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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.

Autism spectrum conditions (ASC) and borderline personality disorder (BPD) have overlapping symptom profiles. Dialectical behaviour therapy (DBT) is an established treatment for self-harm and BPD, but little research has investigated the outcomes of DBT for ASC populations. This exploratory service evaluation aims to investigate the outcomes of a comprehensive DBT programme for adolescents with a diagnosis of emerging BPD and a co-occurring ASC diagnosis as compared to those without an ASC diagnosis.

Differences from the start to end of treatment in the frequency of self-harming behaviours, BPD symptoms, emotion dysregulation, depression, anxiety, the number of A&E attendances and inpatient bed days, education and work status, and treatment non-completion rates were analysed for those with an ASC diagnosis, and compared between those with an ASC diagnosis and those without.

Significant medium to large reductions in self-harming behaviours, BPD symptoms, emotion dysregulation and inpatient bed days were found for those with an ASC diagnosis by the end of treatment. There were no significant differences between those with an ASC and those without in any outcome or in non-completion rates. These findings indicate that DBT may be a useful treatment model for those with an ASC diagnosis, though all results are preliminary and require replication.

To the best of the authors’ knowledge, this is the first study to report the outcomes of a comprehensive DBT programme for adolescents with an ASC diagnosis, and to compare the changes in outcomes between those with a diagnosis and those without.

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.

Literature regarding recovery has focussed on diagnoses such as schizophrenia, with few papers focussing on borderline personality disorder (BPD). This is a significant area in need of change because a lack of research concentrating on recovery from BPD could be seen to perpetuate the view that recovery from this condition may not be possible. Recovery Colleges (RCs) in the UK began in 2009and aim to offer co-produced and co-facilitated psychoeducational courses to encourage recovery and enable people to develop skills and knowledge so they become experts in the self-management of their difficulties. Given the gaps within the recovery literature, it is unclear how Recovery Colleges can support recovery for people diagnosed with BPD. The purpose of this study was to explore the impact of a Recovery College course for people diagnosed with BPD.

Using participatory methods, this paper aims to explore the question of what personal recovery looks like for people with BPD and how this may prove useful in developing future practice in RCs. Qualitative feedback data was collected from 51 managing intense emotions courses delivered to 309 students using a patient reported experience measure between Autumn 2015 and Autumn 2021.

The results of this study indicate that people with BPD can experience recovery, whilst still experiencing symptoms, as long as they receive appropriate co-produced, recovery-orientated support and services.

Further research in this area could help shape future clinical practice by embedding a recovery-focussed programme into community services.

Literature regarding recovery has focussed on diagnoses such as schizophrenia withfew papers focussing on BPD. This is an area in need of change because a lack of research on recovery from BPD could be seen to perpetuate the view that recovery from this condition may not be possible. RCs offer co-produced and co-facilitated psychoeducational courses around recovery, enabling people to develop skills and knowledge to become experts in the self-management of their difficulties. Given the gaps within the recovery literature it is unclear how RCs can support recovery for this group of service users.

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.

This study aims to examine the predictive power of childhood adversity and severity of positive symptoms on suicidality, controlling for selected sociodemographics factors, among hospitalized patients diagnosed with schizophrenia in Jordan.

This study used a descriptive-explorative design. The study was conducted at two major psychiatric hospitals in Jordan. The targeted sample was 66 patients diagnosed with schizophrenia. Data was collected using a structured format in the period February–April 2024.

A two-step multiple hierarchical regression analysis was conducted. In the first model, childhood adversity and the severity of positive symptoms were entered. In the second model, sociodemographic variables were entered. The analysis revealed that the first model (F = 5.35, p = 0.007) was statistically significant. The second model (F = 717, p < 0.001) was statistically significant. Furthermore, the analysis revealed that childhood adversity was not a significant predictor for suicidality. However, positive symptoms and patients’ demographics (age, number of hospitalizations and length of being diagnosed with schizophrenia) were significant predictors of suicidality. The analysis revealed that childhood adversity was not a significant predictor of suicidality. However, positive symptoms and patients’ demographics (age, number of hospitalizations and length of being diagnosed with schizophrenia) were significant predictors of suicidality.

One limitation of this study is related to the sample and the setting where there were only 66 patients recruited from governmental hospitals within inpatient wards. Thus, the upcoming studies should include more participants from private hospitals and different hospital settings including outpatient and emergency departments.

The research provides empirical insights that positive symptoms, age hospitalization and schizophrenia diagnosis length were significant predictors of suicidality. At the same time, childhood adversity was not a significant predictor of suicidality.

The current research contributes to expanding mental health studies. Moreover, this study enlarges the body of knowledge in the academic world and clinical settings. It supports the disciplines of psychology, mental health and social sciences by increasing knowledge of the complicated relationships among childhood adversity, positive symptoms and suicidality.

This paper fulfills an identified need to study childhood adversity with comorbid psychiatric disorders such as schizophrenia, as well as psychiatric mental health covariates.

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.

Pervasive health-care computing applications in medical field provide better diagnosis of various organs such as brain, spinal card, heart, lungs and so on. The purpose of this study is to find brain tumor diagnosis using Machine learning (ML) and Deep Learning(DL) techniques. The brain diagnosis process is an important task to medical research which is the most prominent step for providing the treatment to patient. Therefore, it is important to have high accuracy of diagnosis rate so that patients easily get treatment from medical consult. There are many earlier investigations on this research work to diagnose brain diseases. Moreover, it is necessary to improve the performance measures using deep and ML approaches.

In this paper, various brain disorders diagnosis applications are differentiated through following implemented techniques. These techniques are computed through segment and classify the brain magnetic resonance imaging or computerized tomography images clearly. The adaptive median, convolution neural network, gradient boosting machine learning (GBML) and improved support vector machine health-care applications are the advance methods used to extract the hidden features and providing the medical information for diagnosis. The proposed design is implemented on Python 3.7.8 software for simulation analysis.

This research is getting more help for investigators, diagnosis centers and doctors. In each and every model, performance measures are to be taken for estimating the application performance. The measures such as accuracy, sensitivity, recall, F1 score, peak-to-signal noise ratio and correlation coefficient have been estimated using proposed methodology. moreover these metrics are providing high improvement compared to earlier models.

The implemented deep and ML designs get outperformance the methodologies and proving good application successive score.

Bearings play a critical role in the reliable operation of induction machines, and their failure can lead to significant operational challenges and downtime. Detecting and diagnosing these defects is imperative to ensure the longevity of induction machines and preventing costly downtime. The purpose of this paper is to develop a novel approach for diagnosis of bearing faults in induction machine.

To identify the different fault states of the bearing with accurately and efficiently in this paper, the original bearing vibration signal is first decomposed into several intrinsic mode functions (IMFs) using variational mode decomposition (VMD). The IMFs that contain more noise information are selected using the Pearson correlation coefficient. Subsequently, discrete wavelet transform (DWT) is used to filter the noisy IMFs. Second, the composite multiscale weighted permutation entropy (CMWPE) of each component is calculated to form the features vector. Finally, the features vector is reduced using the locality-sensitive discriminant analysis algorithm, to be fed into the support vector machine model for training and classification.

The obtained results showed the ability of the VMD_DWT algorithm to reduce the noise of raw vibration signals. It also demonstrated that the proposed method can effectively extract different fault features from vibration signals.

This study suggested a new VMD_DWT method to reduce the noise of the bearing vibration signal. The proposed approach for bearing fault diagnosis of induction machine based on VMD-DWT and CMWPE is highly effective. Its effectiveness has been verified using experimental data.

Early bio-psycho-social experiences can dramatically impact all aspects of development. Both autism and traumagenic histories can lead to trans-diagnostic behavioural features that can be confused with one another during diagnostic assessment, unless an in-depth differential diagnostic evaluation is conducted that considers the developmental aetiology and underpinning experiences and triggers to trans-diagnostic behaviours.

This paper will explore the ways in which biological, cognitive, emotional and social sequelae of early trauma and attachment challenges, can look very similar to a range of neurodevelopmental disorders, including autism. Relevant literature and theory will be considered and synthesised with clinical knowledge of trauma and autism.

Recommendations are made for how the overlap between features of autism and trauma can be considered during assessments alongside consideration for interventions to enable people to access the most appropriate support for their needs.

Many features of the behaviours of individuals who have experienced early childhood trauma and disrupted or maladaptive attachments, may look similar to the behaviours associated with autism and hence diagnostic assessments of autism need to carefully differentiate traumagenic causes, to either dual diagnose (if both are present) or exclude autism, if it is not present. This has for long been recognised in child and adolescent autism specialist services but is less well developed in adult autism specialist services.