The effectiveness of case management for cancer patients: an umbrella review

Advanced forecasting of COVID-19 epidemic: Leveraging ensemble models, advanced optimization, and decomposition techniques

In the global effort to address the outbreak of the new coronavirus pneumonia (COVID-19) pandemic, accurate forecasting of epidemic patterns has become crucial for implementing successful interventions aimed at preventing and controlling the spread of the disease. The correct prediction of the course of COVID-19 outbreaks is a complex and challenging task, mainly because of the significant volatility in the data series related to COVID-19. Previous studies have been limited by the exclusive use of individual forecasting techniques in epidemic modeling, disregarding the integration of diverse prediction procedures. The lack of attention to detail in this situation can yield worse-than-ideal results. Consequently, this study introduces a novel ensemble framework that integrates three machine learning methods (kernel ridge regression (KRidge), Deep random vector functional link (dRVFL), and ridge regression) within a linear relationship (L-KRidge-dRVFL-Ridge). The optimization of this framework is accomplished through a distinctive approach, specifically adaptive differential evolution and particle swarm optimization (A-DEPSO). Moreover, an effective decomposition method, known as time-varying filter empirical mode decomposition (TVF-EMD), is employed to decompose the input variables. A feature selection technique, specifically using the light gradient boosting machine (LGBM), is also implemented to extract the most influential input variables. The daily datasets of COVID-19 collected from two countries, namely Italy and Poland, were used as the experimental examples. Additionally, all models are implemented to forecast COVID-19 at two-time horizons: 10- and 14-day ahead (t+10 and t+14). According to the results, the proposed model can yield higher correlation coefficient (R) for both case studies: Italy (t+10 = 0.965, t+14 = 0.961) and Poland (t+10 = 0.952, t+14 = 0.940) than the other models. The experimental results demonstrate that the model suggested in this paper has outstanding results in various kinds of complex epidemic prediction situations. The proposed ensemble model demonstrates exceptional accuracy and resilience, outperforming all similar models in terms of efficacy.

Revolutionizing Infection Control: Harnessing MXene-Based Nanostructures for Versatile Antimicrobial Strategies and Healthcare Advancements

The escalating global health challenge posed by infections prompts the exploration of innovative solutions utilizing MXene-based nanostructures. Societally, the need for effective antimicrobial strategies is crucial for public health, while scientifically, MXenes present promising properties for therapeutic applications, necessitating scalable production and comprehensive characterization techniques. Here we review the versatile physicochemical properties of MXene materials for combatting microbial threats and their various synthesis methods, including etching and top-down or bottom-up techniques. Crucial characterization techniques such as XRD, Raman spectroscopy, SEM/TEM, FTIR, XPS, and BET analysis provide insightful structural and functional attributes. The review highlights MXenes' diverse antimicrobial mechanisms, spanning membrane disruption and oxidative stress induction, demonstrating efficacy against bacterial, viral, and fungal infections. Despite translational hurdles, MXene-based nanostructures offer broad-spectrum antimicrobial potential, with applications in drug delivery and diagnostics, presenting a promising path for advancing infection control in global healthcare.

A novel approach for denoising electrocardiogram signals to detect cardiovascular diseases using an efficient hybrid scheme

Background

Electrocardiogram (ECG) signals are inevitably contaminated with various kinds of noises during acquisition and transmission. The presence of noises may produce the inappropriate information on cardiac health, thereby preventing specialists from making correct analysis.

Methods

In this paper, an efficient strategy is proposed to denoise ECG signals, which employs a time-frequency framework based on S-transform (ST) and combines bi-dimensional empirical mode decomposition (BEMD) and non-local means (NLM). In the method, the ST maps an ECG signal into a subspace in the time frequency domain, then the BEMD decomposes the ST-based time-frequency representation (TFR) into a series of sub-TFRs at different scales, finally the NLM removes noise and restores ECG signal characteristics based on structural self-similarity.

Results

The proposed method is validated using numerous ECG signals from the MIT-BIH arrhythmia database, and several different types of noises with varying signal-to-noise (SNR) are taken into account. The experimental results show that the proposed technique is superior to the existing wavelet based approach and NLM filtering, with the higher SNR and structure similarity index measure (SSIM), the lower root mean squared error (RMSE) and percent root mean square difference (PRD).

Conclusions

The proposed method not only significantly suppresses the noise presented in ECG signals, but also preserves the characteristics of ECG signals better, thus, it is more suitable for ECG signals processing.

Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service

The healthcare sector, characterized by vast datasets and many diseases, is pivotal in shaping community health and overall quality of life. Traditional healthcare methods, often characterized by limitations in disease prevention, predominantly react to illnesses after their onset rather than proactively averting them. The advent of Artificial Intelligence (AI) has ushered in a wave of transformative applications designed to enhance healthcare services, with Machine Learning (ML) as a noteworthy subset of AI. ML empowers computers to analyze extensive datasets, while Deep Learning (DL), a specific ML methodology, excels at extracting meaningful patterns from these data troves. Despite notable technological advancements in recent years, the full potential of these applications within medical contexts remains largely untapped, primarily due to the medical community's cautious stance toward novel technologies. The motivation of this paper lies in recognizing the pivotal role of the healthcare sector in community well-being and the necessity for a shift toward proactive healthcare approaches. To our knowledge, there is a notable absence of a comprehensive published review that delves into ML, DL and distributed systems, all aimed at elevating the Quality of Service (QoS) in healthcare. This study seeks to bridge this gap by presenting a systematic and organized review of prevailing ML, DL, and distributed system algorithms as applied in healthcare settings. Within our work, we outline key challenges that both current and future developers may encounter, with a particular focus on aspects such as approach, data utilization, strategy, and development processes. Our study findings reveal that the Internet of Things (IoT) stands out as the most frequently utilized platform (44.3 %), with disease diagnosis emerging as the predominant healthcare application (47.8 %). Notably, discussions center significantly on the prevention and identification of cardiovascular diseases (29.2 %). The studies under examination employ a diverse range of ML and DL methods, along with distributed systems, with Convolutional Neural Networks (CNNs) being the most commonly used (16.7 %), followed by Long Short-Term Memory (LSTM) networks (14.6 %) and shallow learning networks (12.5 %). In evaluating QoS, the predominant emphasis revolves around the accuracy parameter (80 %). This study highlights how ML, DL, and distributed systems reshape healthcare. It contributes to advancing healthcare quality, bridging the gap between technology and medical adoption, and benefiting practitioners and patients.

The applications of internet of things in smart healthcare sectors: a bibliometric and deep study

The recent attention garnered by Internet of Things (IoT) technology for its potential to alleviate challenges faced by healthcare systems, such as those resulting from an aging population and the rise in chronic illnesses, has underscored the significance of smart healthcare. Surprisingly, no bibliometric study has been conducted on this subject to date. Consequently, this investigation aims to provide a comprehensive overview of the longitudinal state and knowledge structure of IoT in smart healthcare. To achieve this, a content analysis tool is employed for academic research, facilitating the identification of key study themes, the growth trajectory of the research topic, the top journal sources, and the distribution of nations based on subject areas. The bibliometric evaluation encompasses 614 publications published in 14 journals spanning the period from 2016 to 2022. Employing bibliographic coupling analysis, the latest developments in IoT have been uncovered within the domain of smart healthcare. The findings reveal 11 primary research topic areas that have been the focus of scholarly discourse during this period. This study highlights that the computing paradigm and network connectivity emerge as the most prominent topics within this research domain. Blockchain-based security in healthcare closely follows as the second-largest topic discussed by scholars. Additionally, the analysis indicates a significant increase in total publications for the most popular topic, peaking around 2018.

Multi-step influenza forecasting through singular value decomposition and kernel ridge regression with MARCOS-guided gradient-based optimization

This research delves into the significance of influenza outbreaks in public health, particularly the importance of accurate forecasts using weekly Influenza-like illness (ILI) rates. The present work develops a novel hybrid machine-learning model by combining singular value decomposition with kernel ridge regression (SKRR). In this context, a novel hybrid model known as H-SKRR is developed by combining two robust forecasting approaches, SKRR and ridge regression, which aims to improve multi-step-ahead predictions for weekly ILI rates in Southern and Northern China. The study begins with feature selection via XGBoost in the preprocessing phase, identifying optimal precursor information guided by importance factors. It decomposes the original signal using multivariate variational mode decomposition (MVMD) to address non-stationarity and complexity. H-SKRR is implemented by incorporating significant lagged-time components across sub-components. The aggregated forecasted values from these sub-components generate ILI values for two horizons (i.e., 4-and 7-weekly ahead). Employing the gradient-based optimization (GBO) algorithm fine-tunes model parameters. Furthermore, the deep random vector functional link (dRVFL), Ridge regression, and gated recurrent unit neural network (GRU) models were employed to validate the MVMD-H-SKRR-GBO paradigm's effectiveness. The outcomes, assessed using the MARCOS (Measurement of alternatives and ranking according to compromise solution) method as a multi-criteria decision-making method, highlight the superior accuracy of the MVMD-H-SKRR-GBO model in predicting ILI rates. The results clearly highlight the exceptional performance of the MVMD-H-SKRR-GBO model, with outstanding precision demonstrated by impressive R, RMSE, IA, and U95 % values of 0.946, 0.388, 0.970, and 1.075, respectively, at t + 7.

Revolutionizing Breast Cancer Care: AI-Enhanced Diagnosis and Patient History

Breast cancer poses a significant global health challenge, demanding enhanced diagnostic accuracy and streamlined medical history documentation. This study presents a holistic approach that harnesses the power of artificial intelligence (AI) and machine learning (ML) to address these pressing needs. This study presents a comprehensive methodology for breast cancer diagnosis and medical history generation, integrating data collection, feature extraction, machine learning, and AI-driven history-taking. The research employs a systematic approach to ensure accurate diagnosis and efficient history collection. Data preprocessing merges similar attributes to streamline analysis. Three key algorithms, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Fuzzy Logic, are applied. Fuzzy Logic shows exceptional accuracy in handling uncertain data. Deep learning models enhance predictive accuracy, emphasizing the synergy between traditional and deep learning approaches. The AI-driven history collection simplifies the patient history-taking process, adapting questions dynamically based on patient responses. Comprehensive medical history reports summarize patient data, facilitating informed healthcare decisions. The research prioritizes ethical compliance and data privacy. OpenAI has integrated GPT-3.5 to generate automated patient reports, offering structured overviews of patient health history. The study's results indicate the potential for enhanced disease prediction accuracy and streamlined medical history collection, contributing to more reliable healthcare assessments and patient care. Machine learning, deep learning, and AI-driven approaches hold promise for a wide range of applications, particularly in healthcare and beyond.

Hospital factors determining overall survival in cancer patients undergoing curative treatment

BACKGROUND

In oncology, overall survival (OS) and quality of life (QoL) are key indicators. The factors that affect OS and QoL include tumor-related characteristics (stage and grade), patient-related factors (performance status and comorbidities), and cancer-directed therapy (CDT)-related aspects. In addition, external factors such as governance or policy (e.g., inaccessibility to CDT, increased distance to service, poor socioeconomic status, lack of insurance), and hospital-related factors (e.g., facility volume and surgeon volume) can influence OS and QoL.

MATERIALS AND METHODS

The primary objective of this narrative review was to identify hospital-related factors that affect OS and QoL in patients receiving curative CDT. The authors defined extrinsic factors that can be modified at the hospital level as "hospital-related" factors. Only factors supported by randomized controlled trials (RCT), systematic reviews (SR) and/or meta-analyses (MA), and population database (PDB) analyses that address the relationship between OS and hospital factors were considered.

RESULTS

The literature review found that high hospital or oncologist volume, adherence to evidence-based medicine (EBM), optimal time-to-treatment initiation (TTI), and decreased overall treatment time (OTT) increase OS in patients undergoing curative CDT. The use of case management strategies was associated with better symptom management and treatment compliance, but had a mixed effect on QoL. The practice of enhanced recovery after surgery (ERAS) in cancer patients did not result in an increase in OS. There was insufficient evidence to support the impact of factors such as teaching or academic centers, hospital infrastructure, and treatment compliance on OS and QoL.

CONCLUSION

The authors conclude that hospital policies should focus on increasing hospital and oncologist volume, adhering to EBM, optimizing TTI, and reducing OTT for cancer patients receiving curative treatment.

Early diagnosis of oral cancer using a hybrid arrangement of deep belief networkand combined group teaching algorithm

Oral cancer can occur in different parts of the mouth, including the lips, palate, gums, and inside the cheeks. If not treated in time, it can be life-threatening. Incidentally, using CAD-based diagnosis systems can be so helpful for early detection of this disease and curing it. In this study, a new deep learning-based methodology has been proposed for optimal oral cancer diagnosis from the images. In this method, after some preprocessing steps, a new deep belief network (DBN) has been proposed as the main part of the diagnosis system. The main contribution of the proposed DBN is its combination with a developed version of a metaheuristic technique, known as the Combined Group Teaching Optimization algorithm to provide an efficient system of diagnosis. The presented method is then implemented in the "Oral Cancer (Lips and Tongue) images dataset" and a comparison is done between the results and other methods, including ANN, Bayesian, CNN, GSO-NN, and End-to-End NN to show the efficacy of the techniques. The results showed that the DBN-CGTO method achieved a precision rate of 97.71%, sensitivity rate of 92.37%, the Matthews Correlation Coefficient of 94.65%, and 94.65% F1 score, which signifies its ability as the highest efficiency among the others to accurately classify positive samples while remaining the independent correct classification of negative samples.

Biosensor-Based Multimodal Deep Human Locomotion Decoding via Internet of Healthcare Things

Multiple Internet of Healthcare Things (IoHT)-based devices have been utilized as sensing methodologies for human locomotion decoding to aid in applications related to e-healthcare. Different measurement conditions affect the daily routine monitoring, including the sensor type, wearing style, data retrieval method, and processing model. Currently, several models are present in this domain that include a variety of techniques for pre-processing, descriptor extraction, and reduction, along with the classification of data captured from multiple sensors. However, such models consisting of multiple subject-based data using different techniques may degrade the accuracy rate of locomotion decoding. Therefore, this study proposes a deep neural network model that not only applies the state-of-the-art Quaternion-based filtration technique for motion and ambient data along with background subtraction and skeleton modeling for video-based data, but also learns important descriptors from novel graph-based representations and Gaussian Markov random-field mechanisms. Due to the non-linear nature of data, these descriptors are further utilized to extract the codebook via the Gaussian mixture regression model. Furthermore, the codebook is provided to the recurrent neural network to classify the activities for the locomotion-decoding system. We show the validity of the proposed model across two publicly available data sampling strategies, namely, the HWU-USP and LARa datasets. The proposed model is significantly improved over previous systems, as it achieved 82.22% and 82.50% for the HWU-USP and LARa datasets, respectively. The proposed IoHT-based locomotion-decoding model is useful for unobtrusive human activity recognition over extended periods in e-healthcare facilities.

Generating Team-Based Strategies to Reduce Health Inequity in Cancer Care

PURPOSE/OBJECTIVES

Despite increased emphases on reducing racial disparities in the U.S. health care system, interprofessional care teams may inadvertently perpetuate health disparities through lack of awareness or experience in supporting individualized, patient-centered goals of care. Racial disparities can lead to health inequity. Persistent health disparity gaps exist among Black patients with multiple myeloma (MM) when compared with non-Black patients. Black patients experience a two-fold increase in MM risk and earlier age of onset compared with non-Black patients. Black patients are also less likely to receive timely access to some therapies, undergo autologous stem cell transplant, or enroll in clinical trials. This article describes a large-scale, equity-focused implementation science initiative aimed at identifying and overcoming racial disparities and health inequity among patients with MM through quality improvement goals identified by each of the interprofessional cancer care teams.

PRIMARY PRACTICE SETTINGS

Interprofessional cancer care teams in two large oncology systems as well as four community clinics were engaged in this study along with their patients with MM. Geographic areas included the following: Chicago, IL; Washington, DC; Charlotte, NC; Columbus, OH; Denver, CO; and Indianapolis, IN. Interprofessional teams included hematologists/oncologists, primary care physicians, nurse practitioners/physician assistants, and case managers/nurse navigators. Teams collectively examined and compared their own beliefs and attitudes about their patients' goals for MM treatment and management versus those of their patients to uncover and address discordances. Medical records from the clinics were audited to evaluate disparities in treatment and practice at the point of care. Live, team-based audit-feedback sessions were implemented among teams to examine data sets, as well as utilize the data to address interprofessional factors that could enhance more equitable care.

FINDINGS/CONCLUSIONS

Data from comparative surveys between patients and interprofessional team members revealed significant discordances that enabled health care teams to recognize gaps and identify ways to improve patient-centered care, such as shared decision-making. Through audit-feedback sessions, interprofessional teams were able to collaboratively meet and discuss methods to improve access to care coordination services and other strategies aimed at alleviating disparities. Baseline chart audits revealed and confirmed disparities of care including patient/disease characteristics, treatment history, clinical practice metrics, and patient-centered measures. Follow-up chart audits conducted 6 months later measured changes in documented practice behavior. Action plans developed by the interprofessional teams as a result of this study intend to address sustainable reductions in health disparities among patients with MM to improve health equity and overall care.

IMPLICATIONS FOR CASE MANAGEMENT PRACTICE

This implementation science initiative and data results have several implications for case managers caring for diverse patients with MM in both large health systems and smaller community practices. Results punctuate the importance of identifying and supporting diverse patients' individualized goals and preferences in their care journey to mitigate health inequity and maximize health outcomes. The value of working collaboratively as an interprofessional team is evident in the study results, as is the role of the case manager in appropriate resource allocation to mitigate health disparities. Lessons learned from this initiative may also be applied to other case management settings where complex care delivery and interprofessional teams are at work.

Minimal residual disease (MRD) detection in solid tumors using circulating tumor DNA: a systematic review

Minimal residual disease (MRD) refers to a very small number of residual tumor cells in the body during or after treatment, representing the persistence of the tumor and the possibility of clinical progress. Circulating tumor DNA (ctDNA) is a DNA fragment actively secreted by tumor cells or released into the circulatory system during the process of apoptosis or necrosis of tumor cells, which emerging as a non-invasive biomarker to dynamically monitor the therapeutic effect and prediction of recurrence. The feasibility of ctDNA as MRD detection and the revolution in ctDNA-based liquid biopsies provides a potential method for cancer monitoring. In this review, we summarized the main methods of ctDNA detection (PCR-based Sequencing and Next-Generation Sequencing) and their advantages and disadvantages. Additionally, we reviewed the significance of ctDNA analysis to guide the adjuvant therapy and predict the relapse of lung, breast and colon cancer et al. Finally, there are still many challenges of MRD detection, such as lack of standardization, false-negatives or false-positives results make misleading, and the requirement of validation using large independent cohorts to improve clinical outcomes.

Cancer-Related Pain Management in Suitable Intrathecal Therapy Candidates: A Spanish Multidisciplinary Expert Consensus

A consensus is needed among healthcare professionals involved in easing oncological pain in patients who are suitable candidates for intrathecal therapy. A Delphi consultation was conducted, guided by a multidisciplinary scientific committee. The 18-item study questionnaire was designed based on a literature review together with a discussion group. The first-round questionnaire assessed experts' opinion of the current general practice, as well as their recommendation and treatment feasibility in the near future (2-3-year period) using a 9-point Likert scale. Items for which consensus was not achieved were included in a second round. Consensus was defined as ≥75% agreement (1-3 or 7-9). A total of 67 panelists (response rate: 63.2%) and 62 (92.5%) answered the first and second Delphi rounds, respectively. The participants were healthcare professionals from multiple medical disciplines who had an average of 17.6 (7.8) years of professional experience. A consensus was achieved on the recommendations (100%). The actions considered feasible to implement in the short term included effective multidisciplinary coordination, improvement in communication among the parties, and an assessment of patient satisfaction. Efforts should focus on overcoming the barriers identified, eventually leading to the provision of more comprehensive care and consideration of the patient's perspective.