Pub Date : 2024-04-03DOI: 10.3390/biomedinformatics4020057
Shisir Ruwali, S. Talebi, Ashen Fernando, Lakitha O. H. Wijeratne, John Waczak, Prabuddha M. H. Dewage, David J. Lary, John Sadler, T. Lary, Matthew Lary, Adam Aker
Introduction: Air pollution has numerous impacts on human health on a variety of time scales. Pollutants such as particulate matter—PM1 and PM2.5, carbon dioxide (CO2), nitrogen dioxide (NO2), and nitric oxide (NO) are exemplars of the wider human exposome. In this study, we adopted a unique approach by utilizing the responses of human autonomic systems to gauge the abundance of pollutants in inhaled air. Objective: To investigate how the human body autonomically responds to inhaled pollutants in microenvironments, including PM1, PM2.5, CO2, NO2, and NO, on small temporal and spatial scales by making use of biometric observations of the human autonomic response. To test the accuracy in predicting the concentrations of these pollutants using biological measurements of the participants. Methodology: Two experimental approaches having a similar methodology that employs a biometric suite to capture the physiological responses of cyclists were compared, and multiple sensors were used to measure the pollutants in the air surrounding them. Machine learning algorithms were used to estimate the levels of these pollutants and decipher the body’s automatic reactions to them. Results: We observed high precision in predicting PM1, PM2.5, and CO2 using a limited set of biometrics measured from the participants, as indicated with the coefficient of determination (R2) between the estimated and true values of these pollutants of 0.99, 0.96, and 0.98, respectively. Although the predictions for NO2 and NO were reliable at lower concentrations, which was observed qualitatively, the precision varied throughout the data range. Skin temperature, heart rate, and respiration rate were the common physiological responses that were the most influential in predicting the concentration of these pollutants. Conclusion: Biometric measurements can be used to estimate air quality components such as PM1, PM2.5, and CO2 with high degrees of accuracy and can also be used to decipher the effect of these pollutants on the human body using machine learning techniques. The results for NO2 and NO suggest a requirement to improve our models with more comprehensive data collection or advanced machine learning techniques to improve the results for these two pollutants.
{"title":"Quantifying Inhaled Concentrations of Particulate Matter, Carbon Dioxide, Nitrogen Dioxide, and Nitric Oxide Using Observed Biometric Responses with Machine Learning","authors":"Shisir Ruwali, S. Talebi, Ashen Fernando, Lakitha O. H. Wijeratne, John Waczak, Prabuddha M. H. Dewage, David J. Lary, John Sadler, T. Lary, Matthew Lary, Adam Aker","doi":"10.3390/biomedinformatics4020057","DOIUrl":"https://doi.org/10.3390/biomedinformatics4020057","url":null,"abstract":"Introduction: Air pollution has numerous impacts on human health on a variety of time scales. Pollutants such as particulate matter—PM1 and PM2.5, carbon dioxide (CO2), nitrogen dioxide (NO2), and nitric oxide (NO) are exemplars of the wider human exposome. In this study, we adopted a unique approach by utilizing the responses of human autonomic systems to gauge the abundance of pollutants in inhaled air. Objective: To investigate how the human body autonomically responds to inhaled pollutants in microenvironments, including PM1, PM2.5, CO2, NO2, and NO, on small temporal and spatial scales by making use of biometric observations of the human autonomic response. To test the accuracy in predicting the concentrations of these pollutants using biological measurements of the participants. Methodology: Two experimental approaches having a similar methodology that employs a biometric suite to capture the physiological responses of cyclists were compared, and multiple sensors were used to measure the pollutants in the air surrounding them. Machine learning algorithms were used to estimate the levels of these pollutants and decipher the body’s automatic reactions to them. Results: We observed high precision in predicting PM1, PM2.5, and CO2 using a limited set of biometrics measured from the participants, as indicated with the coefficient of determination (R2) between the estimated and true values of these pollutants of 0.99, 0.96, and 0.98, respectively. Although the predictions for NO2 and NO were reliable at lower concentrations, which was observed qualitatively, the precision varied throughout the data range. Skin temperature, heart rate, and respiration rate were the common physiological responses that were the most influential in predicting the concentration of these pollutants. Conclusion: Biometric measurements can be used to estimate air quality components such as PM1, PM2.5, and CO2 with high degrees of accuracy and can also be used to decipher the effect of these pollutants on the human body using machine learning techniques. The results for NO2 and NO suggest a requirement to improve our models with more comprehensive data collection or advanced machine learning techniques to improve the results for these two pollutants.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"1057 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140749295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-02DOI: 10.3390/biomedinformatics4020056
Philip Drake, Ali Algaddafi, Thomas Swift, R. Abd‐Alhameed
Magnetic Field Hyperthermia is a technique where tumours are treated through an increase in local temperature upon exposure to alternating magnetic fields (AMFs) that are mediated by magnetic nano-particles (MNPs). In an AMF, these particles heat-up and kill the cells. The relationship between an AMF and the heating-rate is complex, leading to confusion when comparing data for different MNP and AMF conditions. This work allows for the thermal-response to be monitored at multiple AMF amplitudes while keeping other parameters constant. An induction-heating coil was designed based on a Zero-Voltage-Zero-Current (ZVZC) resonant circuit. The coil operates at 93 kHz with a variable DC drive-voltage (12–30 V). NEC4 software was used to model the magnetic field distribution, and MNPs were synthesised by the coprecipitation method. The magnetic field was found to be uniform at the centre of the coil and ranged from 1 kAm−1 to 12 kAm−1, depending on the DC drive-voltage. The MNPs were found to have a specific absorption rate (SAR) of 1.37 Wg−1[Fe] and 6.13 Wg−1[Fe] at 93 kHz and 2.1 kAm−1 and 12.6 kAm−1, respectively. The measured SAR value was found to be directly proportional to the product of the frequency and field-strength (SARα f Ho). This leads to the recommendation that, when comparing data from various groups, the SAR value should be normalized following this relationship and not using the more common relationship based on the square of the field intensity (SARα f Ho2).
磁场热疗是一种通过交变磁场（AMF）提高局部温度来治疗肿瘤的技术，交变磁场由磁性纳米粒子（MNPs）介导。在交变磁场中，这些粒子升温并杀死细胞。交变磁场与加热速率之间的关系非常复杂，导致在比较不同 MNP 和交变磁场条件下的数据时出现混淆。这项研究可以在保持其他参数不变的情况下，以多种 AMF 振幅监测热反应。基于零电压-零电流（ZVZC）谐振电路设计了一个感应加热线圈。线圈工作频率为 93 kHz，直流驱动电压可变（12-30 V）。使用 NEC4 软件建立磁场分布模型，并通过共沉淀法合成 MNPs。磁场在线圈中心是均匀的，范围从 1 kAm-1 到 12 kAm-1，取决于直流驱动电压。在 93 kHz 和 2.1 kAm-1 和 12.6 kAm-1 频率下，MNPs 的比吸收率（SAR）分别为 1.37 Wg-1[Fe] 和 6.13 Wg-1[Fe]。测得的 SAR 值与频率和场强的乘积（SARα f Ho）成正比。因此，建议在比较各组数据时，应根据这一关系对 SAR 值进行归一化处理，而不是使用更常见的基于场强平方的关系（SARα f Ho2）。
{"title":"Design and Modelling of an Induction Heating Coil to Investigate the Thermal Response of Magnetic Nanoparticles for Hyperthermia Applications","authors":"Philip Drake, Ali Algaddafi, Thomas Swift, R. Abd‐Alhameed","doi":"10.3390/biomedinformatics4020056","DOIUrl":"https://doi.org/10.3390/biomedinformatics4020056","url":null,"abstract":"Magnetic Field Hyperthermia is a technique where tumours are treated through an increase in local temperature upon exposure to alternating magnetic fields (AMFs) that are mediated by magnetic nano-particles (MNPs). In an AMF, these particles heat-up and kill the cells. The relationship between an AMF and the heating-rate is complex, leading to confusion when comparing data for different MNP and AMF conditions. This work allows for the thermal-response to be monitored at multiple AMF amplitudes while keeping other parameters constant. An induction-heating coil was designed based on a Zero-Voltage-Zero-Current (ZVZC) resonant circuit. The coil operates at 93 kHz with a variable DC drive-voltage (12–30 V). NEC4 software was used to model the magnetic field distribution, and MNPs were synthesised by the coprecipitation method. The magnetic field was found to be uniform at the centre of the coil and ranged from 1 kAm−1 to 12 kAm−1, depending on the DC drive-voltage. The MNPs were found to have a specific absorption rate (SAR) of 1.37 Wg−1[Fe] and 6.13 Wg−1[Fe] at 93 kHz and 2.1 kAm−1 and 12.6 kAm−1, respectively. The measured SAR value was found to be directly proportional to the product of the frequency and field-strength (SARα f Ho). This leads to the recommendation that, when comparing data from various groups, the SAR value should be normalized following this relationship and not using the more common relationship based on the square of the field intensity (SARα f Ho2).","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140755102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01DOI: 10.3390/biomedinformatics4020050
Joshua Chuah, Pingkun Yan, Ge Wang, Juergen Hahn
Background: Machine learning (ML) and artificial intelligence (AI)-based classifiers can be used to diagnose diseases from medical imaging data. However, few of the classifiers proposed in the literature translate to clinical use because of robustness concerns. Materials and methods: This study investigates how to improve the robustness of AI/ML imaging classifiers by simultaneously applying perturbations of common effects (Gaussian noise, contrast, blur, rotation, and tilt) to different amounts of training and test images. Furthermore, a comparison with classifiers trained with adversarial noise is also presented. This procedure is illustrated using two publicly available datasets, the PneumoniaMNIST dataset and the Breast Ultrasound Images dataset (BUSI dataset). Results: Classifiers trained with small amounts of perturbed training images showed similar performance on unperturbed test images compared to the classifier trained with no perturbations. Additionally, classifiers trained with perturbed data performed significantly better on test data both perturbed by a single perturbation (p-values: noise = 0.0186; contrast = 0.0420; rotation, tilt, and blur = 0.000977) and multiple perturbations (p-values: PneumoniaMNIST = 0.000977; BUSI = 0.00684) than the classifier trained with unperturbed data. Conclusions: Classifiers trained with perturbed data were found to be more robust to perturbed test data than the unperturbed classifier without exhibiting a performance decrease on unperturbed test images, indicating benefits to training with data that include some perturbed images and no significant downsides.
{"title":"Towards the Generation of Medical Imaging Classifiers Robust to Common Perturbations","authors":"Joshua Chuah, Pingkun Yan, Ge Wang, Juergen Hahn","doi":"10.3390/biomedinformatics4020050","DOIUrl":"https://doi.org/10.3390/biomedinformatics4020050","url":null,"abstract":"Background: Machine learning (ML) and artificial intelligence (AI)-based classifiers can be used to diagnose diseases from medical imaging data. However, few of the classifiers proposed in the literature translate to clinical use because of robustness concerns. Materials and methods: This study investigates how to improve the robustness of AI/ML imaging classifiers by simultaneously applying perturbations of common effects (Gaussian noise, contrast, blur, rotation, and tilt) to different amounts of training and test images. Furthermore, a comparison with classifiers trained with adversarial noise is also presented. This procedure is illustrated using two publicly available datasets, the PneumoniaMNIST dataset and the Breast Ultrasound Images dataset (BUSI dataset). Results: Classifiers trained with small amounts of perturbed training images showed similar performance on unperturbed test images compared to the classifier trained with no perturbations. Additionally, classifiers trained with perturbed data performed significantly better on test data both perturbed by a single perturbation (p-values: noise = 0.0186; contrast = 0.0420; rotation, tilt, and blur = 0.000977) and multiple perturbations (p-values: PneumoniaMNIST = 0.000977; BUSI = 0.00684) than the classifier trained with unperturbed data. Conclusions: Classifiers trained with perturbed data were found to be more robust to perturbed test data than the unperturbed classifier without exhibiting a performance decrease on unperturbed test images, indicating benefits to training with data that include some perturbed images and no significant downsides.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"138 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140758561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01DOI: 10.3390/biomedinformatics4020051
Marek Żyliński, Amir Nassibi, Edoardo Occhipinti, Adil Malik, Matteo Bermond, H. Davies, Danilo P. Mandic
Background: Ambulatory heart rate (HR) monitors that acquire electrocardiogram (ECG) or/and photoplethysmographm (PPG) signals from the torso, wrists, or ears are notably less accurate in tasks associated with high levels of movement compared to clinical measurements. However, a reliable estimation of HR can be obtained through data fusion from different sensors. These methods are especially suitable for multimodal hearable devices, where heart rate can be tracked from different modalities, including electrical ECG, optical PPG, and sounds (heart tones). Combined information from different modalities can compensate for single source limitations. Methods: In this paper, we evaluate the possible application of data fusion methods in hearables. We assess data fusion for heart rate estimation from simultaneous in-ear ECG and in-ear PPG, recorded on ten subjects while performing 5-min sitting and walking tasks. Results: Our findings show that data fusion methods provide a similar level of mean absolute error as the best single-source heart rate estimation but with much lower intra-subject variability, especially during walking activities. Conclusion: We conclude that data fusion methods provide more robust HR estimation than a single cardiovascular signal. These methods can enhance the performance of wearable devices, especially multimodal hearables, in heart rate tracking during physical activity.
{"title":"Hearables: In-Ear Multimodal Data Fusion for Robust Heart Rate Estimation","authors":"Marek Żyliński, Amir Nassibi, Edoardo Occhipinti, Adil Malik, Matteo Bermond, H. Davies, Danilo P. Mandic","doi":"10.3390/biomedinformatics4020051","DOIUrl":"https://doi.org/10.3390/biomedinformatics4020051","url":null,"abstract":"Background: Ambulatory heart rate (HR) monitors that acquire electrocardiogram (ECG) or/and photoplethysmographm (PPG) signals from the torso, wrists, or ears are notably less accurate in tasks associated with high levels of movement compared to clinical measurements. However, a reliable estimation of HR can be obtained through data fusion from different sensors. These methods are especially suitable for multimodal hearable devices, where heart rate can be tracked from different modalities, including electrical ECG, optical PPG, and sounds (heart tones). Combined information from different modalities can compensate for single source limitations. Methods: In this paper, we evaluate the possible application of data fusion methods in hearables. We assess data fusion for heart rate estimation from simultaneous in-ear ECG and in-ear PPG, recorded on ten subjects while performing 5-min sitting and walking tasks. Results: Our findings show that data fusion methods provide a similar level of mean absolute error as the best single-source heart rate estimation but with much lower intra-subject variability, especially during walking activities. Conclusion: We conclude that data fusion methods provide more robust HR estimation than a single cardiovascular signal. These methods can enhance the performance of wearable devices, especially multimodal hearables, in heart rate tracking during physical activity.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"56 44","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140795589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01DOI: 10.3390/biomedinformatics4020054
Rezaul Haque, Abdullah Al Sakib, Md Forhad Hossain, Fahadul Islam, Ferdaus Ibne Aziz, Md Redwan Ahmed, Somasundar Kannan, Ali Rohan, Md Junayed Hasan
Disease recognition has been revolutionized by autonomous systems in the rapidly developing field of medical technology. A crucial aspect of diagnosis involves the visual assessment and enumeration of white blood cells in microscopic peripheral blood smears. This practice yields invaluable insights into a patient’s health, enabling the identification of conditions of blood malignancies such as leukemia. Early identification of leukemia subtypes is paramount for tailoring appropriate therapeutic interventions and enhancing patient survival rates. However, traditional diagnostic techniques, which depend on visual assessment, are arbitrary, laborious, and prone to errors. The advent of ML technologies offers a promising avenue for more accurate and efficient leukemia classification. In this study, we introduced a novel approach to leukemia classification by integrating advanced image processing, diverse dataset utilization, and sophisticated feature extraction techniques, coupled with the development of TL models. Focused on improving accuracy of previous studies, our approach utilized Kaggle datasets for binary and multiclass classifications. Extensive image processing involved a novel LoGMH method, complemented by diverse augmentation techniques. Feature extraction employed DCNN, with subsequent utilization of extracted features to train various ML and TL models. Rigorous evaluation using traditional metrics revealed Inception-ResNet’s superior performance, surpassing other models with F1 scores of 96.07% and 95.89% for binary and multiclass classification, respectively. Our results notably surpass previous research, particularly in cases involving a higher number of classes. These findings promise to influence clinical decision support systems, guide future research, and potentially revolutionize cancer diagnostics beyond leukemia, impacting broader medical imaging and oncology domains.
在快速发展的医疗技术领域，自主系统为疾病识别带来了革命性的变化。诊断的一个重要方面是对显微外周血涂片中的白细胞进行目测和计数。这种做法能为了解病人的健康状况提供宝贵的信息，从而识别血液恶性肿瘤（如白血病）的病情。早期识别白血病亚型对于制定适当的治疗干预措施和提高患者存活率至关重要。然而，依赖视觉评估的传统诊断技术随意性大、费力且容易出错。人工智能技术的出现为更准确、更高效地进行白血病分类提供了一条大有可为的途径。在这项研究中，我们通过整合先进的图像处理、多样化的数据集利用、复杂的特征提取技术以及 TL 模型的开发，引入了一种新的白血病分类方法。为了提高以往研究的准确性，我们的方法利用 Kaggle 数据集进行二元和多元分类。广泛的图像处理涉及一种新颖的 LoGMH 方法，并辅以多种增强技术。特征提取采用 DCNN，随后利用提取的特征训练各种 ML 和 TL 模型。使用传统指标进行的严格评估显示，Inception-ResNet 的性能优越，在二分类和多分类方面的 F1 分数分别为 96.07% 和 95.89%，超过了其他模型。我们的结果明显超过了之前的研究，尤其是在涉及较多类别的情况下。这些发现有望影响临床决策支持系统，指导未来的研究，并有可能彻底改变白血病以外的癌症诊断，影响更广泛的医学成像和肿瘤学领域。
{"title":"Advancing Early Leukemia Diagnostics: A Comprehensive Study Incorporating Image Processing and Transfer Learning","authors":"Rezaul Haque, Abdullah Al Sakib, Md Forhad Hossain, Fahadul Islam, Ferdaus Ibne Aziz, Md Redwan Ahmed, Somasundar Kannan, Ali Rohan, Md Junayed Hasan","doi":"10.3390/biomedinformatics4020054","DOIUrl":"https://doi.org/10.3390/biomedinformatics4020054","url":null,"abstract":"Disease recognition has been revolutionized by autonomous systems in the rapidly developing field of medical technology. A crucial aspect of diagnosis involves the visual assessment and enumeration of white blood cells in microscopic peripheral blood smears. This practice yields invaluable insights into a patient’s health, enabling the identification of conditions of blood malignancies such as leukemia. Early identification of leukemia subtypes is paramount for tailoring appropriate therapeutic interventions and enhancing patient survival rates. However, traditional diagnostic techniques, which depend on visual assessment, are arbitrary, laborious, and prone to errors. The advent of ML technologies offers a promising avenue for more accurate and efficient leukemia classification. In this study, we introduced a novel approach to leukemia classification by integrating advanced image processing, diverse dataset utilization, and sophisticated feature extraction techniques, coupled with the development of TL models. Focused on improving accuracy of previous studies, our approach utilized Kaggle datasets for binary and multiclass classifications. Extensive image processing involved a novel LoGMH method, complemented by diverse augmentation techniques. Feature extraction employed DCNN, with subsequent utilization of extracted features to train various ML and TL models. Rigorous evaluation using traditional metrics revealed Inception-ResNet’s superior performance, surpassing other models with F1 scores of 96.07% and 95.89% for binary and multiclass classification, respectively. Our results notably surpass previous research, particularly in cases involving a higher number of classes. These findings promise to influence clinical decision support systems, guide future research, and potentially revolutionize cancer diagnostics beyond leukemia, impacting broader medical imaging and oncology domains.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"20 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140785117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-25DOI: 10.3390/biomedinformatics4020049
Maurizio Cè, Vittoria Chiarpenello, Alessandra Bubba, P. Felisaz, G. Oliva, Giovanni Irmici, M. Cellina
Introduction: Oncological patients face numerous challenges throughout their cancer journey while navigating complex medical information. The advent of AI-based conversational models like ChatGPT (San Francisco, OpenAI) represents an innovation in oncological patient management. Methods: We conducted a comprehensive review of the literature on the use of ChatGPT in providing tailored information and support to patients with various types of cancer, including head and neck, liver, prostate, breast, lung, pancreas, colon, and cervical cancer. Results and Discussion: Our findings indicate that, in most instances, ChatGPT responses were accurate, dependable, and aligned with the expertise of oncology professionals, especially for certain subtypes of cancers like head and neck and prostate cancers. Furthermore, the system demonstrated a remarkable ability to comprehend patients’ emotional responses and offer proactive solutions and advice. Nevertheless, these models have also showed notable limitations and cannot serve as a substitute for the role of a physician under any circumstances. Conclusions: Conversational models like ChatGPT can significantly enhance the overall well-being and empowerment of oncological patients. Both patients and healthcare providers must become well-versed in the advantages and limitations of these emerging technologies.
{"title":"Exploring the Role of ChatGPT in Oncology: Providing Information and Support for Cancer Patients","authors":"Maurizio Cè, Vittoria Chiarpenello, Alessandra Bubba, P. Felisaz, G. Oliva, Giovanni Irmici, M. Cellina","doi":"10.3390/biomedinformatics4020049","DOIUrl":"https://doi.org/10.3390/biomedinformatics4020049","url":null,"abstract":"Introduction: Oncological patients face numerous challenges throughout their cancer journey while navigating complex medical information. The advent of AI-based conversational models like ChatGPT (San Francisco, OpenAI) represents an innovation in oncological patient management. Methods: We conducted a comprehensive review of the literature on the use of ChatGPT in providing tailored information and support to patients with various types of cancer, including head and neck, liver, prostate, breast, lung, pancreas, colon, and cervical cancer. Results and Discussion: Our findings indicate that, in most instances, ChatGPT responses were accurate, dependable, and aligned with the expertise of oncology professionals, especially for certain subtypes of cancers like head and neck and prostate cancers. Furthermore, the system demonstrated a remarkable ability to comprehend patients’ emotional responses and offer proactive solutions and advice. Nevertheless, these models have also showed notable limitations and cannot serve as a substitute for the role of a physician under any circumstances. Conclusions: Conversational models like ChatGPT can significantly enhance the overall well-being and empowerment of oncological patients. Both patients and healthcare providers must become well-versed in the advantages and limitations of these emerging technologies.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"117 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140381646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-19DOI: 10.3390/biomedinformatics4010048
Bujar Raufi, Luca Longo
Background: Creating models to differentiate self-reported mental workload perceptions is challenging and requires machine learning to identify features from EEG signals. EEG band ratios quantify human activity, but limited research on mental workload assessment exists. This study evaluates the use of theta-to-alpha and alpha-to-theta EEG band ratio features to distinguish human self-reported perceptions of mental workload. Methods: In this study, EEG data from 48 participants were analyzed while engaged in resting and task-intensive activities. Multiple mental workload indices were developed using different EEG channel clusters and band ratios. ANOVA’s F-score and PowerSHAP were used to extract the statistical features. At the same time, models were built and tested using techniques such as Logistic Regression, Gradient Boosting, and Random Forest. These models were then explained using Shapley Additive Explanations. Results: Based on the results, using PowerSHAP to select features led to improved model performance, exhibiting an accuracy exceeding 90% across three mental workload indexes. In contrast, statistical techniques for model building indicated poorer results across all mental workload indexes. Moreover, using Shapley values to evaluate feature contributions to the model output, it was noted that features rated low in importance by both ANOVA F-score and PowerSHAP measures played the most substantial role in determining the model output. Conclusions: Using models with Shapley values can reduce data complexity and improve the training of better discriminative models for perceived human mental workload. However, the outcomes can sometimes be unclear due to variations in the significance of features during the selection process and their actual impact on the model output.
{"title":"Comparing ANOVA and PowerShap Feature Selection Methods via Shapley Additive Explanations of Models of Mental Workload Built with the Theta and Alpha EEG Band Ratios","authors":"Bujar Raufi, Luca Longo","doi":"10.3390/biomedinformatics4010048","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010048","url":null,"abstract":"Background: Creating models to differentiate self-reported mental workload perceptions is challenging and requires machine learning to identify features from EEG signals. EEG band ratios quantify human activity, but limited research on mental workload assessment exists. This study evaluates the use of theta-to-alpha and alpha-to-theta EEG band ratio features to distinguish human self-reported perceptions of mental workload. Methods: In this study, EEG data from 48 participants were analyzed while engaged in resting and task-intensive activities. Multiple mental workload indices were developed using different EEG channel clusters and band ratios. ANOVA’s F-score and PowerSHAP were used to extract the statistical features. At the same time, models were built and tested using techniques such as Logistic Regression, Gradient Boosting, and Random Forest. These models were then explained using Shapley Additive Explanations. Results: Based on the results, using PowerSHAP to select features led to improved model performance, exhibiting an accuracy exceeding 90% across three mental workload indexes. In contrast, statistical techniques for model building indicated poorer results across all mental workload indexes. Moreover, using Shapley values to evaluate feature contributions to the model output, it was noted that features rated low in importance by both ANOVA F-score and PowerSHAP measures played the most substantial role in determining the model output. Conclusions: Using models with Shapley values can reduce data complexity and improve the training of better discriminative models for perceived human mental workload. However, the outcomes can sometimes be unclear due to variations in the significance of features during the selection process and their actual impact on the model output.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"12 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140229698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-14DOI: 10.3390/biomedinformatics4010047
J. Chow, Valerie Wong, Kay Li
This review explores the transformative integration of artificial intelligence (AI) and healthcare through conversational AI leveraging Natural Language Processing (NLP). Focusing on Large Language Models (LLMs), this paper navigates through various sections, commencing with an overview of AI’s significance in healthcare and the role of conversational AI. It delves into fundamental NLP techniques, emphasizing their facilitation of seamless healthcare conversations. Examining the evolution of LLMs within NLP frameworks, the paper discusses key models used in healthcare, exploring their advantages and implementation challenges. Practical applications in healthcare conversations, from patient-centric utilities like diagnosis and treatment suggestions to healthcare provider support systems, are detailed. Ethical and legal considerations, including patient privacy, ethical implications, and regulatory compliance, are addressed. The review concludes by spotlighting current challenges, envisaging future trends, and highlighting the transformative potential of LLMs and NLP in reshaping healthcare interactions.
{"title":"Generative Pre-Trained Transformer-Empowered Healthcare Conversations: Current Trends, Challenges, and Future Directions in Large Language Model-Enabled Medical Chatbots","authors":"J. Chow, Valerie Wong, Kay Li","doi":"10.3390/biomedinformatics4010047","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010047","url":null,"abstract":"This review explores the transformative integration of artificial intelligence (AI) and healthcare through conversational AI leveraging Natural Language Processing (NLP). Focusing on Large Language Models (LLMs), this paper navigates through various sections, commencing with an overview of AI’s significance in healthcare and the role of conversational AI. It delves into fundamental NLP techniques, emphasizing their facilitation of seamless healthcare conversations. Examining the evolution of LLMs within NLP frameworks, the paper discusses key models used in healthcare, exploring their advantages and implementation challenges. Practical applications in healthcare conversations, from patient-centric utilities like diagnosis and treatment suggestions to healthcare provider support systems, are detailed. Ethical and legal considerations, including patient privacy, ethical implications, and regulatory compliance, are addressed. The review concludes by spotlighting current challenges, envisaging future trends, and highlighting the transformative potential of LLMs and NLP in reshaping healthcare interactions.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"22 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140244748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-13DOI: 10.3390/biomedinformatics4010046
Kleanthis Marios Papadopoulos, P. Barmpoutis, Tania Stathaki, V. Kepenekian, Peggy Dartigues, S. Valmary-Degano, Claire Illac-Vauquelin, G. Avérous, A. Chevallier, M. Lavérriere, L. Villeneuve, Olivier Glehen, Sylvie Isaac, J. Hommell-Fontaine, Francois Ng Kee Kwong, N. Benzerdjeb
Background: The advent of Deep Learning initiated a new era in which neural networks relying solely on Whole-Slide Images can estimate the survival time of cancer patients. Remarkably, despite deep learning’s potential in this domain, no prior research has been conducted on image-based survival analysis specifically for peritoneal mesothelioma. Prior studies performed statistical analysis to identify disease factors impacting patients’ survival time. Methods: Therefore, we introduce MPeMSupervisedSurv, a Convolutional Neural Network designed to predict the survival time of patients diagnosed with this disease. We subsequently perform patient stratification based on factors such as their Peritoneal Cancer Index and on whether patients received chemotherapy treatment. Results: MPeMSupervisedSurv demonstrates improvements over comparable methods. Using our proposed model, we performed patient stratification to assess the impact of clinical variables on survival time. Notably, the inclusion of information regarding adjuvant chemotherapy significantly enhances the model’s predictive prowess. Conversely, repeating the process for other factors did not yield significant performance improvements. Conclusions: Overall, MPeMSupervisedSurv is an effective neural network which can predict the survival time of peritoneal mesothelioma patients. Our findings also indicate that treatment by adjuvant chemotherapy could be a factor affecting survival time.
{"title":"Overall Survival Time Estimation for Epithelioid Peritoneal Mesothelioma Patients from Whole-Slide Images","authors":"Kleanthis Marios Papadopoulos, P. Barmpoutis, Tania Stathaki, V. Kepenekian, Peggy Dartigues, S. Valmary-Degano, Claire Illac-Vauquelin, G. Avérous, A. Chevallier, M. Lavérriere, L. Villeneuve, Olivier Glehen, Sylvie Isaac, J. Hommell-Fontaine, Francois Ng Kee Kwong, N. Benzerdjeb","doi":"10.3390/biomedinformatics4010046","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010046","url":null,"abstract":"Background: The advent of Deep Learning initiated a new era in which neural networks relying solely on Whole-Slide Images can estimate the survival time of cancer patients. Remarkably, despite deep learning’s potential in this domain, no prior research has been conducted on image-based survival analysis specifically for peritoneal mesothelioma. Prior studies performed statistical analysis to identify disease factors impacting patients’ survival time. Methods: Therefore, we introduce MPeMSupervisedSurv, a Convolutional Neural Network designed to predict the survival time of patients diagnosed with this disease. We subsequently perform patient stratification based on factors such as their Peritoneal Cancer Index and on whether patients received chemotherapy treatment. Results: MPeMSupervisedSurv demonstrates improvements over comparable methods. Using our proposed model, we performed patient stratification to assess the impact of clinical variables on survival time. Notably, the inclusion of information regarding adjuvant chemotherapy significantly enhances the model’s predictive prowess. Conversely, repeating the process for other factors did not yield significant performance improvements. Conclusions: Overall, MPeMSupervisedSurv is an effective neural network which can predict the survival time of peritoneal mesothelioma patients. Our findings also indicate that treatment by adjuvant chemotherapy could be a factor affecting survival time.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140247976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-06DOI: 10.3390/biomedinformatics4010043
Zain Jabbar, Peter Washington
Electronic Health Records (EHR) provide a vast amount of patient data that are relevant to predicting clinical outcomes. The inherent presence of missing values poses challenges to building performant machine learning models. This paper aims to investigate the effect of various imputation methods on the National Institutes of Health’s All of Us dataset, a dataset containing a high degree of data missingness. We apply several imputation techniques such as mean substitution, constant filling, and multiple imputation on the same dataset for the task of diabetes prediction. We find that imputing values causes heteroskedastic performance for machine learning models with increased data missingness. That is, the more missing values a patient has for their tests, the higher variance there is on a diabetes model AUROC, F1, precision, recall, and accuracy scores. This highlights a critical challenge in using EHR data for predictive modeling. This work highlights the need for future research to develop methodologies to mitigate the effects of missing data and heteroskedasticity in EHR-based predictive models.
电子健康记录（EHR）提供了大量与预测临床结果相关的患者数据。缺失值的固有存在给建立性能良好的机器学习模型带来了挑战。本文旨在研究各种估算方法对美国国立卫生研究院的 "All of Us "数据集的影响。我们在同一数据集上应用了几种归因技术，如均值替换、常数填充和多重归因，以完成糖尿病预测任务。我们发现，随着数据缺失度的增加，估算值会导致机器学习模型的异方差性能。也就是说，患者测试的缺失值越多，糖尿病模型的 AUROC、F1、精确度、召回率和准确度得分的方差就越大。这凸显了使用电子病历数据进行预测建模的一个关键挑战。这项工作凸显了未来研究的必要性，即在基于电子病历的预测模型中开发减轻缺失数据和异方差影响的方法。
{"title":"The Effect of Data Missingness on Machine Learning Predictions of Uncontrolled Diabetes Using All of Us Data","authors":"Zain Jabbar, Peter Washington","doi":"10.3390/biomedinformatics4010043","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010043","url":null,"abstract":"Electronic Health Records (EHR) provide a vast amount of patient data that are relevant to predicting clinical outcomes. The inherent presence of missing values poses challenges to building performant machine learning models. This paper aims to investigate the effect of various imputation methods on the National Institutes of Health’s All of Us dataset, a dataset containing a high degree of data missingness. We apply several imputation techniques such as mean substitution, constant filling, and multiple imputation on the same dataset for the task of diabetes prediction. We find that imputing values causes heteroskedastic performance for machine learning models with increased data missingness. That is, the more missing values a patient has for their tests, the higher variance there is on a diabetes model AUROC, F1, precision, recall, and accuracy scores. This highlights a critical challenge in using EHR data for predictive modeling. This work highlights the need for future research to develop methodologies to mitigate the effects of missing data and heteroskedasticity in EHR-based predictive models.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140261449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：