Pub Date : 2024-11-01DOI: 10.1016/j.imed.2024.04.001
Malcolm J. Fisk
This paper gave attention to two issues that arise because of the growth in the use of health data by general practitioners (GPs) and general practices in England. The issues were (a) the use and commercialisation of patients’ personal health data; and(b) the propensity of GPs and general practice staff, in utilising those data, to see patients as fragmented bodies rather than as ‘whole persons’. The paper included attention to the computing needs of general practice from the 1960s and notes the period of growth in GP computer use during the 1990s. The implications of recent increased computer use by GPs and general practices, as a contributor to the further scientification of health, were then explored. Significant is the fact that the paper finds consciousness, from the 1970s, of the two issues. Their importance was emphasised as the momentum increases around the utilisation and sharing of patient data. Related concerns about data privacy and confidentiality are highlighted. In this context, the paper recommended that further research be undertaken with urgency to explore the ways that caring relationships (that have been a hallmark of the work of GPs) can be safeguarded.
{"title":"Computing, data, and the role of general practitioners and general practice in England","authors":"Malcolm J. Fisk","doi":"10.1016/j.imed.2024.04.001","DOIUrl":"10.1016/j.imed.2024.04.001","url":null,"abstract":"<div><div>This paper gave attention to two issues that arise because of the growth in the use of health data by general practitioners (GPs) and general practices in England. The issues were (a) the use and commercialisation of patients’ personal health data; and(b) the propensity of GPs and general practice staff, in utilising those data, to see patients as fragmented bodies rather than as ‘whole persons’. The paper included attention to the computing needs of general practice from the 1960s and notes the period of growth in GP computer use during the 1990s. The implications of recent increased computer use by GPs and general practices, as a contributor to the further scientification of health, were then explored. Significant is the fact that the paper finds consciousness, from the 1970s, of the two issues. Their importance was emphasised as the momentum increases around the utilisation and sharing of patient data. Related concerns about data privacy and confidentiality are highlighted. In this context, the paper recommended that further research be undertaken with urgency to explore the ways that caring relationships (that have been a hallmark of the work of GPs) can be safeguarded.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"4 4","pages":"Pages 268-274"},"PeriodicalIF":4.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143142238","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 : 2024-11-01DOI: 10.1016/j.imed.2024.05.002
Xinyu Huang , Franziska Schmelter , Annemarie Uhlig , Muhammad Tausif Irshad , Muhammad Adeel Nisar , Artur Piet , Lennart Jablonski , Oliver Witt , Torsten Schröder , Christian Sina , Marcin Grzegorzek
Background
Alterations in glucose metabolism, especially the postprandial glucose response (PPGR), are crucial contributors to metabolic dysfunction, which underlies the pathogenesis of metabolic syndrome. Personalized low-glycemic diets have shown promise in reducing postprandial glucose spikes. However, current methods such as invasive continuous glucose monitoring (CGM) or multi-omics data integration to assess PPGR have limitations, including cost and invasiveness that hinder the widespread adoption of these methods in primary disease prevention. Our aim was to assess machine learning algorithms for predicting individual PPGR using non-invasive wearable devices, thereby, circumventing the limitations associated with the existing approaches. By identifying the most accurate model, we sought to provide a more accessible and efficient method for managing glucose metabolic dysfunction.
Methods
This data-driven analysis used the experimental dataset from the SENSE (”Systemische Ernährungsmedizin”) study. Healthy participants used an Empatica E4 wristband and Abbott Freestyle Libre 3 CGM for 10 days. Blood volume pulse, electrodermal activity, heart rate, skin temperature, and the corresponding CGM values were measured. Subsequently, four data-driven deep learning (DL) models-convolutional neural network, lightweight transformer, long short-term memory with attention, and Bi-directional LSTM (BiLSTM) were implemented and compared to determine the potential of DL in predicting interstitial glucose levels without involving food and activity logs.
Results
The proposed BiLSTM achieved the best interstitial glucose prediction performance, with an average root mean squared error of 13.42 mg/dL, an average mean absolute percentage error of 0.12, and only 3.01% values falling within area D in Clarke error grid analysis, incorporating the leave-one-out cross-validation strategy for a five-minute prediction horizon.
Conclusion
The findings of this study may demonstrate the feasibility of transferring knowledge gained from invasive glucose monitoring devices to non-invasive approaches. Furthermore, it could emphasize the promising prospects of combining DL with wearable technologies to predict glucose levels in healthy individuals.
{"title":"Comparison of feature learning methods for non-invasive interstitial glucose prediction using wearable sensors in healthy cohorts: a pilot study","authors":"Xinyu Huang , Franziska Schmelter , Annemarie Uhlig , Muhammad Tausif Irshad , Muhammad Adeel Nisar , Artur Piet , Lennart Jablonski , Oliver Witt , Torsten Schröder , Christian Sina , Marcin Grzegorzek","doi":"10.1016/j.imed.2024.05.002","DOIUrl":"10.1016/j.imed.2024.05.002","url":null,"abstract":"<div><h3>Background</h3><div>Alterations in glucose metabolism, especially the postprandial glucose response (PPGR), are crucial contributors to metabolic dysfunction, which underlies the pathogenesis of metabolic syndrome. Personalized low-glycemic diets have shown promise in reducing postprandial glucose spikes. However, current methods such as invasive continuous glucose monitoring (CGM) or multi-omics data integration to assess PPGR have limitations, including cost and invasiveness that hinder the widespread adoption of these methods in primary disease prevention. Our aim was to assess machine learning algorithms for predicting individual PPGR using non-invasive wearable devices, thereby, circumventing the limitations associated with the existing approaches. By identifying the most accurate model, we sought to provide a more accessible and efficient method for managing glucose metabolic dysfunction.</div></div><div><h3>Methods</h3><div>This data-driven analysis used the experimental dataset from the SENSE (”Systemische Ernährungsmedizin”) study. Healthy participants used an Empatica E4 wristband and Abbott Freestyle Libre 3 CGM for 10 days. Blood volume pulse, electrodermal activity, heart rate, skin temperature, and the corresponding CGM values were measured. Subsequently, four data-driven deep learning (DL) models-convolutional neural network, lightweight transformer, long short-term memory with attention, and Bi-directional LSTM (BiLSTM) were implemented and compared to determine the potential of DL in predicting interstitial glucose levels without involving food and activity logs.</div></div><div><h3>Results</h3><div>The proposed BiLSTM achieved the best interstitial glucose prediction performance, with an average root mean squared error of 13.42 mg/dL, an average mean absolute percentage error of 0.12, and only 3.01% values falling within area D in Clarke error grid analysis, incorporating the leave-one-out cross-validation strategy for a five-minute prediction horizon.</div></div><div><h3>Conclusion</h3><div>The findings of this study may demonstrate the feasibility of transferring knowledge gained from invasive glucose monitoring devices to non-invasive approaches. Furthermore, it could emphasize the promising prospects of combining DL with wearable technologies to predict glucose levels in healthy individuals.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"4 4","pages":"Pages 226-238"},"PeriodicalIF":4.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143142235","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 : 2024-11-01DOI: 10.1016/j.imed.2024.05.003
Rui Li , Xiaoyan Li , Hongzan Sun , Jinzhu Yang , Md Rahaman , Marcin Grzegozek , Tao Jiang , Xinyu Huang , Chen Li
Background
Colorectal cancer is a prevalent and deadly disease worldwide, posing significant diagnostic challenges. Traditional histopathologic image classification is often inefficient and subjective. Although some histopathologists use computer-aided diagnosis to improve efficiency, these methods depend heavily on extensive data and specific annotations, limiting their development. To address these challenges, this paper proposes a method based on few-shot learning.
Methods
This study introduced a few-shot learning approach that combines transfer learning and contrastive learning to classify colorectal cancer histopathology images into benign and malignant categories. The model comprises modules for feature extraction, dimensionality reduction, and classification, trained using a combination of contrast loss and cross-entropy loss. In this paper, we detailed the setup of hyperparameters: -way, -shot, , and the creation of support, query, and test datasets.
Results
Our method achieved over 98% accuracy on a query dataset with 35 samples per category using only 10 training samples per category. We documented the model’s loss, accuracy, and the confusion matrix of the results. Additionally, we employed the -SNE algorithm to analyze and assess the model’s classification performance.
Conclusion
The proposed model may demonstrate significant advantages in accuracy and minimal data dependency, performing robustly across all tested -way, -shot scenarios. It consistently achieved over 93% accuracy on comprehensive test datasets, including 1916 samples, confirming its high classification accuracy and strong generalization capability. Our research could advance the use of few-shot learning in medical diagnostics and also lays the groundwork for extending it to deal with rare, difficult-to-diagnose cases.
{"title":"Few-shot learning based histopathological image classification of colorectal cancer","authors":"Rui Li , Xiaoyan Li , Hongzan Sun , Jinzhu Yang , Md Rahaman , Marcin Grzegozek , Tao Jiang , Xinyu Huang , Chen Li","doi":"10.1016/j.imed.2024.05.003","DOIUrl":"10.1016/j.imed.2024.05.003","url":null,"abstract":"<div><h3>Background</h3><div>Colorectal cancer is a prevalent and deadly disease worldwide, posing significant diagnostic challenges. Traditional histopathologic image classification is often inefficient and subjective. Although some histopathologists use computer-aided diagnosis to improve efficiency, these methods depend heavily on extensive data and specific annotations, limiting their development. To address these challenges, this paper proposes a method based on few-shot learning.</div></div><div><h3>Methods</h3><div>This study introduced a few-shot learning approach that combines transfer learning and contrastive learning to classify colorectal cancer histopathology images into benign and malignant categories. The model comprises modules for feature extraction, dimensionality reduction, and classification, trained using a combination of contrast loss and cross-entropy loss. In this paper, we detailed the setup of hyperparameters: <span><math><mi>n</mi></math></span>-way, <span><math><mi>k</mi></math></span>-shot, <span><math><mi>β</mi></math></span>, and the creation of support, query, and test datasets.</div></div><div><h3>Results</h3><div>Our method achieved over 98% accuracy on a query dataset with 35 samples per category using only 10 training samples per category. We documented the model’s loss, accuracy, and the confusion matrix of the results. Additionally, we employed the <span><math><mi>t</mi></math></span>-SNE algorithm to analyze and assess the model’s classification performance.</div></div><div><h3>Conclusion</h3><div>The proposed model may demonstrate significant advantages in accuracy and minimal data dependency, performing robustly across all tested <span><math><mi>n</mi></math></span>-way, <span><math><mi>k</mi></math></span>-shot scenarios. It consistently achieved over 93% accuracy on comprehensive test datasets, including 1916 samples, confirming its high classification accuracy and strong generalization capability. Our research could advance the use of few-shot learning in medical diagnostics and also lays the groundwork for extending it to deal with rare, difficult-to-diagnose cases.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"4 4","pages":"Pages 256-267"},"PeriodicalIF":4.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143142239","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 : 2024-11-01DOI: 10.1016/j.imed.2024.05.001
Xingbo Cai , Ying Wu , Junshen Huang , Long Wang , Yongqing Xu , Sheng Lu
Statistical shape models (SSMs) are effective for image processing and analysis and have been used in various medical fields, including face recognition and cranial bone recognition. In orthopedics, SSMs are being used in numerous applications, such as automated segmentation of medical images, preoperative planning, intraoperative navigation combined with robotics, simulation reconstruction of defects, human biomechanics research, description of anatomical shape changes, and prosthesis design. This review highlighted the wide range of applications while acknowledging the diversity of methodologies and techniques encompassed by SSMs, including Gaussian process models and nonlinear solutions. In addition, the available software packages for constructing shape models, such as Scalismo, ShapeWorks, and Deformetrica, were discussed.
{"title":"Application of statistical shape models in orthopedics: a narrative review","authors":"Xingbo Cai , Ying Wu , Junshen Huang , Long Wang , Yongqing Xu , Sheng Lu","doi":"10.1016/j.imed.2024.05.001","DOIUrl":"10.1016/j.imed.2024.05.001","url":null,"abstract":"<div><div>Statistical shape models (SSMs) are effective for image processing and analysis and have been used in various medical fields, including face recognition and cranial bone recognition. In orthopedics, SSMs are being used in numerous applications, such as automated segmentation of medical images, preoperative planning, intraoperative navigation combined with robotics, simulation reconstruction of defects, human biomechanics research, description of anatomical shape changes, and prosthesis design. This review highlighted the wide range of applications while acknowledging the diversity of methodologies and techniques encompassed by SSMs, including Gaussian process models and nonlinear solutions. In addition, the available software packages for constructing shape models, such as Scalismo, ShapeWorks, and Deformetrica, were discussed.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"4 4","pages":"Pages 249-255"},"PeriodicalIF":4.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143142237","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 : 2024-08-01DOI: 10.1016/j.imed.2024.03.001
Yang Yu , Xiaoran Li , Tianming Du , Md Rahaman , Marcin Jerzy Grzegorzek , Chen Li , Hongzan Sun
Background
The reproducibility of positron emission tomography (PET) radiomics features is affected by several factors, such as scanning equipment, drug metabolism time and reconstruction algorithm. We aimed to explore the role of 3D local binary pattern (LBP)-based texture in increasing the accuracy and reproducibility of PET radiomics for predicting pelvic lymph node metastasis (PLNM) in patients with cervical cancer.
Methods
We retrospectively analysed data from 177 patients with cervical squamous cell carcinoma. They underwent 18F-fluorodeoxyglucose (18F-FDG)whole-body PET/computed tomography (PET/CT), followed by pelvic 18F-FDG PET/magnetic resonance imaging (PET/MR). We selected reproducible and informative PET radiomics features using Lin's concordance correlation coefficient, least absolute shrinkage and selection operator algorithm, and established 4 models, PET/CT, PET/CT-fusion, PET/MR and PET/MR-fusion, using the logistic regression algorithm. We performed receiver operating characteristic (ROC) curve analysis to evaluate the models in the training data set (65 patients who underwent radical hysterectomy and pelvic lymph node dissection) and test data set (112 patients who received concurrent chemoradiotherapy or no treatment). The DeLong test was used for pairwise comparison of the ROC curves among the models.
Results
The distribution of age, squamous cell carcinoma (SCC), International Federation of Gynaecology and Obstetrics stage and PLNM between the training and test data sets were different (P < 0.05). The LBP-transformed radiomics features (50/379) had higher reproducibility than the original radiomics features (9/107). Accuracy of each model in predicting PLNM was as follows: training data set: PET/CT = PET/CT-fusion = PET/MR-fusion (0.848) and test data set: PET/CT = PET/CT-fusion (0.985) > PET/MR = PET/MR-fusion (0.954). There was no statistical difference between the ROC curve of PET/CT and PET/MR models in both data sets (P > 0.05).
Conclusions
The LBP-transformed radiomics features based on PET images could increase the accuracy and reproducibility of PET radiomics in predicting pelvic lymph node metastasis in cervical cancer to allow the model to be generalised for clinical use across multiple centres.
{"title":"Increasing the accuracy and reproducibility of positron emission tomography radiomics for predicting pelvic lymph node metastasis in patients with cervical cancer using 3D local binary pattern-based texture features","authors":"Yang Yu , Xiaoran Li , Tianming Du , Md Rahaman , Marcin Jerzy Grzegorzek , Chen Li , Hongzan Sun","doi":"10.1016/j.imed.2024.03.001","DOIUrl":"10.1016/j.imed.2024.03.001","url":null,"abstract":"<div><h3>Background</h3><p>The reproducibility of positron emission tomography (PET) radiomics features is affected by several factors, such as scanning equipment, drug metabolism time and reconstruction algorithm. We aimed to explore the role of 3D local binary pattern (LBP)-based texture in increasing the accuracy and reproducibility of PET radiomics for predicting pelvic lymph node metastasis (PLNM) in patients with cervical cancer.</p></div><div><h3>Methods</h3><p>We retrospectively analysed data from 177 patients with cervical squamous cell carcinoma. They underwent <sup>18</sup>F-fluorodeoxyglucose (<sup>18</sup>F-FDG)whole-body PET/computed tomography (PET/CT), followed by pelvic <sup>18</sup>F-FDG PET/magnetic resonance imaging (PET/MR). We selected reproducible and informative PET radiomics features using Lin's concordance correlation coefficient, least absolute shrinkage and selection operator algorithm, and established 4 models, PET/CT, PET/CT-fusion, PET/MR and PET/MR-fusion, using the logistic regression algorithm. We performed receiver operating characteristic (ROC) curve analysis to evaluate the models in the training data set (65 patients who underwent radical hysterectomy and pelvic lymph node dissection) and test data set (112 patients who received concurrent chemoradiotherapy or no treatment). The DeLong test was used for pairwise comparison of the ROC curves among the models.</p></div><div><h3>Results</h3><p>The distribution of age, squamous cell carcinoma (SCC), International Federation of Gynaecology and Obstetrics stage and PLNM between the training and test data sets were different (<em>P</em> < 0.05). The LBP-transformed radiomics features (50/379) had higher reproducibility than the original radiomics features (9/107). Accuracy of each model in predicting PLNM was as follows: training data set: PET/CT = PET/CT-fusion = PET/MR-fusion (0.848) and test data set: PET/CT = PET/CT-fusion (0.985) > PET/MR = PET/MR-fusion (0.954). There was no statistical difference between the ROC curve of PET/CT and PET/MR models in both data sets (<em>P</em> > 0.05).</p></div><div><h3>Conclusions</h3><p>The LBP-transformed radiomics features based on PET images could increase the accuracy and reproducibility of PET radiomics in predicting pelvic lymph node metastasis in cervical cancer to allow the model to be generalised for clinical use across multiple centres.</p></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"4 3","pages":"Pages 153-160"},"PeriodicalIF":4.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667102624000354/pdfft?md5=d1560acb7f081d11510c33553f4f110f&pid=1-s2.0-S2667102624000354-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141710375","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 : 2024-08-01DOI: 10.1016/j.imed.2023.10.001
Puranam Revanth Kumar , Rajesh Kumar Jha , P Akhendra Kumar , B Deevena Raju
Objective
Segmentation of medical images is a crucial process in various image analysis applications. Automated segmentation methods excel in accuracy when compared to manual segmentation in the context of medical image analysis. One of the essential phases in the quantitative analysis of the brain is automated brain tissue segmentation using clinically obtained magnetic resonance imaging (MRI) data. It allows for precise quantitative examination of the brain, which aids in diagnosis, identification, and classification of disorders. Consequently, the efficacy of the segmentation approach is crucial to disease diagnosis and treatment planning.
Methods
This study presented a hybrid optimization method for segmenting brain tissue in clinical MRI scans using a fractional Henry horse herd gas optimization-based Shepard convolutional neural network (FrHHGO-based ShCNN). To segment the clinical brain MRI images into white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) tissues, the proposed framework was evaluated on the Lifespan Human Connectome Projects (HCP) database. The hybrid optimization algorithm, FrHHGO, integrates the fractional Henry gas optimization (FHGO) and horse herd optimization (HHO) algorithms. Training required 30 min, whereas testing and segmentation of brain tissues from an unseen image required an average of 12 s.
Results
Compared to the results obtained with no refinements, the Skull stripping refinement showed significant improvement. As the method included a preprocessing stage, it was flexible enough to enhance image quality, allowing for better results even with low-resolution input. Maximum precision of 93.2%, recall of 91.5%, Dice score of 91.1%, and F1-score of 90.5% were achieved using the proposed FrHHGO-based ShCNN, which was superior to all other approaches.
Conclusion
The proposed method may outperform existing state-of-the-art methodologies in qualitative and quantitative measurements across a wide range of medical modalities. It might demonstrate its potential for real-life clinical application.
{"title":"Improved neurological diagnoses and treatment strategies via automated human brain tissue segmentation from clinical magnetic resonance imaging","authors":"Puranam Revanth Kumar , Rajesh Kumar Jha , P Akhendra Kumar , B Deevena Raju","doi":"10.1016/j.imed.2023.10.001","DOIUrl":"10.1016/j.imed.2023.10.001","url":null,"abstract":"<div><h3>Objective</h3><p>Segmentation of medical images is a crucial process in various image analysis applications. Automated segmentation methods excel in accuracy when compared to manual segmentation in the context of medical image analysis. One of the essential phases in the quantitative analysis of the brain is automated brain tissue segmentation using clinically obtained magnetic resonance imaging (MRI) data. It allows for precise quantitative examination of the brain, which aids in diagnosis, identification, and classification of disorders. Consequently, the efficacy of the segmentation approach is crucial to disease diagnosis and treatment planning.</p></div><div><h3>Methods</h3><p>This study presented a hybrid optimization method for segmenting brain tissue in clinical MRI scans using a fractional Henry horse herd gas optimization-based Shepard convolutional neural network (FrHHGO-based ShCNN). To segment the clinical brain MRI images into white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) tissues, the proposed framework was evaluated on the Lifespan Human Connectome Projects (HCP) database. The hybrid optimization algorithm, FrHHGO, integrates the fractional Henry gas optimization (FHGO) and horse herd optimization (HHO) algorithms. Training required 30 min, whereas testing and segmentation of brain tissues from an unseen image required an average of 12 s.</p></div><div><h3>Results</h3><p>Compared to the results obtained with no refinements, the Skull stripping refinement showed significant improvement. As the method included a preprocessing stage, it was flexible enough to enhance image quality, allowing for better results even with low-resolution input. Maximum precision of 93.2%, recall of 91.5%, Dice score of 91.1%, and F1-score of 90.5% were achieved using the proposed FrHHGO-based ShCNN, which was superior to all other approaches.</p></div><div><h3>Conclusion</h3><p>The proposed method may outperform existing state-of-the-art methodologies in qualitative and quantitative measurements across a wide range of medical modalities. It might demonstrate its potential for real-life clinical application.</p></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"4 3","pages":"Pages 161-169"},"PeriodicalIF":4.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667102624000342/pdfft?md5=2391abbd7c0cfd5333c834e75e76348b&pid=1-s2.0-S2667102624000342-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141692842","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 : 2024-08-01DOI: 10.1016/j.imed.2023.02.001
Background
Bacterial vaginosis is a polymicrobial syndrome in which the homeostasis exerted by the Latobacillus species that protect the vaginal mucosa has been lost. This study explored the data balancing process with the intention of improving the quality of association rules. The article aimed to balance the unbalanced multiclass dataset to improve association rule creation.
Methods
A dataset with 201 observations and 58 variables was analyzed. A preconstructed dataset was used. The authors collected the data between August 2016 and October 2018 in Tabasco, Mexico. The study population comprised sexually active women ages 18 to 50 who underwent gynecological inspection at the infectious and metabolic diseases research laboratory at the Universidad Juarez Autonoma de Tabasco. To determine the best -value, the random-forest algorithm was used and the balancing was performed with the synthetic minority over-sampling technique (SMOTE), random over-sampling examples (ROSE), and adaptive syntetic sampling approach for imbalanced learning (ADASYN) algorithms. The Apriori algorithm created the rules and to select rules with statistical significance, the is.redundant(), is.significant(), and is.maximal() functions and quality metric Fisher’s exact tes were used. The biological validation was carried out by the expert (bacteriologist).
Results
The ADASYN algorithm at the out of the bag (OOB) error was zero, this was the best -values. In the balancing process the ADASYN algorithm show best the performance. From the dataset balanced with ADASYN, the apriori algorithm created the association rules and the selection with the quality metric Fisher’s exact test, and the biological validation reported 13 rules. Gram - bacteria Atopobium vaginae, Gardnerella vaginalis, Megasphaera filotipo 1, Mycoplasma hominis and Ureaplasma parvum were detected by the apriori algorithm from the balanced dataset.
Conclusion
Balancing may improve the creation of association rules to efficiently model the bacteria that cause bacterial vaginosis.
{"title":"Impact of data balancing a multiclass dataset before the creation of association rules to study bacterial vaginosis","authors":"","doi":"10.1016/j.imed.2023.02.001","DOIUrl":"10.1016/j.imed.2023.02.001","url":null,"abstract":"<div><h3>Background</h3><p>Bacterial vaginosis is a polymicrobial syndrome in which the homeostasis exerted by the <em>Latobacillus</em> species that protect the vaginal mucosa has been lost. This study explored the data balancing process with the intention of improving the quality of association rules. The article aimed to balance the unbalanced multiclass dataset to improve association rule creation.</p></div><div><h3>Methods</h3><p>A dataset with 201 observations and 58 variables was analyzed. A preconstructed dataset was used. The authors collected the data between August 2016 and October 2018 in Tabasco, Mexico. The study population comprised sexually active women ages 18 to 50 who underwent gynecological inspection at the infectious and metabolic diseases research laboratory at the Universidad Juarez Autonoma de Tabasco. To determine the best <span><math><mi>k</mi></math></span>-value, the random-forest algorithm was used and the balancing was performed with the synthetic minority over-sampling technique (SMOTE), random over-sampling examples (ROSE), and adaptive syntetic sampling approach for imbalanced learning (ADASYN) algorithms. The Apriori algorithm created the rules and to select rules with statistical significance, the <em>is.redundant(), is.significant()</em>, and <em>is.maximal()</em> functions and quality metric Fisher’s exact tes were used. The biological validation was carried out by the expert (bacteriologist).</p></div><div><h3>Results</h3><p>The ADASYN algorithm at <span><math><mrow><mi>K</mi><mo>=</mo><mn>9</mn></mrow></math></span> the out of the bag (OOB) error was zero, this was the best <span><math><mi>K</mi></math></span>-values. In the balancing process the ADASYN algorithm show best the performance. From the dataset balanced with ADASYN, the apriori algorithm created the association rules and the selection with the quality metric Fisher’s exact test, and the biological validation reported 13 rules. Gram - bacteria <em>Atopobium vaginae, Gardnerella vaginalis, Megasphaera filotipo 1<strong>,</strong> Mycoplasma hominis</em> and <em>Ureaplasma parvum</em> were detected by the apriori algorithm from the balanced dataset.</p></div><div><h3>Conclusion</h3><p>Balancing may improve the creation of association rules to efficiently model the bacteria that cause bacterial vaginosis.</p></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"4 3","pages":"Pages 188-199"},"PeriodicalIF":4.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667102623000190/pdfft?md5=8cf3d2c99555a9de09737d0e3a9fc329&pid=1-s2.0-S2667102623000190-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48136958","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}
Precise estimation of current and future comorbidities of patients with cardiovascular disease is an important factor in prioritizing continuous physiological monitoring and new therapies. Machine learning (ML) models have shown satisfactory performance in short-term mortality prediction in patients with heart disease, whereas their utility in long-term predictions is limited. This study aimed to investigate the performance of tree-based ML models on long-term mortality prediction and effect of two recently introduced biomarkers on long-term mortality.
Methods
This study used publicly available data from the Collaboration Center of Health Information Application at the Ministry of Health and Welfare, Taiwan, China. The collected data were from patients admitted to the cardiac care unit for acute myocardial infarction (AMI) between November 2003 and September 2004. We collected and analyzed mortality data up to December 2018. Medical records were used to gather demographic and clinical data, including age, gender, body mass index, percutaneous coronary intervention status, and comorbidities such as hypertension, dyslipidemia, ST-segment elevation myocardial infarction, and non-ST-segment elevation myocardial infarction. Using the data, collected from 139 patients with AMI, from medical and demographic records as well as two recently introduced biomarkers, brachial pre-ejection period (bPEP) and brachial ejection time (bET), we investigated the performance of advanced ensemble tree-based ML algorithms (random forest, AdaBoost, and XGBoost) to predict all-cause mortality within 14 years. A nested cross-validation was performed to evaluate and compare the performance of our developed models precisely with that of the conventional logistic regression (LR) as the baseline method.
Results
The developed ML models achieved significantly better performance compared to the baseline LR (C-Statistic, 0.80 for random forest, 0.79 for AdaBoost, and 0.78 for XGBoost, vs. 0.77 for LR) (PRF < 0.001, PAdaBoost < 0.001, and PXGBoost < 0.05). Adding bPEP and bET to our feature set significantly improved the performance of the algorithm, leading to an absolute increase in C-statistic of up to 0.03 (C-statistic, 0.83 for random forest, 0.82 for AdaBoost, and 0.80 for XGBoost, vs. 0.74 for LR) (PRF < 0.001, PAdaBoost < 0.001, PXGBoost < 0.05).
Conclusion
The study indicates that incorporating new biomarkers into advanced ML models may significantly improve long-term mortality prediction in patients with cardiovascular diseases. This advancement may enable better treatment prioritization for high-risk individuals.
背景精确估计心血管疾病患者当前和未来的合并症是优先考虑持续生理监测和新疗法的一个重要因素。机器学习（ML）模型在心脏病患者的短期死亡率预测中表现令人满意，但在长期预测中的作用有限。本研究旨在调查基于树的 ML 模型在长期死亡率预测中的表现，以及最近引入的两种生物标志物对长期死亡率的影响。所收集的数据来自 2003 年 11 月至 2004 年 9 月期间因急性心肌梗死（AMI）入住心脏监护室的患者。我们收集并分析了截至 2018 年 12 月的死亡率数据。病历用于收集人口统计学和临床数据，包括年龄、性别、体重指数、经皮冠状动脉介入治疗情况以及高血压、血脂异常、ST段抬高型心肌梗死和非ST段抬高型心肌梗死等合并症。我们利用从 139 名急性心肌梗死患者的医疗和人口学记录中收集的数据，以及最近推出的两个生物标志物--肱骨射血前时间（bPEP）和肱骨射血时间（bET），研究了基于高级集合树的 ML 算法（随机森林、AdaBoost 和 XGBoost）预测 14 年内全因死亡率的性能。通过嵌套交叉验证来评估和比较我们开发的模型与作为基线方法的传统逻辑回归（LR）的性能。结果与基线逻辑回归相比，所开发的 ML 模型取得了明显更好的性能（C-统计量，随机森林为 0.80，AdaBoost 为 0.79，XGBoost 为 0.78，LR 为 0.77）（PRF < 0.001，PAdaBoost < 0.001，PXGBoost < 0.05）。在特征集中添加 bPEP 和 bET 能显著提高算法的性能，使 C 统计量的绝对值提高了 0.03（随机森林的 C 统计量为 0.83，AdaBoost 为 0.82，XGBoost 为 0.80，而 LR 为 0.74）。74 for LR）（PRF <0.001，PAdaBoost <0.001，PXGBoost <0.05）。结论该研究表明，将新的生物标记物纳入高级 ML 模型可显著改善心血管疾病患者的长期死亡率预测。这种进步可以更好地确定高危人群的治疗优先次序。
{"title":"Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data","authors":"Bijan Roudini , Boshra Khajehpiri , Hamid Abrishami Moghaddam , Mohamad Forouzanfar","doi":"10.1016/j.imed.2024.01.001","DOIUrl":"10.1016/j.imed.2024.01.001","url":null,"abstract":"<div><h3>Background</h3><p>Precise estimation of current and future comorbidities of patients with cardiovascular disease is an important factor in prioritizing continuous physiological monitoring and new therapies. Machine learning (ML) models have shown satisfactory performance in short-term mortality prediction in patients with heart disease, whereas their utility in long-term predictions is limited. This study aimed to investigate the performance of tree-based ML models on long-term mortality prediction and effect of two recently introduced biomarkers on long-term mortality.</p></div><div><h3>Methods</h3><p>This study used publicly available data from the Collaboration Center of Health Information Application at the Ministry of Health and Welfare, Taiwan, China. The collected data were from patients admitted to the cardiac care unit for acute myocardial infarction (AMI) between November 2003 and September 2004. We collected and analyzed mortality data up to December 2018. Medical records were used to gather demographic and clinical data, including age, gender, body mass index, percutaneous coronary intervention status, and comorbidities such as hypertension, dyslipidemia, ST-segment elevation myocardial infarction, and non-ST-segment elevation myocardial infarction. Using the data, collected from 139 patients with AMI, from medical and demographic records as well as two recently introduced biomarkers, brachial pre-ejection period (bPEP) and brachial ejection time (bET), we investigated the performance of advanced ensemble tree-based ML algorithms (random forest, AdaBoost, and XGBoost) to predict all-cause mortality within 14 years. A nested cross-validation was performed to evaluate and compare the performance of our developed models precisely with that of the conventional logistic regression (LR) as the baseline method.</p></div><div><h3>Results</h3><p>The developed ML models achieved significantly better performance compared to the baseline LR (C-Statistic, 0.80 for random forest, 0.79 for AdaBoost, and 0.78 for XGBoost, <em>vs</em>. 0.77 for LR) (<em>P</em><sub>RF</sub> < 0.001, <em>P</em><sub>AdaBoost</sub> < 0.001, and <em>P</em><sub>XGBoost</sub> < 0.05). Adding bPEP and bET to our feature set significantly improved the performance of the algorithm, leading to an absolute increase in C-statistic of up to 0.03 (C-statistic, 0.83 for random forest, 0.82 for AdaBoost, and 0.80 for XGBoost, <em>vs</em>. 0.74 for LR) (<em>P</em><sub>RF</sub> < 0.001, <em>P</em><sub>AdaBoost</sub> < 0.001, <em>P</em><sub>XGBoost</sub> < 0.05).</p></div><div><h3>Conclusion</h3><p>The study indicates that incorporating new biomarkers into advanced ML models may significantly improve long-term mortality prediction in patients with cardiovascular diseases. This advancement may enable better treatment prioritization for high-risk individuals.</p></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"4 3","pages":"Pages 170-176"},"PeriodicalIF":4.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667102624000329/pdfft?md5=039b96bf56f33e4f8342d2c062d97570&pid=1-s2.0-S2667102624000329-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142271265","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}
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