Development Of Predictive Models Research Articles

457P Development of absolute 5-year risk prediction model for gastric cancer and comparisons with current guideline in China: A population-based, prospective study

457P Development of absolute 5-year risk prediction model for gastric cancer and comparisons with current guideline in China: A population-based, prospective study

Development and Validation of an In-Hospital Mortality Prediction Model for Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease.

Patients with chronic obstructive pulmonary disease (COPD) often face unknown risks during acute exacerbation of the disease (AECOPD), which could potentially result in mortality. This study aimed to develop and validate a nomogram model for predicting the risk of in-hospital mortality in AECOPD patients. Clinical data of patients hospitalized at The Second People's Hospital of Wuhu City for AECOPD between January 2013 and December 2022 were retrospectively collected. Variables underwent selection through LASSO regression and multivariable logistic regression to develop a nomogram model. The model's predictive performance was assessed using the concordance index, calibration curve, and decision curve analysis (DCA), with internal validation conducted using the bootstrap method. A total of 1224 patients were included in this study, with 98 (8%) deaths occurring during hospitalization. LASSO regression identified 11 variables, used to construct model A. Further multivariable logistic regression was conducted to select variables with P < 0.05 to establish model B. model B was selected as the final model based on discrimination, calibration, and clinical utility, encompassing variables including acute respiratory failure, lung cancer, heart rate, hemoglobin, absolute neutrophil count, serum albumin, blood urea nitrogen, and serum chloride. The nomogram model achieved a concordance index of 0.858. Internal validation of the model was conducted using the bootstrap method with 500 repetitions, resulting in a concordance index of 0.851 (95% CI: 0.805, 0.893). The calibration curve demonstrated a good fit, with a Hosmer-Lemeshow goodness-of-fit test P-value of 0.520. Moreover, DCA findings suggested patient benefit within a threshold probability range of 0.02 to 0.73, with a maximum net benefit of 0.07. The model constructed in this study has good predictive performance, which helps clinical doctors identify patients at high risk of death early.

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.

Neoadjuvant immunochemotherapy and new radiomic predictor for resectable locally advanced oral squamous cell carcinoma.

6090 Background: The effectiveness of neoadjuvant immunochemotherapy (NAIC) in treating resectable locally advanced oral squamous cell carcinoma (LAOSCC) remains uncertain, with a notable lack of precise prognostic markers for NAIC outcomes. Methods: A single-arm prospective phase II trial was performed to assess the efficacy and safety of NAIC, consisting of 2 cycles of intravenous camrelizumab (200 mg), albumin paclitaxel (260 mg/m²), and cisplatin (75 mg/m²) administered every 3 weeks prior to radical surgery in LAOSCC patients. Adjuvant radiotherapy or chemoradiotherapy was determined by post-surgical pathology. Patients with previously untreated, stage III-IVB LAOSCC (the 8th edition of UICC/AJCC staging system), aged 18-75 years were enrolled. The primary endpoints were major pathological response (MPR, defined as ≤10% residual viable tumour (%RVT) cells) and safety, the second primary point was objective response rate (ORR) according to RECIST 1.1. To identify potential radiomic markers predictive of NAIC benefit, oscillating-gradient spin-echo (OGSE) magnetic resonance imaging sequences were obtained before and after NAIC. Results: From Feb 1, 2023 to Jan 1, 2024, 30 patients were enrolled, among which, 26 patients have received NAIC followed by surgery. The MPR rate was 65.4% (17/26), including a 34.6% (9/26) pathological complete response. Patients with PD-L1 (Combined Positive Score) ≥ 1 had higher MPR (80.0%, 16/20) than those with PD-L1 &lt; 1 (16.7%, 1/6). NAIC was well-tolerated, without adverse effects on subsequent surgery, only two patients experienced grade 3 or 4 adverse events during NAIC, including one with grade 4 neutropenia (3.8%), and one with grade 3 thrombocytopenia (3.8%). The ORR was 80.8% (21/26), with 15.4% (4/26) complete response, 65.4% (17/26) partial response, and with no cases of progressive disease observed. The mean cell diameters of the whole tumor were calculated based on the OGSE sequences, twenty patients completed the OGSE scaning pre- and post-NAIC treatment. Pearson correlation analyses found that small post-NAIC mean cell diameter significantly related with fewer %RVT ( p = 0.026), supporting its potential as new radiomic predictors of NAIC efficacy. Among patients with MPR, the cell diameters were significantly decreased after NAIC, with a mean cell diameter of 18.87 ± 2.49 μm and 14.77 ± 1.85 μm for pre- and post-NAIC tumor, respectively ( p = 0.001). Conclusions: Camrelizumab combined with albumin paclitaxel/cisplatin as NAIC for LAOSCC demonstrates promising MPR and well safety. Mean cell diameter of tumor, derived from OGSE sequences, emerges as a potential new radiomic marker for predicting NAIC efficacy. Ongoing follow-ups on long-term survival and the development of a bio-radiomic prediction model integrating pathological features with OGSE data are underway. Clinical trial information: ChiCTR2200066119.

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.

Advancing Solar Energetic Particle Event Prediction through Survival Analysis and Cloud Computing. I. Kaplan–Meier Estimation and Cox Proportional Hazards Modeling

Solar energetic particles (SEPs) pose significant challenges to technology, astronaut health, and space missions. This initial paper in our two-part series undertakes a comprehensive analysis of the time to detection for SEPs, applying advanced statistical techniques and cloud-computing resources to deepen our understanding of SEP event probabilities over time. We employ a range of models encompassing nonparametric, semiparametric, and parametric approaches, such as the Kaplan–Meier estimator and Cox Proportional Hazards models. These are complemented by various distribution models—including exponential, Weibull, lognormal, and log-logistic distributions—to effectively tackle the challenges associated with “censored data,” a common issue in survival analysis. Employing Amazon Web Services and Python’s “lifelines” and “scikit-survival” libraries, we efficiently preprocess and analyze large data sets. This methodical approach not only enhances our current analysis, but also sets a robust statistical foundation for the development of predictive models, which will be the focus of the subsequent paper. In identifying the key determinants that affect the timing of SEP detection, we establish the vital features that will inform the machine-learning (ML) techniques explored in the second paper. There, we will utilize advanced ML models—such as survival trees and random survival forests—to evolve SEP event prediction capabilities. This research is committed to advancing space weather, strengthening the safety of space-borne technology, and safeguarding astronaut health.

Development of a predictive model for pediatric atopic dermatitis: A retrospective cross-sectional nationwide database study

BackgroundThe rise in prevalence of atopic dermatitis (AD) has been correlated with numerous elements of the exposome, modern-day lifestyle, and familial history. The combined analysis of familial history and other risk elements may allow us to understand the driving factors behind the development of AD. ObjectiveTo develop prediction models to assess the risk of developing AD using a large and diverse cohort (N = 77,525) and easily assessed risk factors. MethodsWe analyzed electronic medical record data from Leumit Health System. Documented predictive factors include sex, season of birth, environment (urban/rural), socioeconomic status, household smoking, diagnosed skin conditions, number of siblings, a paternal, maternal, or sibling history of an atopic condition, and antibiotic prescriptions during pregnancy or after birth. Predictive models were trained and validated on the data set. ResultsMedium (odds ratio [OR] 2.04, CI 1.92-2.17, P < .001) and high (OR 2.13, CI 1.95-2.34, P < .001) socioeconomic status, a previous diagnosis of contact dermatitis (OR 2.57, CI 2.37-2.78, P < .001), presence of siblings with an AD diagnosis (OR 2.21, CI 2.04-2.40, P < .001), and the percentage of siblings with any atopic condition (OR 2.58, CI 2.09-3.17, P < .001) drove risk for AD in a logistic regression model. A random forest prediction model with a sensitivity of 61% and a specificity of 84% was developed. Generalized mixed models accounting for the random effect of familial relationships boasted an area under the curve of 0.98. ConclusionPredictive modeling using noninvasive and accessible inputs is a powerful tool to stratify risk for developing AD.

The effectiveness of mapping-targeted biopsies on the index lesion in transperineal prostate biopsies.

To evaluate the effectiveness of mapping-targeted biopsies (MTB) on the index lesion for the detection of clinically significant prostate cancer (csPCa) in transperineal fusion-image prostate biopsies. A retrospective analysis was conducted on 309 men with suspected PCa who underwent prostate biopsies at the Creu Blanca reference center in Barcelona, Spain. The Prostate Imaging-Reporting and Data System (PI-RADS v.2.1) of the magnetic resonance images (MRI) were reclassified by an expert radiologist reading of pre-biopsy biparametric MRI used for segmentation of suspected lesions. Transperineal MTB of suspicious lesions and 12-core systematic biopsies were performed using the Artemis™ platform. CsPCa was defined as International Society of Urological Pathology grade group ≥ 2. CsPCa was detected in 192 men (62.1%), with detection rates of 6.3% for PI-RADS 2, 26.8% for PI-RADS 3, 87.3% for PI-RADS 4, and 93.1% for PI-RADS 5. MTB of the index lesion identified 185 csPCa (96.3%). CsPCa was detected solely in systematic biopsies in three cases (1.6%), while an additional four cases (2.1%) were identified only in the second suspected lesion. A predictive model for csPCa detection in MTB of the index lesion was developed, with an AUC of 0.918 (95% CI 0.887-0.950). This model had the potential to avoid 23.3% of prostate biopsies without missing additional csPCa cases. MTB of the index lesion was highly effective for identifying csPCa in fusion transperineal prostate biopsies. A developed predictive model successfully reduced the need for almost one quarter of biopsies without missing csPCa cases.

Predictions for functional outcome and mortality in acute ischaemic stroke following successful endovascular thrombectomy

BackgroundAccurate outcome predictions for patients who had ischaemic stroke with successful reperfusion after endovascular thrombectomy (EVT) may improve patient treatment and care. Our study developed prediction models for key clinical...

Machine learning-based model for predicting outcomes in cerebral hemorrhage patients with leukemia

Background and purposeIntracranial hemorrhage (ICH) in leukemia patients progresses rapidly with high mortality. Limited data are available on imaging studies in this population. The study aims to develop prediction models for 7-day and short-term mortality risk based on the non-contrast computed tomography (NCCT) image features. MethodsThe NCCT image features of ICH in 135 leukemia patients between 2007–2023 were retrospectively extracted using manual assessment and radiomics methods. After multiple imputation of missing laboratory data, univariate logistic regression and least absolute shrinkage and selection operator (LASSO) were used for feature selection. Random forest models were built with comprehensive evaluation and ranking of feature importance. Result135 and 129 patients were included in the studies for 7-day and short-term prognostic models, respectively. The median age of all enrolled patients was 35 years, and there were 86 male patients (63.7 %). Clinical models (validation: AUC [area under the curve] = 0.78, AUPRC [area under the precision-recall curve] = 0.73; AUC = 0.84, AUPRC = 0.86), radiomics models (validation: AUC = 0.82, AUPRC = 0.78; AUC = 0.75, AUPRC = 0.77), and the combined models (validation: AUC = 0.84, AUPRC = 0.83; AUC = 0.87, AUPRC = 0.89) predicted 7-day and short-term mortality with good predictive efficacy. Clinical decision curve analysis showed that the combined models predicted 7-day and 30-day risk of death would be more beneficial than other models. Shape features contributed significantly more than semantic features in both radiomics models and combined models (93.3 %, 52.1 %, as well as 85.2 %,37.4 %, respectively) for 7-day and 30-day mortality. ConclusionsCombined models constructed based on NCCT perform well in predicting the risk of 7-day and short-term mortality in ICH patients with leukemia. Shape features extracted by radiomics are important markers for modeling the prognosis.

LASSO-derived model for the prediction of bleeding in aspirin users

Aspirin is widely used for both primary and secondary prevention of panvascular diseases, such as stroke and coronary heart disease (CHD). The optimal balance between reducing panvascular disease events and the potential increase in bleeding risk remains unclear. This study aimed to develop a predictive model specifically designed to assess bleeding risk in individuals using aspirin. A total of 58,415 individuals treated with aspirin were included in this study. Detailed data regarding patient demographics, clinical characteristics, comorbidities, medical history, and laboratory test results were collected from the Affiliated Dongyang Hospital of Wenzhou Medical University. The patients were randomly divided into two groups at a ratio of 7:3. The larger group was used for model development, while the smaller group was used for internal validation. To develop the prediction model, we employed least absolute shrinkage and selection operator (LASSO) regression followed by multivariate logistic regression. The performance of the model was assessed through metrics such as the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA). The LASSO-derived model employed in this study incorporated six variables, namely, sex, operation, previous bleeding, hemoglobin, platelet count, and cerebral infarction. It demonstrated excellent performance at predicting bleeding risk among aspirin users, with a high AUC of 0.866 (95% CI 0.857–0.874) in the training dataset and 0.861 (95% CI 0.848–0.875) in the test dataset. At a cutoff value of 0.047, the model achieved moderate sensitivity (83.0%) and specificity (73.9%). The calibration curve analysis revealed that the nomogram closely approximated the ideal curve, indicating good calibration. The DCA curve demonstrated a favorable clinical net benefit associated with the nomogram model. Our developed LASSO-derived predictive model has potential as an alternative tool for predicting bleeding in clinical settings.

Development of a machine learning-based prediction model for clinical pregnancy of intrauterine insemination in a large Chinese population.

This study aimed to evaluate the effectiveness of a random forest (RF) model in predicting clinical pregnancy outcomes from intrauterine insemination (IUI) and identifying significant factors affecting IUI pregnancy in a large Chinese population. RESULTS: A total of 11 variables, including eight from female (age, body mass index, duration of infertility, prior miscarriage, and spontaneous abortion), hormone levels (anti-Müllerian hormone, follicle-stimulating hormone, luteinizing hormone), and three from male (smoking, semen volume, and sperm concentration), were identified as the significant variables associated with IUI clinical pregnancy in our Chinese dataset. The RF-based prediction model presents an area under the receiver operating characteristic curve (AUC) of 0.716 (95% confidence interval, 0.6914-0.7406), an accuracy rate of 0.6081, a sensitivity rate of 0.7113, and a specificity rate of 0.505. Importance analysis indicated that semen volume was the most vital variable in predicting IUI clinical pregnancy. The machine learning-based IUI clinical pregnancy prediction model showed a promising predictive efficacy that could provide a potent tool to guide selecting targeted infertile couples beneficial from IUI treatment, and also identify which parameters are most relevant in IUI clinical pregnancy.

Developing a Machine Learning Model to Predict 180-day Readmission for Elderly Patients with Angina.

Readmission of elderly angina patients has become a serious problem, with a dearth of available prediction tools for readmission assessment. The objective of this study was to develop a machine learning (ML) model that can predict 180-day all-cause readmission for elderly angina patients. The clinical data for elderly angina patients was retrospectively collected. Five ML algorithms were used to develop prediction models. Area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC), and the Brier score were applied to assess predictive performance. Analysis by Shapley additive explanations (SHAP) was performed to evaluate the contribution of each variable. A total of 1502 elderly angina patients (45.74% female) were enrolled in the study. The extreme gradient boosting (XGB) model showed good predictive performance for 180-day readmission (AUROC = 0.89; AUPRC = 0.91; Brier score = 0.21). SHAP analysis revealed that the number of medications, hematocrit, and chronic obstructive pulmonary disease were important variables associated with 180-day readmission. An ML model can accurately identify elderly angina patients with a high risk of 180-day readmission. The model used to identify individual risk factors can also serve to remind clinicians of appropriate interventions that may help to prevent the readmission of patients.

High-throughput color determination of red raspberry puree and correlation of color parameters with total anthocyanins

BackgroundRed raspberry fruit color is a key driver of consumer preference and a major target of breeding programs worldwide. Screening for fruit color typically involves the determination of anthocyanin content and/or the assessment of color through a colorimeter. However, both procedures are time-consuming when the analyses involve hundreds or thousands of samples. The objectives of this study were to develop a high-throughput method for red raspberry puree color measurement and to test the correlations between color parameters and total anthocyanin content. Color coordinates were collected with a colorimeter on 126 puree samples contained in Petri dishes and with the Tomato Analyzer Color Test (TACT) module to assess the same samples prepared in Petri dishes and in 96-well plates. An additional 425 samples were analyzed using only 96-well plates. Total anthocyanins were extracted from all 551 samples.ResultsRegression models for L*, a*, b* measured with the colorimeter and TACT using Petri dishes were all significant (p < 0.001), but very consistent only for L* (R2 = 0.94). Significant (p < 0.001) and very consistent regressions (R2 = 0.94 for L* and b*, R2 = 0.93 for a*) were obtained for color parameters measured with TACT using Petri dishes and TACT using plates. Of the color parameters measured with the colorimeter, only L*, a*/b*, and hue significantly correlated with total anthocyanins (p < 0.05), but, except for L* (R = − 0.79), the correlations were weak (R = − 0.23 for a*/b* and R = 0.22 for hue). Conversely, all correlations with total anthocyanins and color parameters measured with TACT were significant (p < 0.001) and moderately strong (e.g., R = − 0.69 for L* and R = 0.55 for a*/b*). These values were indicative of darker colors as total anthocyanin content increased.ConclusionsWhile the colorimeter and TACT-based methods were not fully interchangeable, TACT better captured color differences among raspberry genotypes than the colorimeter. The correlations between color parameters measured with TACT and total anthocyanins were not strong enough to develop prediction models, yet the use of TACT with 96-well plates instead of Petri dishes would enable the high-throughput measurement of red raspberry puree color.

PREDICTING DRUG-DRUG INTERACTION USING MACHINE LEARNING

The ever-expanding landscape of pharmaceutical compounds and medical treatments has underscored the critical need for an advanced system to predict drug-drug interactions (DDIs) using machine learning. This paper delves into the development of a novel predictive model that leverages diverse data sources, including chemical structures, pharmacological properties, and scientific literature, to comprehensively analyse and foresee potential DDIs. The current state of DDIs is rife with limitations in scope and accuracy, making the development of an advanced system not only a necessity for enhancing patient safety but also a boon to pharmaceutical research and regulatory oversight. The relationship between two medications in which one drug’s pharmacological actions are altered by another is known as a drug-drug interaction (DDI). Favourable drug-drug interactions (DDIs) generally contribute to improved therapeutic results for patients. On the contrary, adverse DDIs pose a significant risk, often resulting in undesirable drug reactions that can potentially lead to the withdrawal of a drug from the market and even patient fatalities. Consequently, the identification of DDIs is essential for advancing new drug development and effectively managing diseases. Keywords: Drug, Drug-drug interaction, Machine learning, Bioinformatics, Drug development.

Determinants of Chromatin Organization in Aging and Cancer-Emerging Opportunities for Epigenetic Therapies and AI Technology.

This review article critically examines the pivotal role of chromatin organization in gene regulation, cellular differentiation, disease progression and aging. It explores the dynamic between the euchromatin and heterochromatin, coded by a complex array of histone modifications that orchestrate essential cellular processes. We discuss the pathological impacts of chromatin state misregulation, particularly in cancer and accelerated aging conditions such as progeroid syndromes, and highlight the innovative role of epigenetic therapies and artificial intelligence (AI) in comprehending and harnessing the histone code toward personalized medicine. In the context of aging, this review explores the use of AI and advanced machine learning (ML) algorithms to parse vast biological datasets, leading to the development of predictive models for epigenetic modifications and providing a framework for understanding complex regulatory mechanisms, such as those governing cell identity genes. It supports innovative platforms like CEFCIG for high-accuracy predictions and tools like GridGO for tailored ChIP-Seq analysis, which are vital for deciphering the epigenetic landscape. The review also casts a vision on the prospects of AI and ML in oncology, particularly in the personalization of cancer therapy, including early diagnostics and treatment optimization for diseases like head and neck and colorectal cancers by harnessing computational methods, AI advancements and integrated clinical data for a transformative impact on healthcare outcomes.

Distributed Computing Optimization in Unmanned Aerial Vehicle Swarm Cooperative Networks

The Unmanned Aerial Vehicle (UAV) swarm cooperative networks across diverse domains present significant challenges in management and control to maximize collaborative efficiency, particularly in applications for large-scale and dynamic environments where real-time coordination is essential. This challenge has led to the development of a UAV swarm cooperative network flow prediction model, facilitating the strategic anticipation of network dynamics. A novel algorithm for distributed, finite-time cooperative control is introduced, grounded in this model. This algorithm optimizes the operational efficiency and control precision of UAV systems, while simultaneously augmenting the collaborative efficiency and stability of the UAV swarms. It marks a significant advancement in distributed computing strategies, offering a viable solution for the real-time coordination challenges in extensive UAV swarm cooperative networks. The proposed approach leverages multi-agent technology, integrating spatial-temporal constraints, location limitations, and path selection in cooperative behavior for dynamic and effective network management. This study not only enhances the understanding of UAV swarm dynamics but also contributes to the practical application of UAV swarms in areas requiring precise coordination and robust network stability.

Prediction of compressive strength of cross‐shaped stub CFSTs under axial loading: Numerical and analytical study

AbstractSpecial‐shaped concrete‐filled steel tubes (CFSTs) have been used in modern structures like high‐rise commercial and residential buildings due to their superior structural performance compared to steel and reinforced concrete members. Various shapes of special‐shaped CFSTs might be necessary to meet architectural and aesthetic needs. Cross‐shaped CFSTs could be used where two orthogonal walls cross in high‐rise buildings. However, at present, the research on the compressive performance of cross‐shaped CFSTs is limited, consequently, the unavailability of design guidelines and design‐oriented strength prediction models. Therefore, in this study, a finite element (FE) model of cross‐shaped CFSTs was developed following the past experimental data, and the model's accuracy was verified by the failure modes and load–strain curves of specimens. Sensitivity analysis was performed for some parameters of the concrete damaged plasticity model besides imperfections and residual stress. The parametric analysis was conducted considering various study parameters such as the width‐to‐thickness ratio, width‐to‐depth ratio, and steel and concrete strengths. The compressive strength of cross‐shaped CFSTs was predicted by different design codes and available design formulas, which gave unsatisfactory results necessitating the development of new strength prediction models. Finally, a new design formula was developed by performing a linear regression of FE and test results. The proposed formula predicted the strength of cross‐shaped CFSTs with great accuracy and can be used for design purposes.

Development and Validation of Machine Learning-Based Predictive Model for Prolonged Hospital Stay after Decompression Surgery for Lumbar Spinal Canal Stenosis.

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