Intelligence-based medicine最新文献

{"title":"Development of binary-based prediction models for colorectal polyps","authors":"Aaron Morelos-Gomez ,&nbsp;Kohjiro Tokutake ,&nbsp;Ken-ichi Hoshi ,&nbsp;Akira Matsushima ,&nbsp;Armando David Martinez-Iniesta ,&nbsp;Michio Katouda ,&nbsp;Syogo Tejima ,&nbsp;Morinobu Endo","doi":"10.1016/j.ibmed.2025.100236","DOIUrl":"10.1016/j.ibmed.2025.100236","url":null,"abstract":"<div><h3>Background and aims</h3><div>Even though several colorectal cancer (CRC) screening strategies can lower CRC mortality, screening rates remain low. Removing polyps to achieve a clean colon is effective in preventing CRC. This study evaluated the possibility of using artificial intelligence to select features and threshold values required to construct an optimal screening model to prevent colorectal neoplasia.</div></div><div><h3>Methods</h3><div>The collected data consisted of medical check-ups, blood analysis, demographics, colonoscopy observations, and fecal immunochemical test (FIT). The data was divided according to sex and used to construct a screening model that converted each feature into a zero or a one based on a threshold value obtained through particle swarm optimization and the best group of features was selected by sequential combinations. Three optimization targets were evaluated: Mathew's correlation coefficient, the area under the curve, and the minimum between sensitivity and specificity.</div></div><div><h3>Results</h3><div>Using the minimum between sensitivity and specificity as an optimization target the obtained models yielded better overall prediction metrics. The optimization algorithm selected three features for women and ten features for men. The optimized models for both sexes agree that obesity is determinant for predicting polyps according to the selected features. In addition, both models outperform traditional FIT which is used for colorectal cancer screening.</div></div><div><h3>Conclusions</h3><div>The developed algorithm is effective in creating polyp screening models for men and women based on medical data with higher prediction metrics than FIT. In addition, the obtained threshold values and prediction probability can act as a guide for medical practitioners.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100236"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk-based clinical scheduling tool for congenital cardiac catheterization procedures","authors":"Juan C. Ibla ,&nbsp;Kathy J. Jenkins ,&nbsp;Madison Ramsey ,&nbsp;Sarah G. Kotin ,&nbsp;Haven Liu ,&nbsp;Paige McAleney ,&nbsp;Bennett Miller ,&nbsp;Rebecca Olson ,&nbsp;Diego Porras ,&nbsp;Jessily Ramirez ,&nbsp;Sybil A. Russell ,&nbsp;James R. Thompson ,&nbsp;David Slater ,&nbsp;Brian P. Quinn","doi":"10.1016/j.ibmed.2025.100289","DOIUrl":"10.1016/j.ibmed.2025.100289","url":null,"abstract":"<div><h3>Background</h3><div>In centers with multiple catheterization laboratories and other complex procedural units, cases are scheduled to occur simultaneously, resulting in shared resources and compounding risk factors within the care environment. Additional complexity arises from the heterogeneous nature of procedure types, the frequency of cases and turnover, and the resource requirements necessary to care for these patients. This complexity necessitates an innovative approach to scheduling that enhances both safety and efficiency.</div></div><div><h3>Methods</h3><div>In collaboration, The MITRE Corporation and the cardiac catheterization laboratory at Boston Children's Hospital (BCH) developed a tool that allows decision-making about scheduling cases to be based on risk and resource utilization. The aim of this study is to leverage to validate human-interpretable scheduling heuristics that decrease system-level risk, increase system-level efficiency, and are easily integrated into existing scheduler workflows.</div></div><div><h3>Results</h3><div>The Points Split heuristic produced schedules with much fewer unbalanced days compared to the Baseline and Points heuristics. The median count of unbalanced days per year for the Points Split heuristic was 7, compared to 78 and 110 unbalanced days per year for the Points and Baseline heuristics, respectively.</div></div><div><h3>Conclusions</h3><div>This machine learning-enhanced scheduling tool effectively aligns patient risk with resource availability, thereby enhancing operational efficiency and safety in pediatric cardiac catheterization labs. The rule-based scheduling heuristic was also found to be robust to a variety of lab configurations, case arrival rates, and patient population conditions. The approach holds promise for broader application in complex medical environments where procedure scheduling impacts patient outcomes.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100289"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145047874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements in artificial intelligence for colorectal cancer: A comprehensive overview of systematic reviews","authors":"Aurea Valeria Pereira Silva,&nbsp;Plinio Sa Leitao-Junior","doi":"10.1016/j.ibmed.2025.100262","DOIUrl":"10.1016/j.ibmed.2025.100262","url":null,"abstract":"<div><h3>Background:</h3><div>Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide. Computational intelligence (CI) has emerged as a promising tool to improve diagnosis, staging, and treatment, but evidence remains scattered across the literature.</div></div><div><h3>Objective:</h3><div>This tertiary review aims to synthesize systematic reviews on CI applications in CRC care, highlighting algorithms, datasets, performance metrics, clinical scopes, and methodological gaps.</div></div><div><h3>Methods:</h3><div>A structured search in PubMed and EMBASE identified systematic reviews published between 2018 and 2023, following PRISMA guidelines. Twenty-two reviews were included. Extracted data covered CI techniques, evaluation methods, target outcomes, and dataset characteristics. Risk of bias was assessed using AMSTAR 2, and overlap of primary studies was analyzed through a correlation matrix.</div></div><div><h3>Results:</h3><div>The reviews addressed four clinical scopes: macroscopic lesion classification (colonoscopy), histological analysis, disease staging, and survival or treatment prediction. Convolutional neural networks (CNNs) were the most commonly used models. While some applications showed high performance (AUC <span><math><mo>&gt;</mo></math></span> 0.90), most reviews had low to moderate methodological quality. Key limitations included lack of external validation, dataset heterogeneity, and limited generalizability. Significant overlap was observed in studies focused on colonoscopy-based tasks.</div></div><div><h3>Conclusion:</h3><div>CI offers valuable contributions to CRC management, but broader clinical adoption is hindered by methodological inconsistencies and insufficient validation. This review provides a comprehensive synthesis to guide future research and promote the development of robust, explainable, and generalizable models for clinical use.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100262"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Erratum regarding previously published articles","authors":"","doi":"10.1016/j.ibmed.2025.100234","DOIUrl":"10.1016/j.ibmed.2025.100234","url":null,"abstract":"","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100234"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144195201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dual deep learning framework using MaskShiftNet and transformer-augmented CNNs for oral cancer diagnosis from histopathology images","authors":"R. Dharani ,&nbsp;K. Danesh","doi":"10.1016/j.ibmed.2025.100301","DOIUrl":"10.1016/j.ibmed.2025.100301","url":null,"abstract":"<div><div>Oral squamous cell carcinoma (OSCC) is the most prevalent form of oral cancer and poses a significant health threat to the community due to its high death rate. The early detection of OSCC serves as a crucial element for both successful treatment and better patient survival outcomes. A biopsy represents the traditional method for OSCC detection which requires extensive manual processing and expert evaluation. This paper introduces two innovative deep learning architectures, MaskShiftNet and a combined Convolutional neural network with vision Transformer Network (CNN-TransNet), for the efficient segmentation and classification of OSCC from histopathology images. MaskShiftNet amalgamates color, texture, and shape attributes to precisely delineate malignant areas, enhancing localization while minimizing false positives and negatives. CNN-TransNet is a hybrid model that integrates CNN with transformer-based attention mechanisms for efficient gathering of local as well as global contextual data for the robust identification of early-stage OSCC. Comprehensive experimental assessments indicate that the suggested framework outperforms current methodologies, achieving a classification accuracy of 98.94 %, with precision, sensitivity, and specificity at 98.9 %, 98.96 %, and 97.18 %, respectively. Ablation experiments further emphasize the essential functions of segmentation and hybrid feature extraction in improving OSCC classification. These findings validate the capability of CNN-TransNet as a dependable and effective instrument for automated oral cancer detection.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100301"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145264864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Alzheimer's disease detection: An explainable machine learning approach with ensemble techniques","authors":"Eram Mahamud ,&nbsp;Md Assaduzzaman ,&nbsp;Jahirul Islam ,&nbsp;Nafiz Fahad ,&nbsp;Md Jakir Hossen ,&nbsp;Thirumalaimuthu Thirumalaiappan Ramanathan","doi":"10.1016/j.ibmed.2025.100240","DOIUrl":"10.1016/j.ibmed.2025.100240","url":null,"abstract":"<div><div>Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that necessitates early and accurate diagnosis for effective intervention. This study presents a novel machine learning (ML)-driven predictive framework for AD diagnosis, integrating Explainable Artificial Intelligence (XAI) methodologies to enhance interpretability. The dataset, sourced from Kaggle, comprises 2149 patient records with 34 distinct attributes, representing a comprehensive range of demographic, clinical, and lifestyle-related factors. To improve model robustness, rigorous data preprocessing techniques were employed, including mean/mode imputation for missing values, feature scaling using min-max normalization, and class balancing via SMOTE, SMOTEENN, and ADASYN. Feature selection technique was performed using Chi-Square and Recursive Feature Elimination (RFE) to retain the most relevant predictors. Various ML models—including Naïve Bayes, Decision Tree, Random Forest, Logistic Regression, AdaBoost, XGBoost, K-Nearest Neighbors (KNN), and Gradient Boosting—were assessed using accuracy, precision, recall, F1-score, and AUC (Area Under the Curve). The proposed ensemble model, combining LightGBM (LGBM) and Random Forest (RF) with Chi-Square feature selection and utilizing soft voting, achieved the highest test accuracy of 96.35 %, surpassing existing models. Additionally, SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) were utilized to interpret the model's decision-making process, identifying key risk factors and improving transparency for clinical applications. These findings highlight the potential of ML and XAI in advancing AD diagnosis, with future work aiming to validate the model on larger, more diverse datasets and integrate it into real-world clinical workflows.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100240"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning applications for predicting fracture of the adjacent vertebra after vertebroplasty","authors":"Maede Hasanpour ,&nbsp;Mohammadjavad (Matin) Einafshar ,&nbsp;Mohammad Haghpanahi ,&nbsp;Elie Massaad ,&nbsp;Ali Kiapour","doi":"10.1016/j.ibmed.2025.100205","DOIUrl":"10.1016/j.ibmed.2025.100205","url":null,"abstract":"<div><h3>Background</h3><div>Vertebroplasty, a minimally invasive procedure for treating vertebral compression fractures, has shown promising clinical outcomes due to its straightforward surgical technique, low complication rate, and rapid pain relief. However, a significant concern is the 25 % rate of subsequent vertebral fractures following treatment, with 50–67 % of these occurring in adjacent vertebrae that were previously augmented.</div></div><div><h3>Purpose</h3><div>To develop predictive models for fractures in vertebrae adjacent to those treated with vertebroplasty using machine learning techniques and a classification method based on pre-determined risk factors.</div></div><div><h3>Methods</h3><div>A retrospective study has been conducted to discover potential factors that could influence the effectiveness of vertebroplasty. Models were developed using data from 84 patients with osteoporotic vertebral compression fractures (OVCF) who underwent vertebroplasty. K-nearest neighbors (KNN), decision tree (DT), support vector machine (SVM), and logistic regression (LR) algorithms were used to predict fractures at the adjacent level of the augmented vertebra after vertebroplasty. The accuracies of the models were also reported.</div></div><div><h3>Results</h3><div>The DT and LR models achieved an accuracy of 0.94, while KNN and SVM models had an accuracy of 0.88. The DT identified bone mineral density (BMD), cement volume, and cement stiffness as key predictive factors. In contrast, the LR determined BMD, cement volume, and cement location to be the most essential features. Furthermore, the DT and LR models demonstrated the highest macro-average and weighted average metrics, calculated as 0.92 and 0.95, respectively.</div></div><div><h3>Conclusion</h3><div>The high accuracies achieved by the machine learning models confirm their effectiveness in predicting subsequent adjacent vertebral fractures (SAVF) following vertebroplasty. Utilizing these predictive models in clinical practice may enable the successful identification of patients at high risk for SAVF, potentially contributing to preventing these complications through personalized treatment planning and follow-up care.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100205"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility study: Detection of developmental dysplasia of the hip using ultrasound performed by a novice user","authors":"Fleur L.E. Kersten ,&nbsp;Chris L. de Korte ,&nbsp;Maartje M.R. Verhoeven ,&nbsp;Willemijn M. Klein ,&nbsp;Thomas L.A. van den Heuvel","doi":"10.1016/j.ibmed.2025.100228","DOIUrl":"10.1016/j.ibmed.2025.100228","url":null,"abstract":"<div><div>Developmental hip dysplasia (DDH) affects 1 %–4 % of infants globally, and ultrasound is the standard method to diagnose using Graf's method. However, ultrasound is not commonly used in primary care tool due to the required extensive training. Instead, physicians rely on physical examinations and risk stratification, resulting in a significant number of infants without DDH being referred. This study aimed to investigate if a novice user could be trained within 1 h to use an AI-assisted handheld ultrasound device to diagnose DDH.</div><div>The novice user conducted hip ultrasounds on 31 infants at the Radboud UMC. Trained radiologists performed ultrasounds on the same infants, serving as the ground truth. The ultrasound acquisitions by the novice user were evaluated by a pediatric radiologist to determine if they adhered to the standard plane of Graf. When deemed sufficient, the pediatric radiologist assessed if DDH could be excluded, and subsequently, it was compared to the ground truth diagnosis.</div><div>The ground truth identified 28 infants as no DDH, of which 23 (82 %) had AI-assisted ultrasounds in line with the standard plane of Graf, so DDH could be excluded. Additionally, all three infants with DDH (100 %) were correctly identified by the AI-assisted ultrasounds as ‘not excluding DDH’. This study demonstrates that it is possible for a novice user to acquire ultrasound images that satisfy the Graf criteria in 82 % of infants with 1 h of training. Such an approach could reduce the barrier of introducing ultrasound in the first-line of care and decrease the number of referred infants without DDH.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100228"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143479403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing hyperparameters for dual-attention network in lung segmentation","authors":"Rima Tri Wahyuningrum ,&nbsp;Rizki Abdil Fadillah ,&nbsp;Indah Yunita ,&nbsp;Budi Dwi Satoto ,&nbsp;Arif Muntasa ,&nbsp;Amillia Kartika Sari ,&nbsp;Paulus Rahardjo ,&nbsp;Deshinta Arrova Dewi ,&nbsp;Achmad Bauravindah","doi":"10.1016/j.ibmed.2025.100221","DOIUrl":"10.1016/j.ibmed.2025.100221","url":null,"abstract":"<div><div>Medical imaging, particularly chest X-rays (CXR), is a cornerstone in the diagnosis of lung diseases, such as pneumonia, tuberculosis and COVID-19, owing to its accessibility and effectiveness. However, the sheer volume of CXR images, especially during pandemics, combined with the complexity of subtle abnormalities, poses significant challenges for manual analysis. Lung segmentation plays a pivotal role in artificial intelligence-driven CXR analysis by isolating lung fields, which facilitates the detection of disease-affected regions. Recent advances in deep learning, particularly with attention mechanisms, have improved segmentation accuracy, but the performance of these models heavily depends on the selection of appropriate hyperparameters. This study investigates the impact of key hyperparameters—learning rate and number of epochs—on the performance of the dual-attention network (DANet) in lung segmentation tasks. DANet was tested on a CXR dataset from Qatar University and evaluated under four different hyperparameter configurations: 20 epochs with a learning rate of 0.001, 10 epochs with a learning rate of 0.001, 10 epochs with a learning rate of 0.0001 and 20 epochs with a learning rate of 0.0001. The model's performance was assessed using two widely recognised segmentation metrics: the Dice coefficient and Intersection over Union (IoU). The results indicated that higher learning rates and greater numbers of epochs lead to improved segmentation performance. Specifically, the DANet model achieved a Dice coefficient of 97.29 % and an IoU value of 94.74 %, demonstrating its effectiveness compared to other models. These findings highlight the importance of hyperparameter tuning in achieving high segmentation accuracy and demonstrate the potential of the DANet model to improve diagnostic workflows for CXR analysis.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100221"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"“Evaluation of screening parameters and machine learning models for the prediction of neonatal sepsis: A systematic review.”","authors":"Peace Ezeobi Dennis ,&nbsp;Angella Musiimenta ,&nbsp;Wasswa William ,&nbsp;Stella Kyoyagala","doi":"10.1016/j.ibmed.2024.100195","DOIUrl":"10.1016/j.ibmed.2024.100195","url":null,"abstract":"<div><div>About 2.9 million neonates die every year worldwide, and most of these deaths occur in low resource settings where it causes about 30–50 % of the total neonatal deaths annually. Neonatal sepsis occurs when there is a bacterial invasion in the bloodstream; the immune system begins a systemic inflammatory response syndrome (SIRS) damaging to the body and can quickly advance to severe sepsis, multi-organ failure, and finally, death. Sepsis in neonates can progress more rapidly than in adults; therefore, timely diagnosis is critical. The gold standard test for diagnosing neonatal sepsis is blood culture, which takes at least 72 h. Hence, identifying key predictor variables and models that work best can help reduce neonatal morbidity and mortality.</div><div>Matching articles were identified by searching PubMed, IEEE, and Cochrane bibliography databases. Full-text articles with the following criteria were included for analysis based on 1) the subject population are neonates. 2) the study provided a clear definition of neonatal sepsis. 3) the study provides neonatal sepsis onset definition (i.e., time of onset). 4) the study clearly described the predictor variables used. 5) the study clearly described machine learning models used or evaluated any of the consolidated screening parameters. 6) the study must have provided diagnostic performance results. Thirty-one studies met full inclusion criteria. The duration of ROM was found to be more significant than other maternal risk factors. Heart rate and heart rate variability were found to be more significant than other neonatal clinical signs. C reactive protein and I/T ratio were found to be more significant than other laboratory tests.</div><div>A combination of predictor variables has shown to strengthen neonatal sepsis prediction, as shown by some of the reviewed studies. Predictive algorithms that combine multiple variables are urgently needed to improve models for early detection, prognosis, and treatment of neonatal sepsis.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100195"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}