Health Information Science and Systems最新文献

Recognizing emotions accurately in real life is crucial in human-computer interaction (HCI) systems. Electroencephalogram (EEG) signals have been extensively employed to identify emotions. The researchers have used several EEG-based emotion identification datasets to validate their proposed models. In this paper, we have employed a novel metaheuristic optimization approach for accurate emotion classification by applying it to select both channel and rhythm of EEG data. In this work, we have proposed the particle swarm with visit table strategy (PS-VTS) metaheuristic technique to improve the effectiveness of EEG-based human emotion identification. First, the EEG signals are denoised using a low pass filter, and then rhythm extraction is done using discrete wavelet transform (DWT). The continuous wavelet transform (CWT) approach transforms each rhythm signal into a rhythm image. The pre-trained MobilNetv2 model has been pre-trained for deep feature extraction, and a support vector machine (SVM) is used to classify the emotions. Two models are developed for optimal channels and rhythm sets. In Model 1, optimal channels are selected separately for each rhythm, and global optima are determined in the optimization process according to the best channel sets of the rhythms. The best rhythms are first determined for each channel, and then the optimal channel-rhythm set is selected in Model 2. Our proposed model obtained an accuracy of 99.2871% and 97.8571% for the classification of HA (high arousal)-LA (low arousal) and HV (high valence)-LV (low valence), respectively with the DEAP dataset. Our generated model obtained the highest classification accuracy compared to the previously reported methods.

Background: Prognostic models of glioma have been the focus of many studies. However, most of them are based on Western populations. Additionally, because of the complexity of healthcare data in China, it is important to select a suitable model based on existing clinical data. This study aimed to develop and independently validate a nomogram for predicting the overall survival (OS) with newly diagnosed grade II/III astrocytoma after surgery.

Methods: Data of 472 patients with astrocytoma (grades II-III) were collected from Qilu Hospital as training cohort while data of 250 participants from Linyi People's Hospital were collected as validation cohort. Cox proportional hazards model was used to construct the nomogram and individually predicted 1-, 3-, and 5-year survival probabilities. Calibration ability, and discrimination ability were analyzed in both training and validation cohort.

Results: Overall survival was negatively associated with histopathology, age, subtotal resection, multiple tumors, lower KPS and midline tumors. Internal validation and external validation showed good discrimination (The C-index for 1-, 3-, and 5-year survival were 0.791, 0.748, 0.733 in internal validation and 0.754, 0.735, 0.730 in external validation, respectively). The calibration curves showed good agreement between the predicted and actual 1-, 3-, and 5-year OS rates.

Conclusion: This is the first nomogram study that integrates common clinicopathological factors to provide an individual probabilistic prognosis prediction for Chinese Han patients with astrocytoma (grades II-III). This model can serve as an easy-to-use tool to advise patients and establish optimized surveillance approaches after surgery.

Supplementary information: The online version contains supplementary material available at 10.1007/s13755-023-00223-0.

Backgrounds: Dyslipidemia is a prominent risk factor for cardiovascular diseases and one of the primary independent modifiable factors of diabetes and stroke. Statins can significantly improve the prognosis of dyslipidemia, but its side effects cannot be ignored. Traditional Chinese Medicine (TCM) has been used in clinical practice for more than 2000 years in China and has certain traits in treating dyslipidemia with little side effect. Previous research has shown that Mutual Obstruction of Phlegm and Stasis (MOPS) is the most common dyslipidemia type classified in TCM. However, how to compose diagnostic factors in TCM into diagnostic rules relies heavily on the doctor's experience, falling short in standardization and objectiveness. This is a limit for TCM to play its advantages of treating dyslipidemia with MOPS.

Methods: In this study, the syndrome diagnosis in TCM was transformed into the prediction and classification problem in artificial intelligence The deep learning method was employed to build the classification prediction models for dyslipidemia. The models were built and trained with a large amount of multi-centered clinical data on MOPS. The optimal model was screened out by evaluating the performance of prediction models through loss, accuracy, precision, recall, confusion matrix, PR and ROC curve (including AUC).

Results: A total of 20 models were constructed through the deep learning method. All of them performed well in the prediction of dyslipidemia with MOPS. The model-11 is the optimal model. The evaluation indicators of model-11 are as follows: The true positive (TP), false positive (FP), true negative (TN) and false negative (FN) are 51, 15, 129, and 9, respectively. The loss is 0.3241, accuracy is 0.8672, precision is 0.7138, recall is 0.8286, and the AUC is 0.9268. After screening through 89 diagnostic factors of TCM, we identified 36 significant diagnosis factors for dyslipidemia with MOPS. The most outstanding diagnostic factors from the importance were dark purple tongue, slippery pulse and slimy fur, etc.

Conclusions: This study successfully developed a well-performing classification prediction model for dyslipidemia with MOPS, transforming the syndrome diagnosis problem in TCM into a prediction and classification problem in artificial intelligence. Patients with dyslipidemia of MOPS can be accurately recognized through limited information from patients. We also screened out significant diagnostic factors for composing diagnostic rules of dyslipidemia with MOPS. The study is an avant-garde attempt at introducing the deep-learning method into the research of TCM, which provides a useful reference for the extension of deep learning method to other diseases and the construction of disease diagnosis model in TCM, contributing to the standardization and objectiveness of TCM diagnosis.

As one of the key methods of Traditional Chinese Medicine inspection, tongue diagnosis manifests the advantages of simplicity and directness. Sublingual veins can provide essential information about human health. In order to automate tongue diagnosis, sublingual veins segmentation has become one important issue in the field of Chinese medicine medical image processing. At present, the primary methods for sublingual veins segmentation are traditional feature engineering methods and the feature representation methods represented by deep learning. The former, which mainly based on colour space, belongs to unsupervised classification method. The latter, which includes U-Net and other deep neural network models, belongs to supervised classification method. Current feature engineering methods can only capture low dimensional information, which makes it difficult to extract efficient features for sublingual veins. On the other hand, current deep learning methods use down-sampling structures, which manifest weak robustness and low accuracy. So, it is difficult for current segmentation approaches to recognize tiny branches of sublingual veins. To overcome the above limits, this paper proposes a novel two-stage semantic segmentation method for sublingual veins. In the first stage, a fully convolutional network without down-sampling is used to realize the accurate segmentation of the tongue that includes the sublingual veins to be segmented in the next stage. During the tongue segmentation, the proposed networks can effectively reduce the loss of medical images spatial feature information. At the same time, in order to expand the receptive field, the dilated convolution has been introduced to the proposed networks, which can capture multi-scale information of segmentation images. In the second stage, another fully convolutional network has been used to segment the sublingual veins on the base of the results from the first stage. In this model, proper dilated convolutional rates have been selected to avoid gridding issue. In order to keep the quality of the images to be segmented, several particular data pre-processing and post-processing have been used, which includes specular highlight removal, data augmentation, erosion and dilation. Finally, in order to evaluate the performance of the proposed model, segmentation results have been compared with the state-of-the-art methods on the base of the dataset from Shanghai University of Traditional Chinese Medicine. The effectiveness of sublingual veins segmentation has been proved.

The growth of databases in the healthcare domain opens multiple doors for machine learning and artificial intelligence technology. Many medical devices are available in the medical field; however, medical errors remain a severe challenge. Different algorithms are developed to identify and solve medical errors, such as detecting anomalous readings, anomalous health conditions of a patient, etc. However, they fail to answer why those entries are considered an anomaly. This research gap leads to an outlying aspect mining problem. The problem of outlying aspect mining aims to discover the set of features (a.k.a subspace) in which the given data point is dramatically different than others. In this paper, we present a framework that detects anomalies in healthcare data and then provides an explanation of anomalies. This paper aims to effectively and efficiently detect anomalies and explain why they are considered anomalies by detecting outlying aspects. First, we re-introduced four anomaly detection techniques and outlying aspect mining algorithms. Then, we evaluate the performance of anomaly detection techniques and choose the best anomaly detection algorithm. Later, we detect the top k anomaly as a query and detect their outlying aspect. Lastly, we evaluate their performance on 16 real-world healthcare datasets. The experimental results show that the latest isolation-based outlying aspect mining measure, SiNNE, has outstanding performance on this task and has promising results.

Chronic metabolic diseases arise from changes in metabolic fluxes through biomolecular pathways and gene networks accumulated over the lifetime of an individual. While clinical and biochemical profiles present just real-time snapshots of the patients' health, efficient computation models of the pathological disturbance of biomolecular processes are required to achieve individualized mechanistic insights into disease progression. Here, we describe the Generalized metabolic flux analysis (GMFA) for addressing this gap. Suitably grouping individual metabolites/fluxes into pools simplifies the analysis of the resulting more coarse-grain network. We also map non-metabolic clinical modalities onto the network with additional edges. Instead of using the time coordinate, the system status (metabolite concentrations and fluxes) is quantified as function of a generalized extent variable (a coordinate in the space of generalized metabolites) that represents the system's coordinate along its evolution path and evaluates the degree of change between any two states on that path. We applied GMFA to analyze Type 2 Diabetes Mellitus (T2DM) patients from two cohorts: EVAS (289 patients from Singapore) and NHANES (517) from the USA. Personalized systems biology models (digital twins) were constructed. We deduced disease dynamics from the individually parameterized metabolic network and predicted the evolution path of the metabolic health state. For each patient, we obtained an individual description of disease dynamics and predict an evolution path of the metabolic health state. Our predictive models achieve an ROC-AUC in the range 0.79-0.95 (sensitivity 80-92%, specificity 62-94%) in identifying phenotypes at the baseline and predicting future development of diabetic retinopathy and cataract progression among T2DM patients within 3 years from the baseline. The GMFA method is a step towards realizing the ultimate goal to develop practical predictive computational models for diagnostics based on systems biology. This tool has potential use in chronic disease management in medical practice.

Supplementary information: The online version contains supplementary material available at 10.1007/s13755-023-00218-x.

The magnetic resonance (MR) images of fetuses make it possible for doctors to detect out pathological fetal brains in early stages. Brain tissue segmentation is prerequisite for making brain morphology and volume analyses. nnU-Net is an automatic segmentation method based on deep learning. It can adaptively configure itself, so as to adapt to a specific task via preprocessing, network architecture, training, and post-processing. Therefore, we adapt nnU-Net to segment seven types of fetal brain tissues, including external cerebrospinal fluid, gray matter, white matter, ventricle, cerebellum, deep gray matter, and brainstem. With regard to the characteristics of the FeTA 2021 data, some adjustments are made to the original nnU-Net, so that it can segment seven types of fetal brain tissues precisely as far as possible. The average segmentation results on FeTA 2021 training data demonstrate that our advanced nnU-Net is superior to the peers including SegNet, CoTr, AC U-Net and ResUnet. Its average segmentation results are 0.842, 11.759 and 0.957 in terms of Dice, HD95 and VS criteria. Moreover, the experimental results on FeTA 2021 test data further demonstrate that our advanced nnU-Net has obtained good segmentation performance of 0.774, 14.699 and 0.875 in terms of Dice, HD95 and VS, ranked the third in FeTA 2021 challenge. Our advanced nnU-Net realized the task for segmenting the fetal brain tissues using MR images of different gestational ages, which can help doctors to make correct and seasonable diagnoses.

Purpose: Ultrasound image acquisition has the advantages of being low cost, rapid, and non-invasive, and it does not produce radiation. Currently, ultrasound is widely used in the diagnosis of liver tumors. However, owing to the complex presentation and diverse features of benign and malignant liver tumors, accurate diagnosis of liver tumors using ultrasound is difficult even for experienced radiologists. In recent years, artificial intelligence-assisted diagnosis has proven to provide effective support to radiologists. However, there is room for further improvement in the existing ultrasound artificial intelligence diagnostic model of liver tumor. First, the image diagnostic model may not fully consider relevant clinical data in the decision-making process. Second, owing to the difficulty in collecting biopsy pathology and physician-labeled ultrasound data of liver tumors, training datasets are usually small, and commonly used large neural networks tend to overfit on small datasets, which seriously affects the generalization of the model.

Methods: In this study, we propose a deep learning-assisted diagnosis model called USC-ENet, which integrates B-mode ultrasound features of liver tumors and clinical data of patients, and we design a small neural network specifically for small-scale medical images combined with an attention mechanism.

Results and conclusion: Real data from 542 patients with liver tumors (N = 2168 images) are used during model training and validation. Experiments show that USC-ENet can achieve a good classification effect (area under the curve = 0.956, sensitivity = 0.915, and specificity = 0.880) after small-scale data training, and it has certain interpretability, showing good potential for clinical adoption. In conclusion, our model provides not only a reliable second opinion for radiologists but also a reference for junior radiologists who lack clinical experience.

Purpose: Cardiotocography (CTG), which measures uterine contraction (UC) and fetal heart rate (FHR), is a crucial tool for assessing fetal health during pregnancy. However, traditional computerized cardiotocography (cCTG) approaches have non-negligible calibration errors in feature extraction and heavily rely on the expertise and prior experience to define diagnostic features from CTG or FHR signals. Although previous works have studied deep learning methods for extracting CTG or FHR features, these methods still neglect the clinical information of pregnant women.

Methods: In this paper, we proposed a multimodal deep learning architecture (MMDLA) for intelligent antepartum fetal monitoring that is capable of performing automatic CTG feature extraction, fusion with clinical data and classification. The multimodal feature fusion was achieved by concatenating high-level CTG features, which were extracted from preprocessed CTG signals via a convolution neural network (CNN) with six convolution layers and five fully connected layers, and the clinical data of pregnant women. Eventually, light gradient boosting machine (LGBM) was implemented as fetal status assessment classifier. The effectiveness of MMDLA was evaluated using a dataset of 16,355 cases, each of which includes FHR signal, UC signal and pertinent clinical data like maternal age and gestational age.

Results: With an accuracy of 90.77% and an area under the curve (AUC) value of 0.9201, the multimodal features performed admirably. The data imbalance issue was also effectively resolved by the LGBM classifier, with a normal-F1 value of 0.9376 and an abnormal-F1 value of 0.8223.

Conclusion: In summary, the proposed MMDLA is conducive to the realization of intelligent antepartum fetal monitoring.

Purpose: Telemedicine are experiencing an unprecedented boom globally since the beginning of the COVID-19 pandemic. As the most vulnerable groups amid COVID-19, the digital delivery of healthcare poses great challenges to the elderly population, caregiver, health service providers, and health policy makers. To bridge the service delivery gaps between the telemedicine demand side and supply side, explore evidence-based approach for integrated care, address challenges for aging policy, and build foundation for the development of data-driven and community-based telemedicine, our R&D team applied translational research to design and develop telemedicine "SMART" for enhancing elderly mental health wellbeing amid COVID-19. Our aim is to investigate the preparedness mechanisms of mental health disease including response, intervention, and connection these three healthcare delivery pipelines with the collection, consolidation, and synergy of heath parameters and social determinants, using data analytics approach to achieve Evidence-Based Medicine (EBM).

Methods: A mix of quantitative and qualitative research design for scientifically rigorous consultation and analysis was conducted from Jan 2020 to June 2021 in Hong Kong. An exploratory and descriptive qualitative design was used in this study. The data were collected through focus group discussions conducted from elderly and their caregivers living in 10 main districts of Hong Kong. Our research pilot tested "SMART" targeting for elderly with mental health improvement needs. Baseline questionnaire with 110 tele-medicine product users includes questions on demographic information, self-rated mental health digital adoption. The follow-up five focus group discussions with 57 users (elderly and their caregivers) further explore the social determinants of telemedicine transformation and help propose the integrated telemedicine paradigm shift framework establishment, development, and enhancement.

Results: Grounded on the baseline needs assessment and feedbacks collected, it is evident that multi-dimensional health information from the four various streams (community, clinic, home, remote) and customized digital health solutions are playing a key role in addressing elderly mental health digital service needs and bridging digital divide. The designed tele-medicine product lines up health service provider (supplier side) and elderly specific needs (demand side) with our three-level design, enables elderly and their families to follow and control their own health management and connect with the service provider, community of practice (CoP), and health policy makers.

Conclusion: It's beneficial to involve elderly and gerontechnology stakeholders as part of Community-Based Participatory Research (CBPR) before and throughout the developing and delivery phases an integrated and age-friendly digital intervention. The challenges in