Technology and Health Care最新文献

Background: In practice, the collected datasets for data analysis are usually incomplete as some data contain missing attribute values. Many related works focus on constructing specific models to produce estimations to replace the missing values, to make the original incomplete datasets become complete. Another type of solution is to directly handle the incomplete datasets without missing value imputation, with decision trees being the major technique for this purpose.

Objective: To introduce a novel approach, namely Deep Learning-based Decision Tree Ensembles (DLDTE), which borrows the bounding box and sliding window strategies used in deep learning techniques to divide an incomplete dataset into a number of subsets and learning from each subset by a decision tree, resulting in decision tree ensembles.

Method: Two medical domain problem datasets contain several hundred feature dimensions with the missing rates of 10% to 50% are used for performance comparison.

Results: The proposed DLDTE provides the highest rate of classification accuracy when compared with the baseline decision tree method, as well as two missing value imputation methods (mean and k-nearest neighbor), and the case deletion method.

Conclusion: The results demonstrate the effectiveness of DLDTE for handling incomplete medical datasets with different missing rates.

Background: With the end of the coronavirus disease 2019 (COVID-19) pandemic, it becomes intriguing to observe the impact of innovative digital technologies on the diagnosis and management of diseases, in order to improve clinical outcomes for patients.

Objective: The research aims to enhance diagnostics, prediction, and personalized treatment for patients across three classes of clinical severity (mild, moderate, and severe). What sets this study apart is its innovative approach, wherein classification extends beyond mere disease presence, encompassing the classification of disease severity. This novel perspective lays the foundation for a crucial decision support system during patient triage.

Methods: An artificial neural network, as a deep learning technique, enabled the development of a complex model based on the analysis of data collected during the process of diagnosing and treating 1000 patients at the Tešanj General Hospital, Bosnia and Herzegovina.

Results: The final model achieved a classification accuracy of 82.4% on the validation data set, which testifies to the successful application of the artificial neural network in the classification of clinical outcomes and therapy in patients infected with viral infections.

Conclusion: The results obtained show that expert systems are valuable tools for decision support in healthcare in communities with limited resources and increased demands. The research has the potential to improve patient care for future epidemics and pandemics.

Background: Improved access to media and medical knowledge has elicited stronger public health awareness.

Objective: This study developed a smart drug interaction reminder system for patients to increase knowledge and reduce nurse workload.

Methods: This study used a single-group pre-test/post-test design and applied mining techniques to analyze the weight and probability of interaction among various medicines. Data were collected from 258 participants at a teaching hospital in northern Taiwan using convenience sampling. An app was used to give patients real-time feedback to obtain access to information and remind them of their health issues. In addition to guiding the patients on medications, this app measured the nurses' work satisfaction and patients' knowledge of drug interaction.

Results: The results indicate that using information technology products to assist the app's real-time feedback system promoted nurses' work satisfaction, improved their health education skills, and helped patients to better understand drug interactions.

Conclusion: Using information technology to provide patients with real-time inquiring functions has a significant effect on nurses' load reduction. Thus, smart drug interaction reminder system apps can be considered suitable nursing health education tools and the SDINRS app can be integrated into quantitative structure-activity relationship intelligence in the future.

Background: Day surgery is a new surgical model in which patients complete the admission, surgery, and discharge on the same day.

Objective: The present study aimed to explore the effect of pre-admission "quasi-collective" health education for patients with ophthalmic day surgery.

Methods: For this study, a total of 200 patients undergoing ophthalmic day surgery from February 2019 to December 2019 were enrolled as the research subjects. The patients were divided randomly into the observation group and the control group, with 100 cases in each group. For the control group, conventional health education was conducted after admission. On the day of admission, the admission education and peri-operative health education were performed. For the observation group, pre-admission health education was provided to the patients, and detailed education on the admission instructions, pre-operative precautions, and simulation of the intra-operative process were given by the medical staff. On the day of admission, the understanding of the education was evaluated, and any weaknesses in the health education were addressed. The anxiety status, method of handwashing, method of administering the drug to the eye, preoperative preparations, intra-operative training, preoperative medication, diet guidance, and postoperative care were compared between the two groups of patients.

Results: Before discharge, there were significant differences in the anxiety scores, impact, and satisfaction of health education between the two groups of patients, all of which were statistically significant (P< 0.05).

Conclusion: The pre-admission "quasi-collective" health education for patients undergoing day surgery in ophthalmology was better than conventional health education.

Background: Early identification of sepsis has been shown to significantly improve patient prognosis.

Objective: Therefore, the aim of this meta-analysis is to systematically evaluate the diagnostic efficacy of machine-learning algorithms for sepsis prediction.

Methods: Systematic searches were conducted in PubMed, Embase and Cochrane databases, covering literature up to December 2023. The keywords included machine learning, sepsis and prediction. After screening, data were extracted and analysed from studies meeting the inclusion criteria. Key evaluation metrics included sensitivity, specificity and the area under the curve (AUC) for diagnostic accuracy.

Results: The meta-analysis included a total of 21 studies with a data sample size of 4,158,941. Overall, the pooled sensitivity was 0.82 (95% confidence interval [CI] = 0.70-0.90; P< 0.001; I2= 99.7%), the specificity was 0.91 (95% CI = 0.86-0.94; P< 0.001; I2= 99.9%), and the AUC was 0.94 (95% CI = 0.91-0.96). The subgroup analysis revealed that in the emergency department setting (6 studies), the pooled sensitivity was 0.79 (95% CI = 0.68-0.87; P< 0.001; I2= 99.6%), the specificity was 0.94 (95% CI 0.90-0.97; P< 0.001; I2= 99.9%), and the AUC was 0.94 (95% CI = 0.92-0.96). In the Intensive Care Unit setting (11 studies), the sensitivity was 0.91 (95% CI = 0.75-0.97; P< 0.001; I2= 98.3%), the specificity was 0.85 (95% CI = 0.75-0.92; P< 0.001; I2= 99.9%), and the AUC was 0.93 (95% CI = 0.91-0.95). Due to the limited number of studies in the in-hospital and mixed settings (n< 3), no pooled analysis was performed.

Conclusion: Machine-learning algorithms have demonstrated excellent diagnostic accuracy in predicting the occurrence of sepsis, showing potential for clinical application.

Background: Matrine has been reported inhibitory effects on ovarian cancer (OC) cell progression, development, and apoptosis. However, the molecular targets of matrine against OC and the underlying mechanisms of action remain elusive.

Objective: This study endeavors to unveil the potential targets of matrine against OC and to explore the intricate relationships between these targets and the pathogenesis of OC.

Methods: The effects of matrine on the OC cells (A2780 and AKOV3) viability, apoptosis, migration, and invasion was investigated through CCK-8, flow cytometry, wound healing, and Transwell analyses, respectively. Next, Matrine-related targets, OC-related genes, and ribonucleic acid (RNA) sequence data were harnessed from publicly available databases. Differentially expressed analyses, protein-protein interaction (PPI) network, and Venn diagram were involved to unravel the core targets of matrine against OC. Leveraging the GEPIA database, we further validated the expression levels of these core targets between OC cases and controls. Mendelian randomization (MR) study was implemented to delve into potential causal associations between core targets and OC. The AutoDock software was used for molecular docking, and its results were further validated using RT-qPCR in OC cell lines.

Results: Matrine reduced the cell viability, migration, invasion and increased the cell apoptosis of A2780 and AKOV3 cells (P< 0.01). A PPI network with 578 interactions among 105 candidate targets was developed. Finally, six core targets (TP53, CCND1, STAT3, LI1B, VEGFA, and CCL2) were derived, among which five core targets (TP53, CCND1, LI1B, VEGFA, and CCL2) differential expressed in OC and control samples were further picked for MR analysis. The results revealed that CCND1 and TP53 were risk factors for OC. Molecular docking analysis demonstrated that matrine had good potential to bind to TP53, CCND1, and IL1B. Moreover, matrine reduced the expression of CCND1 and IL1B while elevating P53 expression in OC cell lines.

Conclusions: We identified six matrine-related targets against OC, offering novel insights into the molecular mechanisms underlying the therapeutic effects of matrine against OC. These findings provide valuable guidance for developing more efficient and targeted therapeutic approaches for treating OC.

Background: Autism Spectrum Disorder (ASD) is a condition with social interaction, communication, and behavioral difficulties. Diagnostic methods mostly rely on subjective evaluations and can lack objectivity. In this research Machine learning (ML) and deep learning (DL) techniques are used to enhance ASD classification.

Objective: This study focuses on improving ASD and TD classification accuracy with a minimal number of EEG channels. ML and DL models are used with EEG data, including Mu Rhythm from the Sensory Motor Cortex (SMC) for classification.

Methods: Non-linear features in time and frequency domains are extracted and ML models are applied for classification. The EEG 1D data is transformed into images using Independent Component Analysis-Second Order Blind Identification (ICA-SOBI), Spectrogram, and Continuous Wavelet Transform (CWT).

Results: Stacking Classifier employed with non-linear features yields precision, recall, F1-score, and accuracy rates of 78%, 79%, 78%, and 78% respectively. Including entropy and fuzzy entropy features further improves accuracy to 81.4%. In addition, DL models, employing SOBI, CWT, and spectrogram plots, achieve precision, recall, F1-score, and accuracy of 75%, 75%, 74%, and 75% respectively. The hybrid model, which combined deep learning features from spectrogram and CWT with machine learning, exhibits prominent improvement, attained precision, recall, F1-score, and accuracy of 94%, 94%, 94%, and 94% respectively. Incorporating entropy and fuzzy entropy features further improved the accuracy to 96.9%.

Conclusions: This study underscores the potential of ML and DL techniques in improving the classification of ASD and TD individuals, particularly when utilizing a minimal set of EEG channels.

Background: The global implementation of Electronic Health Records has significantly enhanced the quality of medical care and the overall delivery of public health services. The incorporation of Evidence-Based Medicine offers numerous benefits and enhances the efficacy of decision-making in areas such as prevention, prognosis, diagnosis, and therapeutic approaches.

Objective: The objective of this paper is to propose an architectural design of an Evidence-Based Medicine information system based on the Electronic Health Record, taking into account the existing and future level of interoperability of health information systems in Greece.

Methods: A study of the suggested evidence-based medicine architectures found in the existing literature was conducted. Moreover, the interoperability architecture of health information systems in Greece was analyzed. The architecture design reviewed by specialized personnel and their recommendations were incorporated into the final design of the proposed architecture.

Results: The proposed integrated architecture of an Evidence-Based Medicine system based on the Electronic Health Record integrates and utilizes citizens' health data while leveraging the existing knowledge available in the literature.

Conclusions: Taking into consideration the recently established National Interoperability Framework, which aligns with the European Interoperability Framework, the proposed realistic architectural approach contributes to improving the quality of healthcare provided through the ability to make safe, timely and accurate decisions by physicians.

Background: The Stepped Care Model (SCM) is an evidence-based treatment approach that tailors treatment intensity based on patients' health status, aiming to achieve the most positive treatment outcomes with the least intensive and cost-effective interventions. Currently, the effectiveness of the Stepped Care Model in postoperative rehabilitation for TKA (Total Knee Arthroplasty) patients has not been reported.

Objective: The present study aimed to investigate whether the stepped care model could improve early-stage self-report quality of life and knee function after total knee arthroplasty via a prospective randomized controlled design.

Methods: It was a mono-center, parallel-group, open-label, prospective randomized controlled study. Patients who aging from 60-75 years old as well as underwent unilateral primary total knee arthroplasty due to end-stage knee osteoarthritis between 2020.06 to 2022.02 were enrolled. Participants were randomized and arranged into two groups in a 1:1 allocation. The control group was given traditional rehabilitation guidance, while the stepped care model group was given continued stepped care. Hospital for special surgery knee score, daily living ability (ADL), knee flexion range, and adverse events at 1, 3, and 6 months after total knee arthroplasty were recorded.

Results: 88 patients proceeded to the final analysis. There was no significant difference of age, gender, length of stay, BMI, and educational level between the two groups at the baseline. After specific stepped care model interventions, patients showed significant improvements in HHS in 1 month (85.00 (82.25, 86.00) vs. 80.00 (75.00, 83.00), p< 0.001), 3 months (88.00 (86.00, 92.00) vs. 83.00 (76.75, 85.00), p< 0.001), and 6 months (93.00 (90.25, 98.00) vs. 88.00 (84.25, 91.75), p< 0.001) when compared with the control group. Similar results were also found in both daily living ability and knee flexion angle measurements. No adverse event was observed during the follow-up.

Conclusion: The present study found that the stepped care model intervention significantly improved early-stage knee function and self-reported life quality after total knee arthroplasty due to knee osteoarthritis. Female patients and those less than 70 years old benefit more from the stepped care model intervention after total knee arthroplasty.

Background: Gastrointestinal tract (GIT) diseases impact the entire digestive system, spanning from the mouth to the anus. Wireless Capsule Endoscopy (WCE) stands out as an effective analytic instrument for Gastrointestinal tract diseases. Nevertheless, accurately identifying various lesion features, such as irregular sizes, shapes, colors, and textures, remains challenging in this field.

Objective: Several computer vision algorithms have been introduced to tackle these challenges, but many relied on handcrafted features, resulting in inaccuracies in various instances.

Methods: In this work, a novel Deep SS-Hexa model is proposed which is a combination two different deep learning structures for extracting two different features from the WCE images to detect various GIT ailment. The gathered images are denoised by weighted median filter to remove the noisy distortions and augment the images for enhancing the training data. The structural and statistical (SS) feature extraction process is sectioned into two phases for the analysis of distinct regions of gastrointestinal. In the first stage, statistical features of the image are retrieved using MobileNet with the support of SiLU activation function to retrieve the relevant features. In the second phase, the segmented intestine images are transformed into structural features to learn the local information. These SS features are parallelly fused for selecting the best relevant features with walrus optimization algorithm. Finally, Deep belief network (DBN) is used classified the GIT diseases into hexa classes namely normal, ulcer, pylorus, cecum, esophagitis and polyps on the basis of the selected features.

Results: The proposed Deep SS-Hexa model attains an overall average accuracy of 99.16% in GIT disease detection based on KVASIR and KID datasets. The proposed Deep SS-Hexa model achieves high level of accuracy with minimal computational cost in the recognition of GIT illness.

Conclusions: The proposed Deep SS-Hexa Model progresses the overall accuracy range of 0.04%, 0.80% better than GastroVision, Genetic algorithm based on KVASIR dataset and 0.60%, 1.21% better than Modified U-Net, WCENet based on KID dataset respectively.