Health Information Science and Systems最新文献

White blood cells (WBC) play an effective role in the body's defense against parasites, viruses, and bacteria in the human body. Also, WBCs are categorized based on their morphological structures into various subgroups. The number of these WBC types in the blood of non-diseased and diseased people is different. Thus, the study of WBC classification is quite significant for medical diagnosis. Due to the widespread use of deep learning in medical image analysis in recent years, it has also been used in WBC classification. Moreover, the ConvMixer and Swin transformer models, recently introduced, have garnered significant success by attaining efficient long contextual characteristics. Based on this, a new multipath hybrid network is proposed for WBC classification by using ConvMixer and Swin transformer. This proposed model is called Swin Transformer and ConvMixer based Multipath mixer (SC-MP-Mixer). In the SC-MP-Mixer model, firstly, features with strong spatial details are extracted with the ConvMixer. Then Swin transformer effectively handle these features with self-attention mechanism. In addition, the ConvMixer and Swin transformer blocks consist of a multipath structure to obtain better patch representations in the SC-MP-Mixer. To test the performance of the SC-MP-Mixer, experiments were performed on three WBC datasets with 4 (BCCD), 8 (PBC) and 5 (Raabin) classes. The experimental studies resulted in an accuracy of 99.65% for PBC, 98.68% for Raabin, and 95.66% for BCCD. When compared with the studies in the literature and the state-of-the-art models, it was seen that the SC-MP-Mixer had more effective classification results.

According to the World Health Organization (WHO) data from 2000 to 2019, the number of people living with Diabetes Mellitus and Chronic Kidney Disease (CKD) is increasing rapidly. It is observed that Diabetes Mellitus increased by 70% and ranked in the top 10 among all causes of death, while the rate of those who died from CKD increased by 63% and rose from the 13th place to the 10th place. In this work, we combined the drug dose prediction model, drug-drug interaction warnings and drugs that potassium raising (K-raising) warnings to create a novel and effective ontology-based assistive prescription recommendation system for patients having both Type-2 Diabetes Mellitus (T2DM) and CKD. Although there are several computational solutions that use ontology-based systems for treatment plans for these type of diseases, none of them combine information analysis and treatment plans prediction for T2DM and CKD. The proposed method is novel: (1) We develop a new drug-drug interaction model and drug dose ontology called DIAKID (for drugs of T2DM and CKD). (2) Using comprehensive Semantic Web Rule Language (SWRL) rules, we automatically extract the correct drug dose, K-raising drugs, and drug-drug interaction warnings based on the Glomerular Filtration Rate (GFR) value of T2DM and CKD patients. The proposed work achieves very competitive results, and this is the first time such a study conducted on both diseases. The proposed system will guide clinicians in preparing prescriptions by giving necessary warnings about drug-drug interactions and doses.

Purpose: To investigate the association between DNA methylation and childhood simple obesity.

Methods: Genome-wide analysis of DNA methylation was conducted on peripheral blood samples from 41 children with simple obesity and 31 normal controls to identify differentially methylated sites (DMS). Subsequently, gene functional analysis of differentially methylated genes (DMGs) was carried out. After screening the characteristic DMGs based on specific conditions, the methylated levels of these DMS were evaluated and verified by pyrosequencing. Receiver operating characteristic (ROC) curve analysis assessed the predictive efficacy of corresponding DMGs. Finally, Pearson correlation analysis revealed the correlation between specific DMS and clinical data.

Results: The overall DNA methylation level in the obesity group was significantly lower than in normal. A total of 241 DMS were identified. Functional pathway analysis revealed that DMGs were primarily involved in lipid metabolism, carbohydrate metabolism, amino acid metabolism, human diseases, among other pathways. The characteristic DMS within the genes Transcription factor A mitochondrial (TFAM) and Piezo type mechanosensitive ion channel component 1(PIEZO1) were recognized as CpG-cg05831083 and CpG-cg14926485, respectively. Furthermore, the methylation level of CpG-cg05831083 significantly correlated with body mass index (BMI) and vitamin D.

Conclusions: Abnormal DNA methylation is closely related to childhood simple obesity. The altered methylation of CpG-cg05831083 and CpG-cg14926485 could potentially serve as biomarkers for childhood simple obesity.

Supplementary information: The online version contains supplementary material available at 10.1007/s13755-024-00275-w.

Alzheimer's disease is an incurable neurological disorder that damages cognitive abilities, but early identification reduces the symptoms significantly. The absence of competent healthcare professionals has made automatic identification of Alzheimer's disease more crucial since it lessens the amount of work for staff members and improves diagnostic outcomes. The major aim of this work is "to develop a computer diagnostic scheme that makes it possible to identify AD using the Electroencephalogram (EEG) signal". Therefore, Dynamically Stabilized Recurrent Neural Network Optimized with Artificial Gorilla Troops espoused Alzheimer's Disorder Detection using EEG signals (DSRNN-AGTO-ADD) is proposed in this paper. Here, Dynamic Context-Sensitive Filter (DCSF) is considered to eliminate the noise, and interference from the EEG signal. Then Adaptive and Concise Empirical Wavelet Transform (ACEWT) is utilized to separate the filtered signals from the frequency bands, and to feature extraction from the EEG signals. Signal's characteristics, like logarithmic bandwidth power, standard deviation, variance, kurtosis, mean energy, mean square, norm are combined to ACEWT method to create feature vectors and enhance diagnostic performance. After that, the extracted features are fed to Dynamically Stabilized Recurrent Neural Network (DSRNN) for task classification. Weight parameter of DSRNN is enhanced using Artificial Gorilla Troops Optimization Algorithm (AGTOA). The proposed DSRNN-AGTOA-ADD algorithm is activated in MATLAB. The metrics including accuracy, specificity, sensitivity, precision, computation time, ROC are examined for AD diagnosis. The performance of the proposed DSRNN-AGTOA-ADD approach attains 12.98%, 5.98% and 23.45% high specificity; 29.98%, 23.32% and 19.76% lower computation Time and 29.29%, 8.365%, 8.551% and 7.915% higher ROC compared with the existing methods.

Purpose: The main aim of our study was to explore the utility of artificial intelligence (AI) in diagnosing autism spectrum disorder (ASD). The study primarily focused on using machine learning (ML) and deep learning (DL) models to detect ASD potential cases by analyzing text inputs, especially from social media platforms like Twitter. This is to overcome the ongoing challenges in ASD diagnosis, such as the requirement for specialized professionals and extensive resources. Timely identification, particularly in children, is essential to provide immediate intervention and support, thereby improving the quality of life for affected individuals.

Methods: We employed natural language processing (NLP) techniques along with ML models like decision trees, extreme gradient boosting (XGB), k-nearest neighbors algorithm (KNN), and DL models such as recurrent neural networks (RNN), long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), bidirectional encoder representations from transformers (BERT and BERTweet). We extracted a dataset of 404,627 tweets from Twitter users using the platform's API and classified them based on whether they were written by individuals claiming to have ASD (ASD users) or by those without ASD (non-ASD users). From this dataset, we used a subset of 90,000 tweets (45,000 from each classification group) for the training and testing of these models.

Results: The application of our AI models yielded promising results, with the predictive model reaching an accuracy of almost 88% when classifying texts that potentially originated from individuals with ASD.

Conclusion: Our research demonstrated the potential of using AI, particularly DL models, in enhancing the accuracy of ASD detection and diagnosis. This innovative approach signifies the critical role AI can play in advancing early diagnostic techniques, enabling better patient outcomes and underlining the importance of early identification of ASD, especially in children.

Diagnosis prediction, a key factor in enhancing healthcare efficiency, remains a focal point in clinical decision support research. However, the time-series, sparse and multi-noise characteristics of electronic health record (EHR) data make it a great challenge. Existing methods commonly address these issues using RNNs and incorporating medical prior knowledge from medical knowledge bases, but they neglect the local spatial characteristics and spatial-temporal correlation of the data. Consequently, we propose MDPG, a diagnosis prediction model based on patient knowledge graphs. Initially, we represent the electronic visit records of patients as a patient-centered temporal knowledge graph, capturing the local spatial structure and temporal characteristics of the visit information. Subsequently, we design the spatial graph convolution block, temporal self-attention block, and spatial-temporal synchronous graph convolution block to capture the spatial, temporal, and spatial-temporal correlations embedded in them, respectively. Ultimately, we accomplish the prediction of patients' future states through multi-label classification. We conduct comprehensive experiments on two real-world datasets independently and evaluate the results using visit-level precision@k and code-level accuracy@k metrics. The experimental results demonstrate that MDPG outperforms all baseline models, yielding the best performance.

Advances in computer science in combination with the next-generation sequencing have introduced a new era in biology, enabling advanced state-of-the-art analysis of complex biological data. Bioinformatics is evolving as a union field between computer Science and biology, enabling the representation, storage, management, analysis and exploration of many types of data with a plethora of machine learning algorithms and computing tools. In this study, we used machine learning algorithms to detect differentially expressed genes between different types of cancer and showing the existence overlap to final results from RNA-sequencing analysis. The datasets were obtained from the National Center for Biotechnology Information resource. Specifically, dataset GSE68086 which corresponds to PMID:200,068,086. This dataset consists of 171 blood platelet samples collected from patients with six different tumors and healthy individuals. All steps for RNA-sequencing analysis (preprocessing, read alignment, transcriptome reconstruction, expression quantification and differential expression analysis) were followed. Machine Learning- based Random Forest and Gradient Boosting algorithms were applied to predict significant genes. The Rstudio statistical tool was used for the analysis.

Electroencephalograph (EEG) has been a reliable data source for building brain-computer interface (BCI) systems; however, it is not reasonable to use the feature vector extracted from multiple EEG channels and frequency bands to perform recognition directly due to the two deficiencies. One is that EEG data is weak and non-stationary, which easily causes different EEG samples to have different quality. The other is that different feature dimensions corresponding to different brain regions and frequency bands have different correlations to a certain mental task, which is not sufficiently investigated. To this end, a Joint Sample and Feature importance Assessment (JSFA) model was proposed to simultaneously explore the different impacts of EEG samples and features in mental state recognition, in which the former is based on the self-paced learning technique while the latter is completed by the feature self-weighting technique. The efficacy of JSFA is extensively evaluated on two EEG data sets, i.e., SEED-IV and SEED-VIG. One is a classification task for emotion recognition and the other is a regression task for driving fatigue detection. Experimental results demonstrate that JSFA can effectively identify the importance of different EEG samples and features, leading to enhanced recognition performance of corresponding BCI systems.

Purpose: The purpose of this work is to analyse the combined impacts of birth weight and nutritional status on development and recovery of various types of diseases. This work aims to computationally establish the facts about the effects of individual birth weight-nutritional status pairs on disease development and disease recovery.

Methods: This work designs a computational model to analyze the impact of birth weight-nutritional status pairs on disease development and disease recovery. Our model works in two phases. The first phase finds the best machine learning model to predict birth weight from "Child Birth Weight Dataset" available at IEEE Dataport (https://dx.doi.org/10.21227/dvd4-3232). The second phase combines the predicted birth weight labels with nutritional status labels and establishes the effects using differential equations.

Results: The experimental results find Gradient boosting (GB) to work the best with Information gain (IGT) and Support Vector Machine (SVM) with Chi-square test (CST) for predicting the birth weights. The simulated results establish that "normal birth weight and normal nutritional status" is the best pair for resisting disease development as well as enhancing disease recovery. The results also depict that "low birth weight and malnutrition" is the worst pair for disease development while "high birth weight and malnutrition" is the worst combination for disease recovery.

Conclusion: The findings computationally establish the facts about the effects of birth weight-nutritional status pairs on disease development and disease recovery. As a social implication, this study can spread awareness about the importance of birth weight and nutritional status. The outcome can be helpful for the concerned authority in making decisions on healthcare cost and expenditure.

Cardiovascular disease management often involves adjusting medication dosage based on changes in electrocardiogram (ECG) signals' waveform and rhythm. However, the diagnostic utility of ECG signals is often hindered by various types of noise interference. In this work, we propose a novel filter based on a multi-engine evolution framework named MEAs-Filter to address this issue. Our approach eliminates the need for predefined dimensions and allows adaptation to diverse ECG morphologies. By leveraging state-of-the-art optimization algorithms as evolution engine and incorporating prior information inputs from classical filters, MEAs-Filter achieves superior performance while minimizing order. We evaluate the effectiveness of MEAs-Filter on a real ECG database and compare it against commonly used filters such as the Butterworth, Chebyshev filters, and evolution algorithm-based (EA-based) filters. The experimental results indicate that MEAs-Filter outperforms other filters by achieving a reduction of approximately 30% to 60% in terms of the loss function compared to the other algorithms. In denoising experiments conducted on ECG waveforms across various scenarios, MEAs-Filter demonstrates an improvement of approximately 20% in signal-to-noise (SNR) ratio and a 9% improvement in correlation. Moreover, it does not exhibit higher losses of the R-wave compared to other filters. These findings highlight the potential of MEAs-Filter as a valuable tool for high-fidelity extraction of ECG signals, enabling accurate diagnosis in the field of cardiovascular diseases.