design (5%), and summary-level meta-analysis (4%), training/testing (7%), other (5%). Conclusions: The Vivli platform has proven to be a key resource in recent years for the oncology community enabling the integration and publication of new insights. The sharing of IPD is in part motivated to ensure reproducibility of findings; our experience demonstrates that a minority of researchers are interested in reproducibility analysis but the majority of research is focused on new insight generation. The platform's success in facilitating the findability, accessibility and reuse of patient level data is signified by ever increasing numbers of scientific publications emerging from analyses on the Vivli platform and this underscores its role in advancing scientific discovery and development within the field of oncology.

AI-based approach to enable proactive identification of early lung cancer: A retrospective population health study and economic model.

10536 Background: Approximately 48,500 new cases of lung cancer are diagnosed each year in the UK with a five-year survival rate which lags behind other European countries. Most patients are diagnosed at stage III or IV disease, which has a significantly worse prognosis. The integration of real-world data and AI techniques presents a promising avenue for improvement, using novel solutions not achievable through traditional care models. This study explores proactive patient identification at scale, aligning with the "left shift" paradigm in population health. The approach uses innovative technologies and novel data sources to identify patients with early-stage disease, enhancing both clinical and economic outcomes. Importantly, these interventions should be feasible on a large scale without overburdening already stretched clinical services. Methods: A retrospective study of real-world data for an urban population with high socioeconomic disadvantage and health needs was undertaken. Two endpoints were determined: detection potential and health economic impact. For detection potential (DP), patients aged 40 and above who underwent chest X-rays (CXR) at a large academic medical centre between 2016-2022 were included. Natural Language Processing (NLP) analysed unstructured free-text data from Electronic Health Records, combined with ICD-10 codes, for predictive model (PM) development using Machine Learning (ML) classification techniques. The PM's performance was assessed using the area under the receiver operating characteristic curve (AUCROC). Health economic impact was evaluated through a theoretical extrapolation of DP. Assuming a 9% left shift from late (stage III or IV) to early (stage I or II) disease, a health economic model (HEM) compared the impact of the current care model to a potential future state facilitated by the digital medicine care model (DMCM). Results: 75,342 patients were included, with 755 lung cancer diagnoses occurring within 6 months of CXR. The PM achieved a peak AUROC of 0.75, exceeding the performance of currently used risk prediction models (Q Cancer Risk, Risk Assessment Tools). A 9% left shift equated to 4.5 years mean increase in survival. The DMCM was associated with a net healthcare cost benefit of £6k per patient diagnosed with lung cancer, improved quality-adjusted life years of £21M for the year 1 cohort over 10 years and increased economic productivity (from additional years in workforce) of £2.4M. Conclusions: The study demonstrates the practical utility of AI-supported early lung cancer detection at population scale. Real-world evidence is needed to prospectively validate the model as cost-effective, scalable, and efficient. Future work will seek to improve the model's performance against a broader at-risk patient population and explore its applicability to other late-presenting tumours.

Development of Prediction Model for 1-year Mortality after Hip Fracture Surgery.

Hip fractures are associated with increased mortality. The identification of risk factors of mortality could improve patient care. The aim of the study was to identify risk factors of mortality after surgery for a hip fracture and construct a mortality model. A cohort study was conducted on patients with hip fractures at two institutions. Five hundred and ninety-seven patients with hip fractures that were treated in the tertiary hospital, and another 147 patients that were treated in a secondary hospital. The perioperative data were collected from medical charts and interviews. Functional Assessment Measure score, Short Form-12 and mortality were recorded at 12 months. Patients and surgery variables that were associated with increased mortality were used to develop a mortality model. Mortality for the whole cohort was 19.4% at one year. From the variables tested only age >80 years, American Society of Anesthesiologists category, time to surgery (>48 hours), Charlson comorbidity index, sex, use of anti-coagulants, and body mass index <25 kg/m2 were associated with increased mortality and used to construct the mortality model. The area under the curve for the prediction model was 0.814. Functional outcome at one year was similar to preoperative status, even though their level of physical function dropped after the hip surgery and slowly recovered. The mortality prediction model that was developed in this study calculates the risk of death at one year for patients with hip fractures, is simple, and could detect high risk patients that need special management.