Intelligence-based medicine最新文献

{"title":"Progressing microbial genomics: Artificial intelligence and deep learning driven advances in genome analysis and therapeutics","authors":"R. Dhaarani,&nbsp;M. Kiranmai Reddy","doi":"10.1016/j.ibmed.2025.100251","DOIUrl":"10.1016/j.ibmed.2025.100251","url":null,"abstract":"<div><div>The fusion of artificial intelligence (AI) and machine learning has revolutionized microbiology from an empirical science into a data-driven discipline. With the emergence of high-throughput sequencing and multi-omics platforms, microbiologists and clinicians can depend on computational tools to interpret complex data sets, predict antimicrobial resistance (AMR), and design novel therapeutic strategies. The review intends to provide a detailed analysis of AI/ML applications in microbiology, pointing out their roles in genomics, metagenomics, AMR detection, microbial ecology, and CRISPR-based genomic editing in the field of health care settings. It illustrates the recent innovations, practical tools, and challenges in implementing intelligent systems in biomedical microbiology. A structured evaluation was conducted on the present literature databases and AI-driven bioinformatics tools and focused on deep learning models and stochastic methods, specifically on algorithms used across genomic analysis, microbial research, and resistance prediction workflows. AI has empowered rapid genome annotation, functional gene prediction, and identification of biosynthetic gene clusters. ML helps in taxonomic classifications, inference of metabolic pathways, and modeling of synthetic microbiomes. By using AI, about 860,000 novel antimicrobial peptides were identified, and most of them were validated through experiments. Tools such as MG-RAST, antiSMASH, ResFinder, and CRISPR-SID have improved microbial identification and use in clinical settings, giving a mark on the function of the CRISPR-Cas system through deep learning. Even the interactions of microbes, their adaptations, and their potential for bioremediation have been proved through AI models. However, these advancements encounter challenges such as model bias, data heterogeneity, lack of transparency, and infrastructure limitations. Addressing the present challenges through explainable AI (XAL), governance of ethical data, and enhanced computational infrastructure will be a safe and effective use of intelligent technologies in this field.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100251"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877378","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":"ETDHDNet: An advanced DenseNet-based extended texture descriptor for efficient tuberculosis prediction in CXR images","authors":"Asmaa Shati ,&nbsp;Amitava Datta ,&nbsp;Atif Mansoor ,&nbsp;Ghulam Mubashar Hassan","doi":"10.1016/j.ibmed.2025.100269","DOIUrl":"10.1016/j.ibmed.2025.100269","url":null,"abstract":"<div><div>Chest X-ray imaging (CXR) for the prediction of tuberculosis (TB) is essential in medical diagnostics, as it plays a vital role in early detection and the development of effective treatment plans. Although Convolutional Neural Networks (CNNs) are effective in extracting features from images in a hierarchical manner, they have limitation of texture-related features due to their emphasis on capturing global patterns. To address this shortcoming, we propose an Extended Texture Descriptor Histogram DenseNet (ETDHDNet), a model designed for TB prediction from CXR images by integrating texture analysis with deep feature learning. ETDHDNet consists of three key components: the Extended Texture Descriptor Histogram (ETDH) module to capture multi-scale texture features across fine, medium, and coarse granularities; a hierarchical feature learning unit with densely connected layers for extracting high-level features; and a neural network for handling binary and multi-class TB prediction. Experimental results demonstrate that ETDHDNet surpasses existing methods in TB prediction from CXR images across three datasets. For binary classification, the model achieves an AUC of 0.998 on TBX11K, 0.997 on the Tuberculosis Chest X-ray Database, and 0.930 on the Shenzhen Dataset, as well as an AUC of 0.993 for multi-class TB prediction on TBX11K.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100269"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481051","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":"New cognitive computational strategy for optimizing brain tumour classification using magnetic resonance imaging Data","authors":"R. Kishore Kanna ,&nbsp;Ayodeji Olalekan Salau","doi":"10.1016/j.ibmed.2025.100215","DOIUrl":"10.1016/j.ibmed.2025.100215","url":null,"abstract":"<div><div>The brain is one of the most important organs in the human body. It governs all actions whether one is aware of the action or not. Brain tumors occur when the system of cell division in the brain is disrupted. Brain tumors are frequently associated with severe malignancies worldwide. The uncontrolled accumulation and growth of these cells can lead to the formation of seizures or tumors with impaired brain function.</div><div>Magnetic resonance imaging (MRI) is a common technology used to detect brain lesions; however, manual analysis of MRI images by physicians is challenging due to uncertainty and time constraints. The aim of this paper is to introduce machine learning (ML) algorithms designed to increase the speed and cognitive statistical methods for brain tumor classification.</div><div>In this study, we proposed a novel penguin search-optimized quantum-enhanced support vector machine (PSO-QESVM) to categorize brain tumor using MRI data. We used a publicly accessible brain MR image dataset for brain tumor classification tasks which we obtained from an online source. A median filter (MF) was used as part of the pre-processing step to eliminate noise from the data. Using ResNet and VGG16, features were extracted from the pre-processed data.</div><div>The proposed method was implemented using Python 3.7+ software. A comparison was made between the suggested approach and other conventional algorithms. The results show the proposed method achieved a superior efficiency with regards to recall (98.9 %), accuracy (98.90 %), f1-score (98.5 %), and precision (98.7 %).</div><div>The study demonstrated the applicability of the suggested strategy for brain tumor classification. The suggested cognitive computational strategy achieved a promising performance. To reduce the size of the model and implement it on a real-time medical diagnosis framework, we intend to employ knowledge distillation techniques.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100215"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175220","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":"A hybrid feature extraction based ensemble model for breast cancer detection and classification using different medical images","authors":"A.H.M. Zadidul Karim ,&nbsp;Kazi Bil Oual Mahmud ,&nbsp;Celia Shahnaz","doi":"10.1016/j.ibmed.2025.100290","DOIUrl":"10.1016/j.ibmed.2025.100290","url":null,"abstract":"<div><div>Breast cancer is a fatal disease that has a high death rate worldwide, according to the WHO. Hence, implementing medical image-based automated breast cancer detection and classification is essential for early identification and categorization. It plays a crucial role in developing efficient treatment methods by accurately diagnosing the kind and classifying the subtype of breast cancer. Ultrasound and mammograms are primary and efficient methods for detection, whereas histopathology is an advanced method for exactly classifying breast cancer. Previously, different hand-engineered features were used for different types of data sets, respectively, which provided high accuracy individually. However, deep learning is a strong tool for computer vision tasks. Therefore, we developed a unique combination of hand-engineered features for color, shape, and texture extraction in parallel to three different deep neural networks. Such a hybrid method proposed that combines both hand-engineered and deep learning-based feature extractors provides an outstanding performance for breast cancer detection and classification on different types of datasets compared to the state of the art methods thus verifying its robustness and effectiveness.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100290"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144894966","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":"Mobile-based deep learning system for early detection of diabetic retinopathy","authors":"El-Mehdi Chakour ,&nbsp;Zineb Sadok ,&nbsp;Rostom Kachouri ,&nbsp;Anass Mansouri ,&nbsp;Idriss Benatiya Andaloussi ,&nbsp;Mohamed Akil ,&nbsp;Ali Ahaitouf","doi":"10.1016/j.ibmed.2025.100259","DOIUrl":"10.1016/j.ibmed.2025.100259","url":null,"abstract":"<div><div>Diabetic retinopathy (DR) is a leading cause of vision loss globally, especially in regions with limited access to eye care. Early detection is essential to prevent irreversible damage and improve patient outcomes. In this study, a portable, real-time Assisted Mobile Diagnostic (AMD) system for DR detection, which integrates an optimized deep learning model into a mobile platform, is presented. Unlike conventional AI-based approaches that require high-performance computing and stationary fundus cameras, our system combines a non-mydriatic retinal camera with a mobile device, enabling point-of-care diagnostics. Captured retinal images are preprocessed using techniques such as blurring and contrast enhancement before being analyzed by a fine-tuned DenseNet-121 model. The model is trained using a private dataset along with two large public datasets: APTOS (Asia Pacific Tele-Ophthalmology Society) and EyePACS (Eye Picture Archive Communication System). The proposed approach achieved a high accuracy: 97.38% on APTOS, 90.90% on EyePACS, and 98.61% on the private dataset. The system delivers real-time performance on mobile devices, with an average processing time of 162.5 ms, making it well-suited for rapid screening. This Deep learning-based mobile application is part of a multi-platform tele-ophthalmology framework that includes both tablet and desktop integrations, facilitating accessible and remote DR diagnosis.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100259"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263585","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 novel hybrid machine learning approach for early prediction of Parkinson’s disease severity using optimized feature selection and ensemble learning","authors":"Behnaz Motamedi,&nbsp;Balázs Villányi","doi":"10.1016/j.ibmed.2025.100276","DOIUrl":"10.1016/j.ibmed.2025.100276","url":null,"abstract":"<div><div>Parkinson’s disease (PD), a degenerative neurological condition that impairs motor and nonmotor skills, requires early and precise diagnosis for treatment. Machine learning for PD evaluation has improved, but accurate predictions, particularly for early diagnosis and progression, remain challenging. This study aims to improve the prediction of total and motor unified PD rating scale (UPDRS) scores by employing optimized ensemble learning models using the UCI Parkinson’s telemonitoring dataset. Data preprocessing involves outlier removal, normalization, and three feature selection methods: all features, Pearson correlation coefficient (PCC), and variance inflation factor (VIF) to reduce multicollinearity. Model performance is improved using minimum redundancy maximum relevance (mRMR), and robust ReliefF (RRF) feature ranking algorithms. The bagged ensemble (BE) models are optimized using Bayesian and random search hyperparameter tuning, focusing on learning rate and the number of weak learners, and are validated using 10-fold cross-validation to find the optimum configuration. The final proposed models, Bayesian-optimized BE with RRF and VIF (VIF-BOBE-RRF) and random search-optimized BE with RRF and VIF (VIF-RSOBE-RRF), are benchmarked against leading models, including multiple linear regression (MLR), Gaussian process regression (GPR), support vector regression (SVR), multi-layer perceptron (MLP), boosting ensemble, decision tree regression (DTR), and their optimized variants. For total UPDRS, VIF-BOBE-RRF achieves <span><math><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>0</mn><mo>.</mo><mn>97</mn></mrow></math></span>, RMSE = 0.0400, MAE = 0.0169, while VIF-RSOBE-RRF records <span><math><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>0</mn><mo>.</mo><mn>97</mn></mrow></math></span>, RMSE = 0.0462, MAE = 0.0170. For motor UPDRS, VIF-BOBE-RRF attains <span><math><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>0</mn><mo>.</mo><mn>96</mn></mrow></math></span>, RMSE = 0.0454, MAE = 0.0190, while VIF-RSOBE-RRF achieves <span><math><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>0</mn><mo>.</mo><mn>96</mn></mrow></math></span>, RMSE = 0.0468, MAE = 0.0171. Shapley additive explanations analysis was employed to improve interpretability and identify clinically relevant predictors such as age, DFA, and test duration. Although enhancements over baseline models are constrained, the uniformity across datasets and increased model interpretability underscore the promise of these techniques as the preliminary instruments for PD monitoring. Further evaluation in real clinical environments is advised to evaluate their practical efficacy.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100276"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680315","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":"ASDvit: Enhancing autism spectrum disorder classification using vision transformer models based on static features of facial images","authors":"Hayder Ibadi,&nbsp;Amir Lakizadeh","doi":"10.1016/j.ibmed.2025.100226","DOIUrl":"10.1016/j.ibmed.2025.100226","url":null,"abstract":"<div><div>This study embarks on an exploratory journey into autism spectrum disorder (ASD), a multifaceted neurological developmental disorder with a spectrum of manifestations. Recognizing the transformative impact of early diagnosis and tailored medical interventions on the lives of children diagnosed with ASD and their families, The intersection of early diagnosis and tailored medical intervention can substantially enhance the quality of life for children diagnosed with ASD and their families. This study embarks on an innovative approach to augmenting the diagnostic process, specifically through the analysis of static features extracted from facial photographs of autistic children. By employing Vision Transformers (ViT) enhanced with Squeeze-and-Excitation (SE) blocks, our research delves into the potential of facial features as a biomarker for distinguishing autistic children from their typically developing counterparts. The fusion of ViT with SE mechanisms aims to amplify the model's sensitivity toward the subtle yet diagnostically crucial facial cues associated with ASD. Through comprehensive experimentation on a curated dataset, categorized into “autistic” and “non-autistic” groups, our approach demonstrates remarkable proficiency in identifying ASD, thereby opening new avenues for employing facial image analysis as a scalable biomarker in ASD diagnosis.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100226"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488628","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":"DieT Transformer model with PCA-ADE integration for advanced multi-class brain tumor classification","authors":"Mohammad Amin ,&nbsp;Khalid M.O. Nahar ,&nbsp;Hasan Gharaibeh ,&nbsp;Ahmad Nasayreh ,&nbsp;Neda'a Alsalmanc ,&nbsp;Alaa Alomar ,&nbsp;Majd Malkawi ,&nbsp;Noor Alqasem ,&nbsp;Aseel Smerat ,&nbsp;Raed Abu Zitar ,&nbsp;Shawd Nusier ,&nbsp;Absalom E. Ezugwu ,&nbsp;Laith Abualigah","doi":"10.1016/j.ibmed.2024.100192","DOIUrl":"10.1016/j.ibmed.2024.100192","url":null,"abstract":"<div><div>Early and accurate diagnosis of brain tumors is crucial to improving patient outcomes and optimizing treatment strategies. Long-term brain injury results from aberrant proliferation of either malignant or nonmalignant tissues in the brain. MRIs, or magnetic resonance imaging, are one of the most used approaches for detecting brain tumors. Professionals physically evaluate people after they have had MRI filtering, the process of enhancing MRI scans for radiologist interpretation, to establish if they have a brain tumor. Because different specialists use different frames to make judgments on the same MRI image, their analyses may yield contradictory results. Furthermore, simply detecting a tumor is insufficient. Inconsistent diagnoses can lead to delays in treatment, impacting survival rates and quality of care. It is also crucial to diagnose the patient's tumor so that treatment can begin as soon as possible. In this research, we investigate the multi-class classification of brain tumors utilizing a cutting-edge methodology that includes feature extraction from pictures using the DieT Transformer model, dimensionality reduction with PCA, and feature selection using the ADE algorithm. The proposed model, known in the publication as ADE_DieT, obtained an accuracy of 96.09 %. In addition, this article analyzes the performance of various pre-trained models, including MobileNetV3, NasNet, ResNet50, VGG16, VGG19, and DeiT. The proposed approach shortens the time required for manual diagnosis by clinicians by assisting in the rapid and accurate identification of brain tumors using MRI data. In oncology, this is important since it allows for early treatment. Integrating ADE_DieT into clinical workflows can support radiologists by reducing diagnosis time and enhancing diagnostic consistency.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100192"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174360","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":"CFMKGATDDA: A new collaborative filtering and multiple kernel graph attention network-based method for predicting drug-disease associations","authors":"Van Tinh Nguyen,&nbsp;Duc Huy Vu,&nbsp;Thi Kim Phuong Pham,&nbsp;Trong Hop Dang","doi":"10.1016/j.ibmed.2024.100194","DOIUrl":"10.1016/j.ibmed.2024.100194","url":null,"abstract":"<div><div>Drug-disease association prediction is increasingly recognized as crucial for a comprehensive understanding of the functions and mechanisms of drugs. However, the process of obtaining approval for a new drug to deal with a disease is often laborious, time-consuming and expensive. As a consequence, there is a growing interest among researchers from diverse fields in developing computational methods to identify drug-disease interactions. Thus, in this work, a new CFMKGATDDA method was proposed to unveil drug-disease associations. It firstly uses a collaborative filtering algorithm for mitigating the impact sparse associations. It secondly provides a new way to fuse multiple similarities of drugs and diseases to obtain integrated similarities for drugs and diseases. Finally, it learns drugs and diseases’ embeddings by combining multiple kernels and graph attention networks to predict high quality drug-disease associations. It attains a noticeable performance of drug-disease interaction prediction with remarkable averaged AUC and AUPR values of 0.9931 and 0.9334, respectively, on the Cdataset. When comparing on the same Cdataset, it outperforms other approaches in both metrics of AUC and AUPR. Thus, it can be regarded a useful tool for revealing drug-disease associations.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100194"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174361","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":"Development of an explainable machine learning model for predicting neurological deterioration in spontaneous intracerebral hemorrhage","authors":"Ming Jie, Jonathan Yeo ,&nbsp;Chun Peng Goh ,&nbsp;Christine Xia Wu ,&nbsp;Francis Phng ,&nbsp;Ping Yong ,&nbsp;Shiong Wen Low","doi":"10.1016/j.ibmed.2025.100237","DOIUrl":"10.1016/j.ibmed.2025.100237","url":null,"abstract":"<div><h3>Background</h3><div>Intracerebral hemorrhage (ICH) is a severe form of stroke associated with high morbidity and mortality. Early prediction of neurological deterioration (ND)—defined as a decline of at least 2 points on the Glasgow Coma Scale (GCS) within 48 h of admission or mortality at discharge—is essential for timely intervention and improved outcomes.</div></div><div><h3>Methods</h3><div>We developed an explainable machine learning model to predict ND using clinical, laboratory, and radiological data extracted from electronic medical records (EMR) of a retrospective cohort of 491 ICH patients, with ND observed in 52.3 % of cases. Multiple machine learning algorithms—including random forests, extra trees, and CatBoost—were trained, and model performance was evaluated using metrics such as the area under the receiver operating characteristic curve (AUC-ROC) and F1-score. Shapley Additive Explanations (SHAP) were employed to enhance interpretability.</div></div><div><h3>Results</h3><div>The final model, a blended ensemble, achieved an AUC-ROC of 0.8743, an F1-score of 0.8077, and a sensitivity of 0.8182 on the test set. Key predictors included initial GCS, hematoma volume, age, and the presence of intraventricular hemorrhage. SHAP analysis provided insights into the relative contributions of these predictors, reinforcing the model's clinical relevance.</div></div><div><h3>Conclusions</h3><div>Our model demonstrates promising predictive performance, suggesting its potential utility for early risk stratification and guiding interventions in ICH management. Further validation in diverse clinical settings is warranted.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100237"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725919","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}