Intelligence-based medicine最新文献

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Using big data to predict young adult ischemic vs. non-ischemic heart disease risk factors: An artificial intelligence based model

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100207

Salam Bani Hani , Muayyad Ahmad

引用次数: 0

Open-source small language models for personal medical assistant chatbots

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 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.

{"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}

引用次数: 0

Development and validation of a moderate aortic stenosis disease progression model

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100201

Miguel R. Sotelo , Paul Nona , Loren Wagner , Chris Rogers , Julian Booker , Efstathia Andrikopoulou

Background

Understanding the multifactorial determinants of rapid progression in patients with aortic stenosis (AS) remains limited. We aimed to develop and validate a machine learning model (ML) for predicting rapid progression from moderate to severe AS within one year.

Methods

8746 patients were identified with moderate AS across seven healthcare organizations. Three ML models were trained using demographic, and echocardiographic variables, namely Random Forest, XGBoost and causal discovery-logistic regression. An ensemble model was developed integrating the aforementioned three. A total of 3355 patients formed the training and internal validation cohort. External validation was performed on 171 patients from one institution.

Results

An ensemble model was selected due to its superior F1 score and precision in internal validation (0.382 and 0.301, respectively). Its performance on the external validation cohort was modest (F1 score = 0.626, precision = 0.532).

Conclusion

An ensemble model comprising only demographic and echocardiographic variables was shown to have modest performance in predicting one-year progression from moderate to severe AS. Further validation in larger populations, along with integration of comprehensive clinical data, is crucial for broader applicability.

{"title":"Development and validation of a moderate aortic stenosis disease progression model","authors":"Miguel R. Sotelo , Paul Nona , Loren Wagner , Chris Rogers , Julian Booker , Efstathia Andrikopoulou","doi":"10.1016/j.ibmed.2025.100201","DOIUrl":"10.1016/j.ibmed.2025.100201","url":null,"abstract":"<div><h3>Background</h3><div>Understanding the multifactorial determinants of rapid progression in patients with aortic stenosis (AS) remains limited. We aimed to develop and validate a machine learning model (ML) for predicting rapid progression from moderate to severe AS within one year.</div></div><div><h3>Methods</h3><div>8746 patients were identified with moderate AS across seven healthcare organizations. Three ML models were trained using demographic, and echocardiographic variables, namely Random Forest, XGBoost and causal discovery-logistic regression. An ensemble model was developed integrating the aforementioned three. A total of 3355 patients formed the training and internal validation cohort. External validation was performed on 171 patients from one institution.</div></div><div><h3>Results</h3><div>An ensemble model was selected due to its superior F1 score and precision in internal validation (0.382 and 0.301, respectively). Its performance on the external validation cohort was modest (F1 score = 0.626, precision = 0.532).</div></div><div><h3>Conclusion</h3><div>An ensemble model comprising only demographic and echocardiographic variables was shown to have modest performance in predicting one-year progression from moderate to severe AS. Further validation in larger populations, along with integration of comprehensive clinical data, is crucial for broader applicability.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100201"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174355","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}

引用次数: 0

Improving CNN interpretability and evaluation via alternating training and regularization in chest CT scans

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100211

Rodrigo Ramos-Díaz , Jesús García-Ramírez , Jimena Olveres , Boris Escalante-Ramírez

Interpretable machine learning is an emerging trend that holds significant importance, considering the growing impact of machine learning systems on society and human lives. Many interpretability methods are applied in CNN after training to provide deeper insights into the outcomes, but only a few have tried to promote interpretability during training. The aim of this experimental study is to investigate the interpretability of CNN. This research was applied to chest computed tomography scans, as understanding CNN predictions has particular importance in the automatic classification of medical images. We attempted to implement a CNN technique aimed at improving interpretability by relating filters in the last convolutional to specific output classes. Variations of such a technique were explored and assessed using chest CT images for classification based on the presence of lungs and lesions. A search was conducted to optimize the specific hyper-parameters necessary for the evaluated strategies. A novel strategy is proposed employing transfer learning and regularization. Models obtained with this strategy and the optimized hyperparameters were statistically compared to standard models, demonstrating greater interpretability without a significant loss in predictive accuracy. We achieved CNN models with improved interpretability, which is crucial for the development of more explainable and reliable AI systems.

{"title":"Improving CNN interpretability and evaluation via alternating training and regularization in chest CT scans","authors":"Rodrigo Ramos-Díaz , Jesús García-Ramírez , Jimena Olveres , Boris Escalante-Ramírez","doi":"10.1016/j.ibmed.2025.100211","DOIUrl":"10.1016/j.ibmed.2025.100211","url":null,"abstract":"<div><div>Interpretable machine learning is an emerging trend that holds significant importance, considering the growing impact of machine learning systems on society and human lives. Many interpretability methods are applied in CNN after training to provide deeper insights into the outcomes, but only a few have tried to promote interpretability during training. The aim of this experimental study is to investigate the interpretability of CNN. This research was applied to chest computed tomography scans, as understanding CNN predictions has particular importance in the automatic classification of medical images. We attempted to implement a CNN technique aimed at improving interpretability by relating filters in the last convolutional to specific output classes. Variations of such a technique were explored and assessed using chest CT images for classification based on the presence of lungs and lesions. A search was conducted to optimize the specific hyper-parameters necessary for the evaluated strategies. A novel strategy is proposed employing transfer learning and regularization. Models obtained with this strategy and the optimized hyperparameters were statistically compared to standard models, demonstrating greater interpretability without a significant loss in predictive accuracy. We achieved CNN models with improved interpretability, which is crucial for the development of more explainable and reliable AI systems.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100211"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388250","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}

引用次数: 0

Predicting the effect of Bevacizumab therapy in ovarian cancer from H&E whole slide images using transformer model

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100231

Md Shakhawat Hossain , Munim Ahmed , Md Sahilur Rahman , MM Mahbubul Syeed , Mohammad Faisal Uddin

Ovarian cancer (OC) ranks fifth in all cancer-related fatalities in women. Epithelial ovarian cancer (EOC) is a subclass of OC, accounting for 95 % of all patients. Conventional treatment for EOC is debulking surgery with adjuvant Chemotherapy; however, in 70 % of cases, this leads to progressive resistance and tumor recurrence. The United States Food and Drug Administration (FDA) recently approved Bevacizumab therapy for EOC patients. Bevacizumab improved survival and decreased recurrence in 30 % of cases, while the rest reported side effects, which include severe hypertension (27 %), thrombocytopenia (26 %), bleeding issues (39 %), heart problems (11 %), kidney problems (7 %), intestinal perforation and delayed wound healing. Moreover, it is costly; single-cycle Bevacizumab therapy costs approximately $3266. Therefore, selecting patients for this therapy is critical due to the high cost, probable adverse effects and small beneficiaries. Several methods were proposed previously; however, they failed to attain adequate accuracy. We present an AI-driven method to predict the effect from H&E whole slide image (WSI) produced from a patient's biopsy. We trained multiple CNN and transformer models using 10 × and 20 × images to predict the effect. Finally, the Data Efficient Image Transformer (DeiT) model was selected considering its high accuracy, interoperability and time efficiency. The proposed method achieved 96.60 % test accuracy and 93 % accuracy in 5-fold cross-validation and can predict the effect in less than 30 s. This method outperformed the state-of-the-art test accuracy (85.10 %) by 11 % and cross-validation accuracy (88.2 %) by 5 %. High accuracy and low prediction time ensured the efficacy of the proposed method.

{"title":"Predicting the effect of Bevacizumab therapy in ovarian cancer from H&E whole slide images using transformer model","authors":"Md Shakhawat Hossain , Munim Ahmed , Md Sahilur Rahman , MM Mahbubul Syeed , Mohammad Faisal Uddin","doi":"10.1016/j.ibmed.2025.100231","DOIUrl":"10.1016/j.ibmed.2025.100231","url":null,"abstract":"<div><div>Ovarian cancer (OC) ranks fifth in all cancer-related fatalities in women. Epithelial ovarian cancer (EOC) is a subclass of OC, accounting for 95 % of all patients. Conventional treatment for EOC is debulking surgery with adjuvant Chemotherapy; however, in 70 % of cases, this leads to progressive resistance and tumor recurrence. The United States Food and Drug Administration (FDA) recently approved Bevacizumab therapy for EOC patients. Bevacizumab improved survival and decreased recurrence in 30 % of cases, while the rest reported side effects, which include severe hypertension (27 %), thrombocytopenia (26 %), bleeding issues (39 %), heart problems (11 %), kidney problems (7 %), intestinal perforation and delayed wound healing. Moreover, it is costly; single-cycle Bevacizumab therapy costs approximately $3266. Therefore, selecting patients for this therapy is critical due to the high cost, probable adverse effects and small beneficiaries. Several methods were proposed previously; however, they failed to attain adequate accuracy. We present an AI-driven method to predict the effect from H&E whole slide image (WSI) produced from a patient's biopsy. We trained multiple CNN and transformer models using 10 × and 20 × images to predict the effect. Finally, the Data Efficient Image Transformer (DeiT) model was selected considering its high accuracy, interoperability and time efficiency. The proposed method achieved 96.60 % test accuracy and 93 % accuracy in 5-fold cross-validation and can predict the effect in less than 30 s. This method outperformed the state-of-the-art test accuracy (85.10 %) by 11 % and cross-validation accuracy (88.2 %) by 5 %. High accuracy and low prediction time ensured the efficacy of the proposed method.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100231"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601341","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}

引用次数: 0

Using convolutional network in graphical model detection of autism disorders with fuzzy inference systems

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100213

S. Rajaprakash , C. Bagath Basha , C. Sunitha Ram , I. Ameethbasha , V. Subapriya , R. Sofia

Autism spectrum disorder (ASD) study faces several challenges, including variations in brain connectivity patterns, small sample sizes, and data heterogeneity detection by magnetic resonance imaging (MRI). These issues make it challenging to identify consistent imaging modalities. Researchers have explored improved analysis techniques to solve the above problem via multimodal imaging and graph-based methods. Therefore, it is better to understand ASD neurology. The current techniques focus mainly on pairwise comparisons between individuals and often overlook features and individual characteristics. To overcome these limitations, in the proposed novel method, a multiscale enhanced graph with a convolutional network is used for ASD detection.

This work integrates non-imaging phenotypic data (from brain imaging data) with functional connectivity data (from Functional magnetic resonance images). In this approach, the population graph represents all individuals as vertices. The phenotypic data were used to calculate the weight between vertices in the graph using the fuzzy inference system. Fuzzy if-then rules, is used to determine the similarity between the phenotypic data. Each vertice connects feature vectors derived from the image data. The vertices and weights of each edge are used to incorporate phenotypic information. A random walk with a fuzzy MSE-GCN framework employs multiple parallel GCN layer embeddings. The outputs from these layers are joined in a completely linked layer to detect ASD efficiently. We assessed the performance of this background by the ABIDE data set and utilized recursive feature elimination and a multilayer perceptron for feature selection. This method achieved an accuracy rate of 87 % better than the current study.

{"title":"Using convolutional network in graphical model detection of autism disorders with fuzzy inference systems","authors":"S. Rajaprakash , C. Bagath Basha , C. Sunitha Ram , I. Ameethbasha , V. Subapriya , R. Sofia","doi":"10.1016/j.ibmed.2025.100213","DOIUrl":"10.1016/j.ibmed.2025.100213","url":null,"abstract":"<div><div>Autism spectrum disorder (ASD) study faces several challenges, including variations in brain connectivity patterns, small sample sizes, and data heterogeneity detection by magnetic resonance imaging (MRI). These issues make it challenging to identify consistent imaging modalities. Researchers have explored improved analysis techniques to solve the above problem via multimodal imaging and graph-based methods. Therefore, it is better to understand ASD neurology. The current techniques focus mainly on pairwise comparisons between individuals and often overlook features and individual characteristics. To overcome these limitations, in the proposed novel method, a multiscale enhanced graph with a convolutional network is used for ASD detection.</div><div>This work integrates non-imaging phenotypic data (from brain imaging data) with functional connectivity data (from Functional magnetic resonance images). In this approach, the population graph represents all individuals as vertices. The phenotypic data were used to calculate the weight between vertices in the graph using the fuzzy inference system. Fuzzy if-then rules, is used to determine the similarity between the phenotypic data. Each vertice connects feature vectors derived from the image data. The vertices and weights of each edge are used to incorporate phenotypic information. A random walk with a fuzzy MSE-GCN framework employs multiple parallel GCN layer embeddings. The outputs from these layers are joined in a completely linked layer to detect ASD efficiently. We assessed the performance of this background by the ABIDE data set and utilized recursive feature elimination and a multilayer perceptron for feature selection. This method achieved an accuracy rate of 87 % better than the current study.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100213"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350535","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}

引用次数: 0

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

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 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}

引用次数: 0

A multimodal machine learning model for bipolar disorder mania classification: Insights from acoustic, linguistic, and visual cues

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100223

Kiruthiga Devi Murugavel , Parthasarathy R , Sandeep Kumar Mathivanan , Saravanan Srinivasan , Basu Dev Shivahare , Mohd Asif Shah

Mood fluctuations that can vary from manic to depressive states are a symptom of a disease known as bipolar disorder, which affects mental health. Interviews with patients and gathering information from their families are essential steps in the diagnostic process for bipolar disorder. Automated approaches for treating bipolar disorder are also being explored. In mental health prevention and care, machine learning techniques (ML) are increasingly used to detect and treat diseases. With frequently analyzed human behaviour patterns, identified symptoms, and risk factors as various parameters of the dataset, predictions can be made for improving traditional diagnosis methods. In this study, A Multimodal Fusion System was developed based on an auditory, linguistic, and visual patient recording as an input dataset for a three-stage mania classification decision system. Deep Denoising Autoencoders (DDAEs) are introduced to learn common representations across five modalities: acoustic characteristics, eye gaze, facial landmarks, head posture, and Facial Action Units (FAUs). This is done in particular for the audio-visual modality. The distributed representations and the transient information during each recording session are eventually encoded into Fisher Vectors (FVs), which capture the representations. Once the Fisher Vectors (FVs) and document embeddings are integrated, a Multi-Task Neural Network is used to perform the classification task, while mitigating overfitting issues caused by the limited size of the bipolar disorder dataset. The study introduces Deep Denoising Autoencoders (DDAEs) for cross-modal representation learning and utilizes Fisher Vectors with Multi-Task Neural Networks, enhancing diagnostic accuracy while highlighting the benefits of multimodal fusion for mental health diagnostics. Achieving an unweighted average recall score of 64.8 %, with the highest AUC-ROC of 0.85 & less interface time of 6.5 ms/sample scores the effectiveness of integrating multiple modalities in improving system performance and advancing feature representation and model interpretability.

{"title":"A multimodal machine learning model for bipolar disorder mania classification: Insights from acoustic, linguistic, and visual cues","authors":"Kiruthiga Devi Murugavel , Parthasarathy R , Sandeep Kumar Mathivanan , Saravanan Srinivasan , Basu Dev Shivahare , Mohd Asif Shah","doi":"10.1016/j.ibmed.2025.100223","DOIUrl":"10.1016/j.ibmed.2025.100223","url":null,"abstract":"<div><div>Mood fluctuations that can vary from manic to depressive states are a symptom of a disease known as bipolar disorder, which affects mental health. Interviews with patients and gathering information from their families are essential steps in the diagnostic process for bipolar disorder. Automated approaches for treating bipolar disorder are also being explored. In mental health prevention and care, machine learning techniques (ML) are increasingly used to detect and treat diseases. With frequently analyzed human behaviour patterns, identified symptoms, and risk factors as various parameters of the dataset, predictions can be made for improving traditional diagnosis methods. In this study, A Multimodal Fusion System was developed based on an auditory, linguistic, and visual patient recording as an input dataset for a three-stage mania classification decision system. Deep Denoising Autoencoders (DDAEs) are introduced to learn common representations across five modalities: acoustic characteristics, eye gaze, facial landmarks, head posture, and Facial Action Units (FAUs). This is done in particular for the audio-visual modality. The distributed representations and the transient information during each recording session are eventually encoded into Fisher Vectors (FVs), which capture the representations. Once the Fisher Vectors (FVs) and document embeddings are integrated, a Multi-Task Neural Network is used to perform the classification task, while mitigating overfitting issues caused by the limited size of the bipolar disorder dataset. The study introduces Deep Denoising Autoencoders (DDAEs) for cross-modal representation learning and utilizes Fisher Vectors with Multi-Task Neural Networks, enhancing diagnostic accuracy while highlighting the benefits of multimodal fusion for mental health diagnostics. Achieving an unweighted average recall score of 64.8 %, with the highest AUC-ROC of 0.85 & less interface time of 6.5 ms/sample scores the effectiveness of integrating multiple modalities in improving system performance and advancing feature representation and model interpretability.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100223"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792053","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}

引用次数: 0

An integrated machine learning based adaptive error minimization framework for Alzheimer's stage identification

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 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.

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引用次数: 0

Optimizing ResNet50 performance using stochastic gradient descent on MRI images for Alzheimer's disease classification

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100219

Mohamed Amine Mahjoubi , Driss Lamrani , Shawki Saleh , Wassima Moutaouakil , Asmae Ouhmida , Soufiane Hamida , Bouchaib Cherradi , Abdelhadi Raihani

The field of optimization is focused on the formulation, analysis, and resolution of problems involving the minimization or maximization of functions. A particular subclass of optimization problems, known as empirical risk minimization, involves fitting a model to observed data. These problems play a central role in various areas such as machine learning, statistical modeling, and decision theory, where the objective is to find a model that best approximates underlying patterns in the data by minimizing a specified loss or risk function. In deep learning (DL) systems, various optimization algorithms are utilized with the gradient descent (GD) algorithm being one of the most significant and effective. Research studies have improved the GD algorithm and developed various successful variants, including stochastic gradient descent (SGD) with momentum, AdaGrad, RMSProp, and Adam. This article provides a comparative analysis of these stochastic gradient descent algorithms based on their accuracy, loss, and training time, as well as the loss of each algorithm in generating an optimization solution. Experiments were conducted using Transfer Learning (TL) technique based on the pre-trained ResNet50 base model for image classification, with a focus on stochastic gradient (SG) for performances optimization. The case study in this work is based on a data extract from the Alzheimer's image dataset, which contains four classes such as Mild Demented, Moderate Demented, Non-Demented, and Very Mild Demented. The obtained results with the Adam and SGD momentum optimizers achieved the highest accuracy of 97.66 % and 97.58 %, respectively.

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