Pub Date : 2025-01-01Epub Date: 2025-01-07DOI: 10.1016/j.ibmed.2024.100197
Matteo Magnini , Gianluca Aguzzi , Sara Montagna
Medical chatbots are becoming essential components of telemedicine applications as tools to assist patients in the self-management of their conditions. This trend is particularly driven by advancements in natural language processing techniques with pre-trained language models (LMs). However, the integration of LMs into clinical environments faces challenges related to reliability and privacy concerns.
This study seeks to address these issues by exploiting a privacy by design architectural solution that utilises the fully local deployment of open-source LMs. Specifically, to mitigate any risk of information leakage, we focus on evaluating the performance of open-source language models (SLMs) that can be deployed on personal devices, such as smartphones or laptops, without stringent hardware requirements.
We assess the effectiveness of this solution adopting hypertension management as a case study. Models are evaluated across various tasks, including intent recognition and empathetic conversation, using Gemini Pro 1.5 as a benchmark. The results indicate that, for certain tasks such as intent recognition, Gemini outperforms other models. However, by employing the “large language model (LLM) as a judge” approach for semantic evaluation of response correctness, we found several models that demonstrate a close alignment with the ground truth. In conclusion, this study highlights the potential of locally deployed SLMs as components of medical chatbots, while addressing critical concerns related to privacy and reliability.
医疗聊天机器人正在成为远程医疗应用的重要组成部分，作为帮助患者自我管理病情的工具。这一趋势尤其受到自然语言处理技术与预训练语言模型（LMs）的进步的推动。然而，将LMs集成到临床环境中面临着与可靠性和隐私问题相关的挑战。本研究试图通过利用开源LMs的完全本地部署来利用隐私设计架构解决方案来解决这些问题。具体来说，为了减少信息泄露的风险，我们着重于评估可以部署在个人设备（如智能手机或笔记本电脑）上的开源语言模型（slm）的性能，而不需要严格的硬件要求。我们以高血压管理为例来评估这种解决方案的有效性。模型在各种任务中进行评估，包括意图识别和移情对话，使用Gemini Pro 1.5作为基准。结果表明，对于某些任务，如意图识别，Gemini优于其他模型。然而，通过采用“大型语言模型（LLM）作为判断”的方法来对响应正确性进行语义评估，我们发现了几个与基本事实密切一致的模型。总之，本研究强调了本地部署的slm作为医疗聊天机器人组件的潜力，同时解决了与隐私和可靠性相关的关键问题。
{"title":"Open-source small language models for personal medical assistant chatbots","authors":"Matteo Magnini , Gianluca Aguzzi , Sara Montagna","doi":"10.1016/j.ibmed.2024.100197","DOIUrl":"10.1016/j.ibmed.2024.100197","url":null,"abstract":"<div><div>Medical chatbots are becoming essential components of telemedicine applications as tools to assist patients in the self-management of their conditions. This trend is particularly driven by advancements in natural language processing techniques with pre-trained language models (LMs). However, the integration of LMs into clinical environments faces challenges related to reliability and privacy concerns.</div><div>This study seeks to address these issues by exploiting a <em>privacy by design</em> architectural solution that utilises the fully local deployment of open-source LMs. Specifically, to mitigate any risk of information leakage, we focus on evaluating the performance of open-source language models (SLMs) that can be deployed on personal devices, such as smartphones or laptops, without stringent hardware requirements.</div><div>We assess the effectiveness of this solution adopting hypertension management as a case study. Models are evaluated across various tasks, including intent recognition and empathetic conversation, using Gemini Pro 1.5 as a benchmark. The results indicate that, for certain tasks such as intent recognition, Gemini outperforms other models. However, by employing the “large language model (LLM) as a judge” approach for semantic evaluation of response correctness, we found several models that demonstrate a close alignment with the ground truth. In conclusion, this study highlights the potential of locally deployed SLMs as components of medical chatbots, while addressing critical concerns related to privacy and reliability.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100197"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143173635","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}
Pub Date : 2025-01-01Epub Date: 2025-01-24DOI: 10.1016/j.ibmed.2025.100207
Salam Bani Hani , Muayyad Ahmad
{"title":"Using big data to predict young adult ischemic vs. non-ischemic heart disease risk factors: An artificial intelligence based model","authors":"Salam Bani Hani , Muayyad Ahmad","doi":"10.1016/j.ibmed.2025.100207","DOIUrl":"10.1016/j.ibmed.2025.100207","url":null,"abstract":"","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100207"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143173634","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}
Pub Date : 2025-01-01Epub Date: 2025-11-12DOI: 10.1016/j.ibmed.2025.100312
Anas Ali Alhur , Jamilu Sani , Mohamed Mustaf Ahmed
Background
Chronic obstructive pulmonary disease (COPD) is a leading cause of hospitalization and mortality globally, placing a substantial burden on healthcare systems. In Saudi Arabia, COPD admissions are rising due to demographic shifts and environmental exposures. Accurate prediction of COPD-related hospitalizations is essential for timely intervention and resource planning. This study applied machine learning (ML) techniques to predict COPD admissions using routine hospital data from major healthcare facilities in Riyadh.
Methods
A cross-sectional analysis was conducted using 41,544 patient admission records from eight major hospitals in Saudi Arabia between 2022 and 2024. The dataset included demographic, clinical, and healthcare utilization variables. Several ML classifiers: Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, Gradient Boosting, and XGBoost, were developed and evaluated. The primary outcome was inpatient admission for COPD. Model performance was assessed using accuracy, precision, recall, F1-score, AUROC, and confusion matrices. SHapley Additive exPlanations (SHAP) were used to interpret model outputs and rank feature importance.
Results
The Random Forest model outperformed other classifiers with an accuracy of 0.73, precision of 0.70, recall of 0.79, F1-score of 0.74, and AUROC of 0.79. Key predictors identified by SHAP analysis included hospital name, admission count, comorbid conditions, and disease severity. Features such as gender and seasonal variation showed minimal influence on prediction outcomes. SHAP visualizations provided interpretable insights into individual-level risk contributions.
Conclusion
Machine learning models, particularly Random Forest, demonstrated moderate but promising capacity for predicting COPD admissions using routine hospital data. Model interpretability through SHAP enhances clinical relevance and supports early identification of high-risk patients. Integration of these tools into hospital systems may facilitate proactive care and improve resource allocation for respiratory conditions.
{"title":"Predicting COPD admissions using machine learning and SHAP: An exploratory multi-hospital study in Riyadh, Saudi Arabia","authors":"Anas Ali Alhur , Jamilu Sani , Mohamed Mustaf Ahmed","doi":"10.1016/j.ibmed.2025.100312","DOIUrl":"10.1016/j.ibmed.2025.100312","url":null,"abstract":"<div><h3>Background</h3><div>Chronic obstructive pulmonary disease (COPD) is a leading cause of hospitalization and mortality globally, placing a substantial burden on healthcare systems. In Saudi Arabia, COPD admissions are rising due to demographic shifts and environmental exposures. Accurate prediction of COPD-related hospitalizations is essential for timely intervention and resource planning. This study applied machine learning (ML) techniques to predict COPD admissions using routine hospital data from major healthcare facilities in Riyadh.</div></div><div><h3>Methods</h3><div>A cross-sectional analysis was conducted using 41,544 patient admission records from eight major hospitals in Saudi Arabia between 2022 and 2024. The dataset included demographic, clinical, and healthcare utilization variables. Several ML classifiers: Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, Gradient Boosting, and XGBoost, were developed and evaluated. The primary outcome was inpatient admission for COPD. Model performance was assessed using accuracy, precision, recall, F1-score, AUROC, and confusion matrices. SHapley Additive exPlanations (SHAP) were used to interpret model outputs and rank feature importance.</div></div><div><h3>Results</h3><div>The Random Forest model outperformed other classifiers with an accuracy of 0.73, precision of 0.70, recall of 0.79, F1-score of 0.74, and AUROC of 0.79. Key predictors identified by SHAP analysis included hospital name, admission count, comorbid conditions, and disease severity. Features such as gender and seasonal variation showed minimal influence on prediction outcomes. SHAP visualizations provided interpretable insights into individual-level risk contributions.</div></div><div><h3>Conclusion</h3><div>Machine learning models, particularly Random Forest, demonstrated moderate but promising capacity for predicting COPD admissions using routine hospital data. Model interpretability through SHAP enhances clinical relevance and supports early identification of high-risk patients. Integration of these tools into hospital systems may facilitate proactive care and improve resource allocation for respiratory conditions.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100312"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519505","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}
Pub Date : 2025-01-01Epub Date: 2025-11-01DOI: 10.1016/j.ibmed.2025.100308
Seyyed Ali Zendehbad , Elias Mazrooei Rad , Shahryar Salmani Bajestani
Swarm Intelligence (SI), a specialized branch of Artificial Intelligence (AI), is founded on the collective behaviors observed in biological systems, such as those of ants, bees, and bird flocks. This bio-inspired approach enables SI to develop computational algorithms that tackle complex problems, which traditional methods often struggle to address. Over the past few decades, SI has gained substantial traction in biomedical engineering due to its capacity to address multifaceted issues with higher adaptability and efficiency. This review presents key advancements in SI applications across three primary areas: neurorehabilitation, Alzheimer's Disease Diagnosis (ADD), and medical image processing. In neurorehabilitation, SI has played a pivotal role in improving the precision and adaptability of devices such as exoskeletons and neuroprostheses, enhancing motor function recovery for patients. Similarly, in ADD, SI algorithms have shown significant promise in analyzing neuroimaging and neurophysiological data, increasing diagnostic accuracy and enabling earlier intervention. Furthermore, in medical image processing, SI techniques have been effectively applied to tasks such as image segmentation, tumor detection, feature extraction, and artifact reduction, particularly in modalities like Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and ultrasound imaging (such as Sonography). Based on the literature analyzed, SI methods have demonstrated consistent strengths in global optimization, adaptability to noisy data, and robustness in feature selection tasks when compared to traditional machine learning techniques. However, SI still faces challenges, especially regarding computational complexity, model interpretability, and limited clinical translation. Overcoming these hurdles is crucial for the full-scale adoption of this technology in clinical settings. This review not only highlights the progress in these areas but also synthesizes current limitations and future directions to guide the effective integration of SI into real-world biomedical applications.
{"title":"Swarm intelligence in biomedical engineering","authors":"Seyyed Ali Zendehbad , Elias Mazrooei Rad , Shahryar Salmani Bajestani","doi":"10.1016/j.ibmed.2025.100308","DOIUrl":"10.1016/j.ibmed.2025.100308","url":null,"abstract":"<div><div>Swarm Intelligence (SI), a specialized branch of Artificial Intelligence (AI), is founded on the collective behaviors observed in biological systems, such as those of ants, bees, and bird flocks. This bio-inspired approach enables SI to develop computational algorithms that tackle complex problems, which traditional methods often struggle to address. Over the past few decades, SI has gained substantial traction in biomedical engineering due to its capacity to address multifaceted issues with higher adaptability and efficiency. This review presents key advancements in SI applications across three primary areas: neurorehabilitation, Alzheimer's Disease Diagnosis (ADD), and medical image processing. In neurorehabilitation, SI has played a pivotal role in improving the precision and adaptability of devices such as exoskeletons and neuroprostheses, enhancing motor function recovery for patients. Similarly, in ADD, SI algorithms have shown significant promise in analyzing neuroimaging and neurophysiological data, increasing diagnostic accuracy and enabling earlier intervention. Furthermore, in medical image processing, SI techniques have been effectively applied to tasks such as image segmentation, tumor detection, feature extraction, and artifact reduction, particularly in modalities like Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and ultrasound imaging (such as Sonography). Based on the literature analyzed, SI methods have demonstrated consistent strengths in global optimization, adaptability to noisy data, and robustness in feature selection tasks when compared to traditional machine learning techniques. However, SI still faces challenges, especially regarding computational complexity, model interpretability, and limited clinical translation. Overcoming these hurdles is crucial for the full-scale adoption of this technology in clinical settings. This review not only highlights the progress in these areas but also synthesizes current limitations and future directions to guide the effective integration of SI into real-world biomedical applications.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100308"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465892","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}
Pub Date : 2025-01-01Epub Date: 2025-03-28DOI: 10.1016/j.ibmed.2025.100243
Fahima Hossain, Rajib Kumar Halder, Mohammed Nasir Uddin
Alzheimer's disease (AD) is a degenerative neurological condition that impairs cognitive functioning. Early detection is critical for slowing disease progression and limiting brain damage. Although machine learning and deep learning models help identify Alzheimer's disease, their accuracy and efficiency are widely questioned. This study provides an integrated system for classifying four AD phases from 6400 MRI scans using pre-trained neural networks and machine learning classifiers. Preprocessing steps include noise removal, image enhancement (AGCWD, Bilateral Filter), and segmentation. Intensity normalization and data augmentation methods are applied to improve model generalization. Two models are developed: the first employs pre-trained neural net-works (VGG16, VGG19, DenseNet201, ResNet50, EfficientNetV7, InceptionV3, InceptionResNetV2, and MobileNet) for both feature extraction and classification. In contrast, the second integrates features from these networks with machine learning classifiers (XGBoost, Random Forest, SVM, KNN, Gradient Boosting, AdaBoost, Decision Tree, Linear Discriminant Analysis, Logistic Regression, and Multilayer Perceptron). The second model incorporates an adaptive error minimization sys-tem for enhanced accuracy. VGG16 achieved the highest accuracy (99.61 % training and 97.94 % testing), whereas VGG19+MLP with adaptive error minimization achieved 97.08 %, exhibiting superior AD classification ability.
{"title":"An integrated machine learning based adaptive error minimization framework for Alzheimer's stage identification","authors":"Fahima Hossain, Rajib Kumar Halder, Mohammed Nasir Uddin","doi":"10.1016/j.ibmed.2025.100243","DOIUrl":"10.1016/j.ibmed.2025.100243","url":null,"abstract":"<div><div>Alzheimer's disease (AD) is a degenerative neurological condition that impairs cognitive functioning. Early detection is critical for slowing disease progression and limiting brain damage. Although machine learning and deep learning models help identify Alzheimer's disease, their accuracy and efficiency are widely questioned. This study provides an integrated system for classifying four AD phases from 6400 MRI scans using pre-trained neural networks and machine learning classifiers. Preprocessing steps include noise removal, image enhancement (AGCWD, Bilateral Filter), and segmentation. Intensity normalization and data augmentation methods are applied to improve model generalization. Two models are developed: the first employs pre-trained neural net-works (VGG16, VGG19, DenseNet201, ResNet50, EfficientNetV7, InceptionV3, InceptionResNetV2, and MobileNet) for both feature extraction and classification. In contrast, the second integrates features from these networks with machine learning classifiers (XGBoost, Random Forest, SVM, KNN, Gradient Boosting, AdaBoost, Decision Tree, Linear Discriminant Analysis, Logistic Regression, and Multilayer Perceptron). The second model incorporates an adaptive error minimization sys-tem for enhanced accuracy. VGG16 achieved the highest accuracy (99.61 % training and 97.94 % testing), whereas VGG19+MLP with adaptive error minimization achieved 97.08 %, exhibiting superior AD classification ability.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100243"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807121","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}
Pub Date : 2025-01-01Epub Date: 2025-05-26DOI: 10.1016/j.ibmed.2025.100257
Anwar Jimi , Nabila Zrira , Oumaima Guendoul , Ibtissam Benmiloud , Haris Ahmad Khan , Shah Nawaz
One of the most important tasks in computer-aided diagnostics is the automatic segmentation of skin lesions, which plays an essential role in the early diagnosis and treatment of skin cancer. In recent years, the Convolutional Neural Network (CNN) has largely replaced other traditional methods for segmenting skin lesions. However, due to insufficient information and unclear lesion region segmentation, skin lesion image segmentation still has challenges. In this paper, we propose a novel deep medical image segmentation approach named “ESC-UNET” which combines the advantages of CNN and Transformer to effectively leverage local information and long-range dependencies to enhance medical image segmentation. In terms of the local information, we use a CNN-based encoder and decoder framework. The CNN branch mines local information from medical images using the locality of convolution processes and the pre-trained EfficientNetB5 network. As for the long-range dependencies, we build a Transformer branch that emphasizes the global context. In addition, we employ Atrous Spatial Pyramid Pooling (ASPP) to gather network-wide relevant information. The Convolution Block Attention Module (CBAM) is added to the model to promote effective features and suppress ineffective features in segmentation. We have evaluated our network using the ISIC 2016, ISIC 2017, and ISIC 2018 datasets. The results demonstrate the efficiency of the proposed model in segmenting skin lesions.
{"title":"ESC-UNET: A hybrid CNN and Swin Transformers for skin lesion segmentation","authors":"Anwar Jimi , Nabila Zrira , Oumaima Guendoul , Ibtissam Benmiloud , Haris Ahmad Khan , Shah Nawaz","doi":"10.1016/j.ibmed.2025.100257","DOIUrl":"10.1016/j.ibmed.2025.100257","url":null,"abstract":"<div><div>One of the most important tasks in computer-aided diagnostics is the automatic segmentation of skin lesions, which plays an essential role in the early diagnosis and treatment of skin cancer. In recent years, the Convolutional Neural Network (CNN) has largely replaced other traditional methods for segmenting skin lesions. However, due to insufficient information and unclear lesion region segmentation, skin lesion image segmentation still has challenges. In this paper, we propose a novel deep medical image segmentation approach named “ESC-UNET” which combines the advantages of CNN and Transformer to effectively leverage local information and long-range dependencies to enhance medical image segmentation. In terms of the local information, we use a CNN-based encoder and decoder framework. The CNN branch mines local information from medical images using the locality of convolution processes and the pre-trained EfficientNetB5 network. As for the long-range dependencies, we build a Transformer branch that emphasizes the global context. In addition, we employ Atrous Spatial Pyramid Pooling (ASPP) to gather network-wide relevant information. The Convolution Block Attention Module (CBAM) is added to the model to promote effective features and suppress ineffective features in segmentation. We have evaluated our network using the ISIC 2016, ISIC 2017, and ISIC 2018 datasets. The results demonstrate the efficiency of the proposed model in segmenting skin lesions.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100257"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144168078","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}
Pub Date : 2025-01-01Epub Date: 2025-03-27DOI: 10.1016/j.ibmed.2025.100242
Albert C. Yang , Wei-Ming Ma , Dung-Hung Chiang , Yi-Ze Liao , Hsien-Yung Lai , Shu-Chuan Lin , Mei-Chin Liu , Kai-Ting Wen , Tzong-Huei Lin , Wen-Xiang Tsai , Jun-Ding Zhu , Ting-Yu Chen , Hung-Fu Lee , Pei-Hung Liao , Huey-Wen Yien , Chien-Ying Wang
We aimed to develop an early warning system to predict sepsis based solely on single time-point and non-invasive vital signs, and to evaluate its correlation with related biomarkers, namely C-reactive protein (CRP) and Procalcitonin (PCT). We utilized retrospective data from Physionet and four medical centers in Taiwan, encompassing a total of 46,184 Intensive Care Unit (ICU) patients, to develop and validate a machine learning algorithm based on XGBoost for predicting sepsis. The model was specifically designed to use non-invasive vital signs captured at a single time point, The correlation between sepsis AI prediction model and levels of CRP and PCT was evaluated. The developed model demonstrated balanced performance across various datasets, with an average recall of 0.908 and precision of 0.577. The model's performance was further validated by the independent dataset from Cheng-Hsin General Hospital (recall: 0.986, precision: 0.585). Temperature, systolic blood pressure, and respiration rate were the top contributing predictors in the model. A significant correlation was observed between the model's sepsis predictions and elevated CRP levels, while PCT showed a less consistent pattern. Our approach, combining AI algorithms with vital sign data and its clinical relevance to CRP level, offers a more precise and timely sepsis detection, with the potential to improve care in emergency and critical care settings.
{"title":"Early prediction of sepsis using an XGBoost model with single time-point non-invasive vital signs and its correlation with C-reactive protein and procalcitonin: A multi-center study","authors":"Albert C. Yang , Wei-Ming Ma , Dung-Hung Chiang , Yi-Ze Liao , Hsien-Yung Lai , Shu-Chuan Lin , Mei-Chin Liu , Kai-Ting Wen , Tzong-Huei Lin , Wen-Xiang Tsai , Jun-Ding Zhu , Ting-Yu Chen , Hung-Fu Lee , Pei-Hung Liao , Huey-Wen Yien , Chien-Ying Wang","doi":"10.1016/j.ibmed.2025.100242","DOIUrl":"10.1016/j.ibmed.2025.100242","url":null,"abstract":"<div><div>We aimed to develop an early warning system to predict sepsis based solely on single time-point and non-invasive vital signs, and to evaluate its correlation with related biomarkers, namely C-reactive protein (CRP) and Procalcitonin (PCT). We utilized retrospective data from Physionet and four medical centers in Taiwan, encompassing a total of 46,184 Intensive Care Unit (ICU) patients, to develop and validate a machine learning algorithm based on XGBoost for predicting sepsis. The model was specifically designed to use non-invasive vital signs captured at a single time point, The correlation between sepsis AI prediction model and levels of CRP and PCT was evaluated. The developed model demonstrated balanced performance across various datasets, with an average recall of 0.908 and precision of 0.577. The model's performance was further validated by the independent dataset from Cheng-Hsin General Hospital (recall: 0.986, precision: 0.585). Temperature, systolic blood pressure, and respiration rate were the top contributing predictors in the model. A significant correlation was observed between the model's sepsis predictions and elevated CRP levels, while PCT showed a less consistent pattern. Our approach, combining AI algorithms with vital sign data and its clinical relevance to CRP level, offers a more precise and timely sepsis detection, with the potential to improve care in emergency and critical care settings.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100242"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747482","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}
Pub Date : 2025-01-01Epub Date: 2025-04-25DOI: 10.1016/j.ibmed.2025.100248
ArunaDevi Karuppasamy , Hamza zidoum , Majda Said Sultan Al-Rashdi , Maiya Al-Bahri
The Deep Learning (DL) has demonstrated a significant impact on a various pattern recognition applications, resulting in significant advancements in areas such as visual recognition, autonomous cars, language processing, and healthcare. Nowadays, deep learning was widely applied on the medical images to identify the diseases efficiently. Still, the use of applications in clinical settings is now limited to a small number. The main factors to this might be due to an inadequate annotated data, noises in the images and challenges related to collecting data. Our research proposed a convolutional autoencoder to classify the breast cancer tumors, using the Sultan Qaboos University Hospital(SQUH) and BreakHis datasets. The proposed model named Convolutional AutoEncoder with modified Loss Function (CAE-LF) achieved a good performance, by attaining a F1-score of 0.90, recall of 0.89, and accuracy of 91%. The results obtained are comparable to those obtained in earlier researches. Additional analyses conducted on the SQUH dataset demonstrate that it yields a good performance with an F1-score of 0.91, 0.93, 0.92, and 0.93 for 4x, 10x, 20x, and 40x magnifications, respectively. Our study highlights the potential of deep learning in analyzing medical images to classify breast tumors.
{"title":"Optimizing breast cancer diagnosis with convolutional autoencoders: Enhanced performance through modified loss functions","authors":"ArunaDevi Karuppasamy , Hamza zidoum , Majda Said Sultan Al-Rashdi , Maiya Al-Bahri","doi":"10.1016/j.ibmed.2025.100248","DOIUrl":"10.1016/j.ibmed.2025.100248","url":null,"abstract":"<div><div>The Deep Learning (DL) has demonstrated a significant impact on a various pattern recognition applications, resulting in significant advancements in areas such as visual recognition, autonomous cars, language processing, and healthcare. Nowadays, deep learning was widely applied on the medical images to identify the diseases efficiently. Still, the use of applications in clinical settings is now limited to a small number. The main factors to this might be due to an inadequate annotated data, noises in the images and challenges related to collecting data. Our research proposed a convolutional autoencoder to classify the breast cancer tumors, using the Sultan Qaboos University Hospital(SQUH) and BreakHis datasets. The proposed model named Convolutional AutoEncoder with modified Loss Function (CAE-LF) achieved a good performance, by attaining a F1-score of 0.90, recall of 0.89, and accuracy of 91%. The results obtained are comparable to those obtained in earlier researches. Additional analyses conducted on the SQUH dataset demonstrate that it yields a good performance with an F1-score of 0.91, 0.93, 0.92, and 0.93 for 4x, 10x, 20x, and 40x magnifications, respectively. Our study highlights the potential of deep learning in analyzing medical images to classify breast tumors.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100248"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887937","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}
This study was conducted due to the lack of applications that can assist people intreating common external wounds. Therefore, we proposed the application of image-based detection which takes external wounds and identifies them using Artificial Intelligence namely LukaKu. In addition to detecting the type of wound that occurs, the application is expected to be able to produce first aid and medicine for each existing external wound label. The model used is YOLOv5 with various versions, namely YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. By calculating the validation data, each version has its own precision, recall, f1-score, and Mean Average Precision (mAP) values which are the comparison factors in determining the best model version, where YOLOv5l with mAP value of 0.785 is the best result and YOLOv5n with mAP value of 0.588 is the result with the lowest value. In the model development process, datasets of external injuries are needed to be used during the training process and test datasets for each existing model version. After each version of the model has been successfully built and analysed, the model with the best value is implemented in the mobile application, making it easier for users to access.
这项研究是由于缺乏应用程序，可以帮助人们治疗常见的外部伤口。因此，我们提出了基于图像的检测应用，该检测采用人工智能即LukaKu来识别外部伤口。除了检测发生的伤口类型之外，该应用程序预计能够为每个现有的外部伤口标签生产急救和药物。型号为YOLOv5，有YOLOv5n、YOLOv5s、YOLOv5m、YOLOv5l、YOLOv5x等多个版本。通过计算验证数据，每个版本都有自己的精度、召回率、f1-score和Mean Average precision （mAP）值，这些值是确定最佳模型版本的比较因素，其中mAP值为0.785的YOLOv5l为最佳结果，mAP值为0.588的YOLOv5n为最低结果。在模型开发过程中，在训练过程中需要使用外伤性数据集，在现有的各个模型版本中需要使用测试数据集。在成功构建和分析了每个版本的模型后，将最有价值的模型实现在移动应用程序中，使用户更容易访问。
{"title":"A mobile application LukaKu as a tool for detecting external wounds with artificial intelligence","authors":"Dessy Novita , Herika Hayurani , Eva Krishna Sutedja , Firdaus Ryan Pratomo , Achmad Dino Saputra , Zahra Ramadhanti , Nuryadin Abutani , Muhammad Rafi Triandi , Aldin Mubarok Guferol , Anindya Apriliyanti Pravitasari , Fajar Wira Adikusuma , Atiek Rostika Noviyanti","doi":"10.1016/j.ibmed.2025.100200","DOIUrl":"10.1016/j.ibmed.2025.100200","url":null,"abstract":"<div><div>This study was conducted due to the lack of applications that can assist people intreating common external wounds. Therefore, we proposed the application of image-based detection which takes external wounds and identifies them using Artificial Intelligence namely LukaKu. In addition to detecting the type of wound that occurs, the application is expected to be able to produce first aid and medicine for each existing external wound label. The model used is YOLOv5 with various versions, namely YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. By calculating the validation data, each version has its own precision, recall, f1-score, and Mean Average Precision (mAP) values which are the comparison factors in determining the best model version, where YOLOv5l with mAP value of 0.785 is the best result and YOLOv5n with mAP value of 0.588 is the result with the lowest value. In the model development process, datasets of external injuries are needed to be used during the training process and test datasets for each existing model version. After each version of the model has been successfully built and analysed, the model with the best value is implemented in the mobile application, making it easier for users to access.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100200"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174331","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}
Pub Date : 2025-01-01Epub Date: 2025-06-10DOI: 10.1016/j.ibmed.2025.100267
Shuaibu Saidu Musa , Adamu Muhammad Ibrahim , Muhammad Yasir Alhassan , Abubakar Hafs Musa , Abdulrahman Garba Jibo , Auwal Rabiu Auwal , Olalekan John Okesanya , Zhinya Kawa Othman , Muhammad Sadiq Abubakar , Mohamed Mustaf Ahmed , Carina Joane V. Barroso , Abraham Fessehaye Sium , Manuel B. Garcia , James Brian Flores , Adamu Safiyanu Maikifi , M.B.N. Kouwenhoven , Don Eliseo Lucero-Prisno
The fusion of molecular-scale engineering in nanotechnology with machine learning (ML) analytics is reshaping the field of precision medicine. Nanoparticles enable ultrasensitive diagnostics, targeted drug and gene delivery, and high-resolution imaging, whereas ML models mine vast multimodal datasets to optimize nanoparticle design, enhance predictive accuracy, and personalize treatment in real-time. Recent breakthroughs include ML-guided formulations of lipid, polymeric, and inorganic carriers that cross biological barriers; AI-enhanced nanosensors that flag early disease from breath, sweat, or blood; and nanotheranostic agents that simultaneously track and treat tumors. Comparative insights into Retrieval-Augmented Generation and supervised learning pipelines reveal distinct advantages for nanodevice engineering across diverse data environments. An expanded focus on explainable AI tools, such as SHAP, LIME, Grad-CAM, and Integrated Gradients, highlights their role in enhancing transparency, trust, and interpretability in nano-enabled clinical decisions. A structured narrative review method was applied, and key ML model performances were synthesized to strengthen analytical clarity. Emerging biodegradable nanomaterials, autonomous micro-nanorobots, and hybrid lab-on-chip systems promise faster point-of-care decisions but raise pressing questions about data integrity, interpretability, scalability, regulation, ethics, and equitable access. Addressing these hurdles will require robust data standards, privacy safeguards, interdisciplinary R&D networks, and flexible approval pathways to translate bench advances into bedside benefits for patients. This review synthesizes the current landscape, critical challenges, and future directions at the intersection of nanotechnology and ML in precision medicine.
{"title":"Nanotechnology and machine learning: a promising confluence for the advancement of precision medicine","authors":"Shuaibu Saidu Musa , Adamu Muhammad Ibrahim , Muhammad Yasir Alhassan , Abubakar Hafs Musa , Abdulrahman Garba Jibo , Auwal Rabiu Auwal , Olalekan John Okesanya , Zhinya Kawa Othman , Muhammad Sadiq Abubakar , Mohamed Mustaf Ahmed , Carina Joane V. Barroso , Abraham Fessehaye Sium , Manuel B. Garcia , James Brian Flores , Adamu Safiyanu Maikifi , M.B.N. Kouwenhoven , Don Eliseo Lucero-Prisno","doi":"10.1016/j.ibmed.2025.100267","DOIUrl":"10.1016/j.ibmed.2025.100267","url":null,"abstract":"<div><div>The fusion of molecular-scale engineering in nanotechnology with machine learning (ML) analytics is reshaping the field of precision medicine. Nanoparticles enable ultrasensitive diagnostics, targeted drug and gene delivery, and high-resolution imaging, whereas ML models mine vast multimodal datasets to optimize nanoparticle design, enhance predictive accuracy, and personalize treatment in real-time. Recent breakthroughs include ML-guided formulations of lipid, polymeric, and inorganic carriers that cross biological barriers; AI-enhanced nanosensors that flag early disease from breath, sweat, or blood; and nanotheranostic agents that simultaneously track and treat tumors. Comparative insights into Retrieval-Augmented Generation and supervised learning pipelines reveal distinct advantages for nanodevice engineering across diverse data environments. An expanded focus on explainable AI tools, such as SHAP, LIME, Grad-CAM, and Integrated Gradients, highlights their role in enhancing transparency, trust, and interpretability in nano-enabled clinical decisions. A structured narrative review method was applied, and key ML model performances were synthesized to strengthen analytical clarity. Emerging biodegradable nanomaterials, autonomous micro-nanorobots, and hybrid lab-on-chip systems promise faster point-of-care decisions but raise pressing questions about data integrity, interpretability, scalability, regulation, ethics, and equitable access. Addressing these hurdles will require robust data standards, privacy safeguards, interdisciplinary R&D networks, and flexible approval pathways to translate bench advances into bedside benefits for patients. This review synthesizes the current landscape, critical challenges, and future directions at the intersection of nanotechnology and ML in precision medicine.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100267"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271155","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号