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This study aims to use the database of consumers referred to the dual diagnosis shared care service to examine those connections. The Eastern Dual Diagnosis Service, based in Melbourne, Australia, has established a database of consumers with co-occurring mental health disorders and problematic substance use. An examination of mental health and substance-use information obtained over a two-year period in the delivery of dual diagnosis shared care to consumers of mental health services is supporting an improved understanding of substance use and the connections to specific mental health diagnoses of schizophrenia, bipolar disorder and schizoaffective disorder.

This research uses a quantitative approach to review the prevalence of primary substance use and mental health diagnoses for consumers referred to as dual diagnosis shared care. Reviewed are referrals from adult mental health community and rehabilitation teams operating within a mental health and well-being program between January 2019 and December 2020 inclusive.

Of the 387 clients referred to the specialist dual diagnosis shared care, methamphetamine, alcohol and cannabis are associated with 89.4% of the primary mental health diagnosis (PMHD). The most common PMHDs are schizophrenia, schizoaffective disorder and bipolar disorder. The most common PMHD and substance-use connection was schizophrenia and methamphetamine. Nicotine was reported to be used by 84% of consumers and often occurred in addition to another problematic primary substance.

Improved dual diagnosis data collection from a community-based clinical mental health service is increasing understanding of the mental health and substance-use relationship. This is now providing clarity on routes of investigation into co-occurring mental health and problematic substance-use trends and guiding improved integrated treatments within a contemporary mental health setting.

Motivated by recent research indicating the significant challenges posed by imbalanced datasets in industrial settings, this paper presents a novel framework for Industrial Data-driven Modeling for Imbalanced Fault Diagnosis, aiming to improve fault detection accuracy and reliability.

This study addressing the challenge of imbalanced datasets in predicting hard drive failures is both innovative and comprehensive. By integrating data enhancement techniques with cost-sensitive methods, the research pioneers a solution that directly targets the intrinsic issues posed by imbalanced data, a common obstacle in predictive maintenance and reliability analysis.

In real industrial environments, there is a critical demand for addressing the issue of imbalanced datasets. When faced with limited data for rare events or a heavily skewed distribution of categories, it becomes essential for models to effectively mine insights from the original imbalanced dataset. This involves employing techniques like data augmentation to generate new insights and rules, enhancing the model’s ability to accurately identify and predict failures.

Previous research has highlighted the complexity of diagnosing faults within imbalanced industrial datasets, often leading to suboptimal predictive accuracy. This paper bridges this gap by introducing a robust framework for Industrial Data-driven Modeling for Imbalanced Fault Diagnosis. It combines data enhancement and cost-sensitive methods to effectively manage the challenges posed by imbalanced datasets, further innovating with a bagging method to refine model optimization. The validation of the proposed approach demonstrates superior accuracy compared to existing methods, showcasing its potential to significantly improve fault diagnosis in industrial applications.

Three-way decision (3WD) and probabilistic rough sets (PRSs) are theoretical tools capable of simulating humans' multi-level and multi-perspective thinking modes in the field of decision-making. They are proposed to assist decision-makers in better managing incomplete or imprecise information under conditions of uncertainty or fuzziness. However, it is easy to cause decision losses and the personal thresholds of decision-makers cannot be taken into account. To solve this problem, this paper combines picture fuzzy (PF) multi-granularity (MG) with 3WD and establishes the notion of PF MG 3WD.

An effective incomplete model based on PF MG 3WD is designed in this paper. First, the form of PF MG incomplete information systems (IISs) is established to reasonably record the uncertain information. On this basis, the PF conditional probability is established by using PF similarity relations, and the concept of adjustable PF MG PRSs is proposed by using the PF conditional probability to fuse data. Then, a comprehensive PF multi-attribute group decision-making (MAGDM) scheme is formed by the adjustable PF MG PRSs and the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. Finally, an actual breast cancer data set is used to reveal the validity of the constructed method.

The experimental results confirm the effectiveness of PF MG 3WD in predicting breast cancer. Compared with existing models, PF MG 3WD has better robustness and generalization performance. This is mainly due to the incomplete PF MG 3WD proposed in this paper, which effectively reduces the influence of unreasonable outliers and threshold settings.

The model employs the VIKOR method for optimal granularity selections, which takes into account both group utility maximization and individual regret minimization, while incorporating decision-makers' subjective preferences as well. This ensures that the experiment maintains higher exclusion stability and reliability, enhancing the robustness of the decision results.

Coronary artery disease is one of the most common cardiovascular disorders in the world, and it can be deadly. Traditional diagnostic approaches are based on angiography, which is an interventional procedure having side effects such as contrast nephropathy or radio exposure as well as significant expenses. The purpose of this paper is to propose a novel artificial intelligence (AI) approach for the diagnosis of coronary artery disease as an effective alternative to traditional diagnostic methods.

In this study, a novel ensemble AI approach based on optimization and classification is proposed. The proposed ensemble structure consists of three stages: feature selection, classification and combining. In the first stage, important features for each classification method are identified using the binary particle swarm optimization algorithm (BPSO). In the second stage, individual classification methods are used. In the final stage, the prediction results obtained from the individual methods are combined in an optimized way using the particle swarm optimization (PSO) algorithm to achieve better predictions.

The proposed method has been tested using an up-to-date real dataset collected at Basaksehir Çam and Sakura City Hospital. The data of disease prediction are unbalanced. Hence, the proposed ensemble approach improves majorly the F-measure and ROC area which are more prominent measures in case of unbalanced classification. The comparison shows that the proposed approach improves the F-measure and ROC area results of the individual classification methods around 14.5% in average and diagnoses with an accuracy rate of 96%.

This study presents a low-cost and low-risk AI-based approach for diagnosing heart disease compared to traditional diagnostic methods. Most of the existing research studies focus on base classification methods. In this study, we mainly investigate an effective ensemble method that uses optimization approaches for feature selection and combining stages for the medical diagnostic domain. Furthermore, the approaches in the literature are commonly tested on open-access dataset in heart disease diagnoses, whereas we apply our approach on a real and up-to-date dataset.

This study investigated two key aspects: (1) how a hospital bundles limited resources for preventive care performance and (2) how to develop IS capabilities to enhance preventive care performance.

A case study method was adopted to examine how a hospital integrates its limited resources which leads to the need for resource bundles and an understanding of IS capabilities development to understand how they contribute to the delivery of preventive care in a Malaysian hospital.

This research proposes a comprehensive framework outlining resource-bundling and IS capabilities development to improve preventive care.

We acknowledge that the problem of transferring and generalizing results has been a common criticism of a single case study. However, our objective was to enhance the reader’s understanding by including compelling, detailed narratives demonstrating how our research results offer practical examples that can be generalized theoretically. The findings also apply to similar-sized public hospitals in Malaysia and other developing countries, facing challenges like resource constraints, HIS adoption levels, healthcare workforce shortages, cultural and linguistic diversity, bureaucratic hurdles, and specific patient demographics and health issues. Further, lessons from this context can be usefully applied to non-healthcare service sector domains.

This study provides a succinct strategy for enhancing preventive care in Malaysian public hospitals, focusing on system integration and alignment with hospital strategy, workforce diversity through recruitment and mentorship, and continuous training for health equity and inclusivity. This approach aims to improve resource efficiency, communication, cultural competence, and healthcare outcomes.

Efficiently using limited resources through HIS investment is essential to improve preventive care and reduce chronic diseases, which cause approximately nine million deaths annually in Southeast Asia, according to WHO. This issue has significantly impacted the socioeconomic development of developing countries.

This research refines resource orchestration theory with new mechanisms for resource mobilization, extends IS literature by identifying how strategic bundling forms specialized healthcare IS capabilities, enriches preventive care literature through actionable resource-bundling activities, and adds to HIS literature by advocating for an integrated, preventive care focus from the alignment of HIS design, people and institutional policies to address concerns raised by other research regarding the utilization of HIS in improving the quality of preventive care.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

This study aims to explore the information needs of Chinese parents of children with Autism Spectrum Disorder (ASD) and how these needs evolve as their children develop.

This study collated 17,122 questions regarding raising children with ASD via the Yi Lin website until November 2021.

The information needs of parents of children with ASD were classified into two categories: 1) Cognition-motivation: related to children with ASD; and 2) Affection-motivation: related to their parents. Child development causes the adaptation of information needs of these parents. Within the first three years, nine different topics of these parents' information needs were identified. Major information needs at this stage are as follows: intervention content, intervention methods and pre-diagnosis questions. During the ages of three to six years, there were 13 topics of information needs for parents, focusing on three areas: intervention content, intervention methods and diagnosis and examination. There are eight topics of information needs post six years. Parents are more concerned with the three topics of intervention content, life planning and intervention methods.

This novel study indicates the complex and changing information needs of parents of children with ASD in China. It may enhance the understanding of the information needs of these parents at theoretical and practical levels, provide support for them to understand their own information needs and provide a reference for relevant government and social organisations to provide targeted information services for them.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2022-0247

This paper aims to critically evaluate the role of advanced artificial intelligence (AI)-enhanced image fusion techniques in lung cancer diagnostics within the context of AI-driven precision medicine.

We conducted a systematic review of various studies to assess the impact of AI-based methodologies on the accuracy and efficiency of lung cancer diagnosis. The focus was on the integration of AI in image fusion techniques and their application in personalized treatment strategies.

The review reveals significant improvements in diagnostic precision, a crucial aspect of the evolution of AI in healthcare. These AI-driven techniques substantially enhance the accuracy of lung cancer diagnosis, thereby influencing personalized treatment approaches. The study also explores the broader implications of these methodologies on healthcare resource allocation, policy formation, and epidemiological trends.

This study is notable for both emphasizing the clinical importance of AI-integrated image fusion in lung cancer treatment and illuminating the profound influence these technologies have in the future AI-driven healthcare systems.

Parents of children with intellectual and developmental disabilities are frequently given news that is difficult to hear and can be very traumatic. Whether receiving an initial diagnosis for their baby or learning about guardianship options for their adult child, emotional reactions almost always occur, especially because of the interdependent relationship they have with their child. These emotions likely impact the meaning parents give to information and decisions they make for their children throughout their lives. Medical, education, and other support providers sometimes assume parents can objectively receive information that frequently is communicated in a technical and clinical way. They may not give parents the time to emotionally process what they have learned, limiting their ability to care for their child. This chapter presents the results from a series of focus groups with 21 parents of children with intellectual and developmental disabilities of varying ages. The participants discussed their emotional reactions to information communicated to them about medical, educational and social concerns related to their children. In addition, they discussed how emotions impacted their information processing and sensemaking as they gave meaning to what they learned. Analysis of the results identified eight emotion-based information processing and sensemaking themes that are described in detail. The discussion section provides an enhanced explanation for emotion's role in parental information processing and sensemaking. In addition, recommendations for providers communicating emotional information to parents are provided.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.