Pub Date : 2025-02-01DOI: 10.1016/j.imed.2025.01.001
Jia Li, Zichun Zhou, Han Lyu, Zhenchang Wang
This editorial presents an optimistic yet cautious perspective on the development, deployment, and regulation of large language models (LLMs) in the field of medicine. It is essential to strike a balance between embracing the benefits of artificial intelligence-driven solutions and preserving the human touch that is vital for providing compassionate care. The exponential growth of medical data has paved the way for the integration of LLMs into healthcare, offering unprecedented opportunities to enhance clinical decision-making and alleviate physicians' workloads. Recently, LLMs have exhibited remarkable potential across various clinical scenarios, including streamlining diagnostic processes, optimizing radiology reports, and providing personalized treatment recommendations. However, the implementation of LLMs in healthcare is not without its challenges. Issues such as the scarcity of high-quality annotated data, privacy concerns, and the risk of generating misleading or overconfident information are significant hurdles that must be addressed. Moreover, while LLMs can replace certain basic tasks traditionally performed by humans, it is crucial to recognize that senior clinicians play an irreplaceable role in complex decision-making and providing emotional support to patients. By harnessing the power of LLMs to augment human capabilities while maintaining essential human elements within healthcare, we might shape a future where artificial intelligence and human intelligence coexist harmoniously. Prioritizing ethical development and deployment for artificial intelligence, empowering healthcare professionals, and safeguarding patient privacy will be key to realizing the full potential of LLMs in revolutionizing healthcare delivery. Through ongoing research, collaboration, and adaptation, responsible integration of LLMs holds promise for elevating both quality and accessibility globally, ultimately creating a more efficient, personalized, and patient-centric healthcare system.
{"title":"Large language models-powered clinical decision support: enhancing or replacing human expertise?","authors":"Jia Li, Zichun Zhou, Han Lyu, Zhenchang Wang","doi":"10.1016/j.imed.2025.01.001","DOIUrl":"10.1016/j.imed.2025.01.001","url":null,"abstract":"<div><div>This editorial presents an optimistic yet cautious perspective on the development, deployment, and regulation of large language models (LLMs) in the field of medicine. It is essential to strike a balance between embracing the benefits of artificial intelligence-driven solutions and preserving the human touch that is vital for providing compassionate care. The exponential growth of medical data has paved the way for the integration of LLMs into healthcare, offering unprecedented opportunities to enhance clinical decision-making and alleviate physicians' workloads. Recently, LLMs have exhibited remarkable potential across various clinical scenarios, including streamlining diagnostic processes, optimizing radiology reports, and providing personalized treatment recommendations. However, the implementation of LLMs in healthcare is not without its challenges. Issues such as the scarcity of high-quality annotated data, privacy concerns, and the risk of generating misleading or overconfident information are significant hurdles that must be addressed. Moreover, while LLMs can replace certain basic tasks traditionally performed by humans, it is crucial to recognize that senior clinicians play an irreplaceable role in complex decision-making and providing emotional support to patients. By harnessing the power of LLMs to augment human capabilities while maintaining essential human elements within healthcare, we might shape a future where artificial intelligence and human intelligence coexist harmoniously. Prioritizing ethical development and deployment for artificial intelligence, empowering healthcare professionals, and safeguarding patient privacy will be key to realizing the full potential of LLMs in revolutionizing healthcare delivery. Through ongoing research, collaboration, and adaptation, responsible integration of LLMs holds promise for elevating both quality and accessibility globally, ultimately creating a more efficient, personalized, and patient-centric healthcare system.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 1","pages":"Pages 1-4"},"PeriodicalIF":4.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453681","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 : 2025-02-01DOI: 10.1016/j.imed.2024.10.001
Mingming Lin , Liwei Zhang , Zhibin Wang , Hengyu Liu , Keqiang Wang , Guozhang Tang , Wenkai Wang , Pin Sun
Background
Accurate measurement of left ventricular ejection fraction (LVEF) is crucial in diagnosing and managing cardiac conditions. Deep learning (DL) models offer potential to improve the consistency and efficiency of these measurements, reducing reliance on operator expertise.
Objective
The aim of this study was to develop an innovative software-hardware combined device, featuring a novel DL algorithm for the automated quantification of LVEF from 2D echocardiographic images.
Methods
A dataset of 2,113 patients admitted to the Affiliated Hospital of Qingdao University between January and June 2023 was assembled and split into training and test groups. Another 500 patients from another campus were prospectively collected as external validation group. The age, sex, reason for echocardiography and the type of patients were collected. Following standardized protocol training by senior echocardiographers using domestic ultrasound equipment, apical four-chamber view images were labeled manually and utilized for training our deep learning framework. This system combined convolutional neural networks (CNN) with transformers for enhanced image recognition and analysis. Combined with the model that was named QHAutoEF, a ‘one-touch’ software module was developed and integrated into the echocardiography hardware, providing intuitive, real-time visualization of LVEF measurements. The device's performance was evaluated with metrics such as the Dice coefficient and Jaccard index, along with computational efficiency indicators. The dice index, intersection over union, size, floating point operations per second and calculation time were used to compare the performance of our model with alternative deep learning architectures. Bland-Altman analysis and the receiver operating characteristic (ROC) curve were used for validation of the accuracy of the model. The scatter plot was used to evaluate the consistency of the manual and automated results among subgroups.
Results
Patients from external validation group were older than those from training group ((60±14) years vs. (55±16) years, respectively, P < 0.001). The gender distribution among three groups were showed no statistical difference (43 % vs. 42 % vs. 50 %, respectively, P = 0.095). Significant differences were showed among patients with different type (all P < 0.001) and reason for echocardiography (all P <0.001 except for other reasons). QHAutoEF achieved a high Dice index (0.942 at end-diastole, 0.917 at end-systole) with a notably compact model size (10.2 MB) and low computational cost (93.86 G floating point operations (FLOPs)). It exhibited high consistency with expert manual measurements (intraclass correlation coefficient (ICC) =0.90 (0.89, 0.92), P < 0.001) and excellent capability to differentiate patients with
{"title":"A combined system with convolutional neural networks and transformers for automated quantification of left ventricular ejection fraction from 2D echocardiographic images","authors":"Mingming Lin , Liwei Zhang , Zhibin Wang , Hengyu Liu , Keqiang Wang , Guozhang Tang , Wenkai Wang , Pin Sun","doi":"10.1016/j.imed.2024.10.001","DOIUrl":"10.1016/j.imed.2024.10.001","url":null,"abstract":"<div><h3>Background</h3><div>Accurate measurement of left ventricular ejection fraction (LVEF) is crucial in diagnosing and managing cardiac conditions. Deep learning (DL) models offer potential to improve the consistency and efficiency of these measurements, reducing reliance on operator expertise.</div></div><div><h3>Objective</h3><div>The aim of this study was to develop an innovative software-hardware combined device, featuring a novel DL algorithm for the automated quantification of LVEF from 2D echocardiographic images.</div></div><div><h3>Methods</h3><div>A dataset of 2,113 patients admitted to the Affiliated Hospital of Qingdao University between January and June 2023 was assembled and split into training and test groups. Another 500 patients from another campus were prospectively collected as external validation group. The age, sex, reason for echocardiography and the type of patients were collected. Following standardized protocol training by senior echocardiographers using domestic ultrasound equipment, apical four-chamber view images were labeled manually and utilized for training our deep learning framework. This system combined convolutional neural networks (CNN) with transformers for enhanced image recognition and analysis. Combined with the model that was named QHAutoEF, a ‘one-touch’ software module was developed and integrated into the echocardiography hardware, providing intuitive, real-time visualization of LVEF measurements. The device's performance was evaluated with metrics such as the Dice coefficient and Jaccard index, along with computational efficiency indicators. The dice index, intersection over union, size, floating point operations per second and calculation time were used to compare the performance of our model with alternative deep learning architectures. Bland-Altman analysis and the receiver operating characteristic (ROC) curve were used for validation of the accuracy of the model. The scatter plot was used to evaluate the consistency of the manual and automated results among subgroups.</div></div><div><h3>Results</h3><div>Patients from external validation group were older than those from training group ((60±14) years <em>vs.</em> (55±16) years, respectively, <em>P</em> < 0.001). The gender distribution among three groups were showed no statistical difference (43 % <em>vs.</em> 42 % <em>vs.</em> 50 %, respectively, <em>P</em> = 0.095). Significant differences were showed among patients with different type (all <em>P</em> < 0.001) and reason for echocardiography (all <em>P</em> <0.001 except for other reasons). QHAutoEF achieved a high Dice index (0.942 at end-diastole, 0.917 at end-systole) with a notably compact model size (10.2 MB) and low computational cost (93.86 G floating point operations (FLOPs)). It exhibited high consistency with expert manual measurements (intraclass correlation coefficient (ICC) =0.90 (0.89, 0.92), <em>P</em> < 0.001) and excellent capability to differentiate patients with","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 1","pages":"Pages 46-53"},"PeriodicalIF":4.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454242","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 : 2025-02-01DOI: 10.1016/j.imed.2024.09.005
Hedayetul Islam , Md. Sadiq Iqbal , Muhammad Minoar Hossain
Objective
Hypertension is a critical medical condition that increases the risks of many fatal diseases. Early detection of hypertension can be crucial to lead a healthy life. Machine learning (ML) can be useful for the early prediction of a patient's likelihood of having a blood pressure abnormality and preventing it. Explainable artificial intelligence (XAI) is a state-of-the-art ML toolset that helps us understand and explain the prediction of an ML model. This research aims to build an automatic blood pressure anomaly detection system with maximum accuracy using the fewest features and learn why a model arrived at a particular result using XAI.
Methods
This study utilized the “Blood Pressure Data for Disease Prediction” dataset from Kaggle. Data were collected from medical reports of random participants in 2019 based on the presence of blood pressure abnormality, chronic kidney disease, and adrenal and thyroid disorders. We have used several ML algorithms (extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), decision tree (DT), and logistic regression (LR)) to predict blood pressure abnormality based on patient's data. Principal component analysis (PCA) and recursive feature elimination (RFE) algorithms were used as feature optimizers. Key outcome metrics included receiver operating characteristic (ROC) curve analysis and accuracy. Additional performance measurement techniques, such as precision, recall, specificity, F1-score, and kappa were calculated to identify the model with the best performance. Moreover, several XAI methods, namely permutation feature importance (PFI), partial dependence plots (PDP), Shapley additive explanations (SHAP), and local interpretable model-agnostic explanations (LIME) were implemented for additional exploration of our best model.
Results
The combination of RFE and XGBoost provides the most significant results. The results of the study show that the algorithm has an AUC of 0.95, indicating good discriminatory power in detecting abnormal blood pressure. The accuracy, precision, recall, specificity, F1-score, and kappa scores were 91.50%, 88.64%, 92.65%, 92.27%, 90.83%, and 0.8, respectively. According to the XAI experiment, the genetic pedigree coefficient and hemoglobin level in a patient contribute the most to blood pressure abnormality prediction. Adrenal and thyroid diseases, as well as chronic kidney illness, have an impact on the projections. Existing research backs up this conclusion.
Conclusion
Compared to previous studies on this dataset, our results would be superior, and the use of XAI shed new light on our model's prediction. This study would provide new insight into blood pressure detection in the medical profession.
{"title":"Blood pressure abnormality detection and interpretation utilizing explainable artificial intelligence","authors":"Hedayetul Islam , Md. Sadiq Iqbal , Muhammad Minoar Hossain","doi":"10.1016/j.imed.2024.09.005","DOIUrl":"10.1016/j.imed.2024.09.005","url":null,"abstract":"<div><h3>Objective</h3><div>Hypertension is a critical medical condition that increases the risks of many fatal diseases. Early detection of hypertension can be crucial to lead a healthy life. Machine learning (ML) can be useful for the early prediction of a patient's likelihood of having a blood pressure abnormality and preventing it. Explainable artificial intelligence (XAI) is a state-of-the-art ML toolset that helps us understand and explain the prediction of an ML model. This research aims to build an automatic blood pressure anomaly detection system with maximum accuracy using the fewest features and learn why a model arrived at a particular result using XAI.</div></div><div><h3>Methods</h3><div>This study utilized the “Blood Pressure Data for Disease Prediction” dataset from Kaggle. Data were collected from medical reports of random participants in 2019 based on the presence of blood pressure abnormality, chronic kidney disease, and adrenal and thyroid disorders. We have used several ML algorithms (extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), decision tree (DT), and logistic regression (LR)) to predict blood pressure abnormality based on patient's data. Principal component analysis (PCA) and recursive feature elimination (RFE) algorithms were used as feature optimizers. Key outcome metrics included receiver operating characteristic (ROC) curve analysis and accuracy. Additional performance measurement techniques, such as precision, recall, specificity, F1-score, and kappa were calculated to identify the model with the best performance. Moreover, several XAI methods, namely permutation feature importance (PFI), partial dependence plots (PDP), Shapley additive explanations (SHAP), and local interpretable model-agnostic explanations (LIME) were implemented for additional exploration of our best model.</div></div><div><h3>Results</h3><div>The combination of RFE and XGBoost provides the most significant results. The results of the study show that the algorithm has an AUC of 0.95, indicating good discriminatory power in detecting abnormal blood pressure. The accuracy, precision, recall, specificity, F1-score, and kappa scores were 91.50%, 88.64%, 92.65%, 92.27%, 90.83%, and 0.8, respectively. According to the XAI experiment, the genetic pedigree coefficient and hemoglobin level in a patient contribute the most to blood pressure abnormality prediction. Adrenal and thyroid diseases, as well as chronic kidney illness, have an impact on the projections. Existing research backs up this conclusion.</div></div><div><h3>Conclusion</h3><div>Compared to previous studies on this dataset, our results would be superior, and the use of XAI shed new light on our model's prediction. This study would provide new insight into blood pressure detection in the medical profession.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 1","pages":"Pages 54-65"},"PeriodicalIF":4.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454243","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.2024.02.002
Bo Wang , Ruijie Wang , Zongren Chen , Qixiang Zhang , Wan Yuwen , Xia Liu
Objective
Vertebral segmentation in computed tomography (CT) images remains an essential issue in medical image analysis, stemming from the variability in vertebral shapes, high complex deformations, and the inherent ambiguity in CT scans. The purpose of this study was to develop advanced methods to effectively address this challenging task.
Methods
We proposed an attention-driven asymmetric convolution deep learning (AACDL) framework for vertebral segmentation. Specifically, our approach involved constructing a novel asymmetric convolutional U-shaped deep learning architecture to enhance the feature extraction capabilities by increasing its depth for capturing richer spatial details. Further, we constructed a pyramid global context module that integrates global context information through pyramid pooling to boost segmentation accuracy particularly in smaller anatomical regions. Sequential channel and spatial attention mechanisms were also implemented within the network to enable it to automatically concentrate on learning the most salient features and regions across different dimensions.
Results
The performance precision of our network was rigorously assessed using a suite of four benchmark metrics: the dice coefficient, mean intersection over union (mIoU), precision rate, and F1-score. When compared against the ground truth, our model delivered outstanding scores, attaining a dice coefficient of 82.79%, an mIoU of 90.72%, a precision rate of 90.19%, and an F1-score of 90.09%, each reflecting the commendable accuracy and reliability of our network's segmentation output.
Conclusion
The proposed AACDL method might successfully realize accurate segmentation of vertebral CT images, thereby demonstrating significant potential for clinical applications with its robust performance metrics. Its ability to handle the complexities associated with vertebral segmentation may pave the way for enhanced diagnostic and treatment planning processes in healthcare settings.
{"title":"Improving vertebral diagnosis in computed tomography scans: a clinically oriented attention-driven asymmetric convolution network for segmentation","authors":"Bo Wang , Ruijie Wang , Zongren Chen , Qixiang Zhang , Wan Yuwen , Xia Liu","doi":"10.1016/j.imed.2024.02.002","DOIUrl":"10.1016/j.imed.2024.02.002","url":null,"abstract":"<div><h3>Objective</h3><div>Vertebral segmentation in computed tomography (CT) images remains an essential issue in medical image analysis, stemming from the variability in vertebral shapes, high complex deformations, and the inherent ambiguity in CT scans. The purpose of this study was to develop advanced methods to effectively address this challenging task.</div></div><div><h3>Methods</h3><div>We proposed an attention-driven asymmetric convolution deep learning (AACDL) framework for vertebral segmentation. Specifically, our approach involved constructing a novel asymmetric convolutional U-shaped deep learning architecture to enhance the feature extraction capabilities by increasing its depth for capturing richer spatial details. Further, we constructed a pyramid global context module that integrates global context information through pyramid pooling to boost segmentation accuracy particularly in smaller anatomical regions. Sequential channel and spatial attention mechanisms were also implemented within the network to enable it to automatically concentrate on learning the most salient features and regions across different dimensions.</div></div><div><h3>Results</h3><div>The performance precision of our network was rigorously assessed using a suite of four benchmark metrics: the dice coefficient, mean intersection over union (mIoU), precision rate, and F1-score. When compared against the ground truth, our model delivered outstanding scores, attaining a dice coefficient of 82.79%, an mIoU of 90.72%, a precision rate of 90.19%, and an F1-score of 90.09%, each reflecting the commendable accuracy and reliability of our network's segmentation output.</div></div><div><h3>Conclusion</h3><div>The proposed AACDL method might successfully realize accurate segmentation of vertebral CT images, thereby demonstrating significant potential for clinical applications with its robust performance metrics. Its ability to handle the complexities associated with vertebral segmentation may pave the way for enhanced diagnostic and treatment planning processes in healthcare settings.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"4 4","pages":"Pages 239-248"},"PeriodicalIF":4.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143142236","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.2024.09.001
Fei Zhou , Yue Huang , Chengquan Li , Xiaobin Feng , Wei Yin , Guoyan Zhang , Sisi Duan
The healthcare industry is significantly transforming toward digital and smart healthcare. Blockchain, as an emerging distributed collaborative paradigm, offers a promising solution for ensuring trustworthiness and high availability of services in the evolving healthcare sector. This study aimed to provide a comprehensive survey of blockchain-based applications in smart healthcare. We first present the real-world blockchain use cases in smart healthcare and related fields, outlining the motivations for this study. Next, we review the cutting-edge blockchain applications in various domains, including health data sharing, public health management, drug supply chains, insurance claims, and the Internet-of-Medical-Things. A detailed analysis of several blockchain-based healthcare data sharing scenarios is also included. The findings illustrate the diverse applications of blockchain technology in enhancing healthcare systems, along with a detailed examination of the challenges related to technical implementation and adoption. We discussed the challenges encountered in blockchain integration in smart healthcare and propose potential solutions to guide future research in this area.
{"title":"Blockchain for digital healthcare: Case studies and adoption challenges","authors":"Fei Zhou , Yue Huang , Chengquan Li , Xiaobin Feng , Wei Yin , Guoyan Zhang , Sisi Duan","doi":"10.1016/j.imed.2024.09.001","DOIUrl":"10.1016/j.imed.2024.09.001","url":null,"abstract":"<div><div>The healthcare industry is significantly transforming toward digital and smart healthcare. Blockchain, as an emerging distributed collaborative paradigm, offers a promising solution for ensuring trustworthiness and high availability of services in the evolving healthcare sector. This study aimed to provide a comprehensive survey of blockchain-based applications in smart healthcare. We first present the real-world blockchain use cases in smart healthcare and related fields, outlining the motivations for this study. Next, we review the cutting-edge blockchain applications in various domains, including health data sharing, public health management, drug supply chains, insurance claims, and the Internet-of-Medical-Things. A detailed analysis of several blockchain-based healthcare data sharing scenarios is also included. The findings illustrate the diverse applications of blockchain technology in enhancing healthcare systems, along with a detailed examination of the challenges related to technical implementation and adoption. We discussed the challenges encountered in blockchain integration in smart healthcare and propose potential solutions to guide future research in this area.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"4 4","pages":"Pages 215-225"},"PeriodicalIF":4.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141431","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.2024.04.001
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}
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