AIM To define regional grey-matter abnormalities in schizophrenia patients with poor insight(Insight-),relative to patients with preserved clinical insight(Insight+),and healthy controls.METHODS Forty stable schizophr...AIM To define regional grey-matter abnormalities in schizophrenia patients with poor insight(Insight-),relative to patients with preserved clinical insight(Insight+),and healthy controls.METHODS Forty stable schizophrenia outpatients(20 Insight-and 20 Insight+) and 20 healthy controls underwent whole brain magnetic resonance imaging(MRI).Insight in all patients was assessed using the Birchwood Insight Scale(BIS;a self-report measure).The two patient groups were preselected to match on most clinical and demographic parameters but,by design,they had markedly distinct BIS scores.Voxel-based morphometry employed in SPM8 was used to examine group differences in grey matter volumes across the whole brain.RESULTS The three participant groups were comparable in age [F(2,57) = 0.34,P = 0.71] and the patient groups did not differ in age at illness onset [t(38) = 0.87,P = 0.39].Insight-and Insight+ patient groups also did not differ in symptoms on the Positive and Negative Syndromes scale(PANSS):Positive symptoms [t(38) = 0.58,P = 0.57],negative symptoms [t(38) = 0.61,P = 0.55],general psychopathology [t(38) = 1.30,P = 0.20] and total PANSS scores [t(38) = 0.21,P = 0.84].The two patient groups,as expected,varied significantly in the level of BIS-assessed insight [t(38) = 12.11,P < 0.001].MRI results revealed lower fronto-temporal,parahippocampal,occipital and cerebellar grey matter volumes in Insightpatients,relative to Insight+ patients and healthy controls(for all clusters,family-wise error corrected P < 0.05).Insight+ patient and healthy controls did not differ significantly(P > 0.20) from each other.CONCLUSION Our findings demonstrate a clear association between poor clinical insight and smaller fronto-temporal,occipital and cerebellar grey matter volumes in stable long-term schizophrenia patients.展开更多
Customer churn poses a significant challenge for the banking and finance industry in the United States, directly affecting profitability and market share. This study conducts a comprehensive comparative analysis of ma...Customer churn poses a significant challenge for the banking and finance industry in the United States, directly affecting profitability and market share. This study conducts a comprehensive comparative analysis of machine learning models for customer churn prediction, focusing on the U.S. context. The research evaluates the performance of logistic regression, random forest, and neural networks using industry-specific datasets, considering the economic impact and practical implications of the findings. The exploratory data analysis reveals unique patterns and trends in the U.S. banking and finance industry, such as the age distribution of customers and the prevalence of dormant accounts. The study incorporates macroeconomic factors to capture the potential influence of external conditions on customer churn behavior. The findings highlight the importance of leveraging advanced machine learning techniques and comprehensive customer data to develop effective churn prevention strategies in the U.S. context. By accurately predicting customer churn, financial institutions can proactively identify at-risk customers, implement targeted retention strategies, and optimize resource allocation. The study discusses the limitations and potential future improvements, serving as a roadmap for researchers and practitioners to further advance the field of customer churn prediction in the evolving landscape of the U.S. banking and finance industry.展开更多
How organizations analyze and use data for decision-making has been changed by cognitive computing and artificial intelligence (AI). Cognitive computing solutions can translate enormous amounts of data into valuable i...How organizations analyze and use data for decision-making has been changed by cognitive computing and artificial intelligence (AI). Cognitive computing solutions can translate enormous amounts of data into valuable insights by utilizing the power of cutting-edge algorithms and machine learning, empowering enterprises to make deft decisions quickly and efficiently. This article explores the idea of cognitive computing and AI in decision-making, emphasizing its function in converting unvalued data into valuable knowledge. It details the advantages of utilizing these technologies, such as greater productivity, accuracy, and efficiency. Businesses may use cognitive computing and AI to their advantage to obtain a competitive edge in today’s data-driven world by knowing their capabilities and possibilities [1].展开更多
基金Supported by The Wellcome Trust,United Kingdom and was carried out as part of the first author’s PhD research under Professor Veena Kumari and Dr Dominic ffytche’s supervision,Nos.067427 and 072298Professor Kumari is part funded by the Biomedical Research Centre for Mental Health at the Institute of Psychiatry,Psychology and Neuroscience King’s College London,and the South London and Maudsley NHS Foundation Trust,United Kingdom(to Kumari V)
文摘AIM To define regional grey-matter abnormalities in schizophrenia patients with poor insight(Insight-),relative to patients with preserved clinical insight(Insight+),and healthy controls.METHODS Forty stable schizophrenia outpatients(20 Insight-and 20 Insight+) and 20 healthy controls underwent whole brain magnetic resonance imaging(MRI).Insight in all patients was assessed using the Birchwood Insight Scale(BIS;a self-report measure).The two patient groups were preselected to match on most clinical and demographic parameters but,by design,they had markedly distinct BIS scores.Voxel-based morphometry employed in SPM8 was used to examine group differences in grey matter volumes across the whole brain.RESULTS The three participant groups were comparable in age [F(2,57) = 0.34,P = 0.71] and the patient groups did not differ in age at illness onset [t(38) = 0.87,P = 0.39].Insight-and Insight+ patient groups also did not differ in symptoms on the Positive and Negative Syndromes scale(PANSS):Positive symptoms [t(38) = 0.58,P = 0.57],negative symptoms [t(38) = 0.61,P = 0.55],general psychopathology [t(38) = 1.30,P = 0.20] and total PANSS scores [t(38) = 0.21,P = 0.84].The two patient groups,as expected,varied significantly in the level of BIS-assessed insight [t(38) = 12.11,P < 0.001].MRI results revealed lower fronto-temporal,parahippocampal,occipital and cerebellar grey matter volumes in Insightpatients,relative to Insight+ patients and healthy controls(for all clusters,family-wise error corrected P < 0.05).Insight+ patient and healthy controls did not differ significantly(P > 0.20) from each other.CONCLUSION Our findings demonstrate a clear association between poor clinical insight and smaller fronto-temporal,occipital and cerebellar grey matter volumes in stable long-term schizophrenia patients.
文摘Customer churn poses a significant challenge for the banking and finance industry in the United States, directly affecting profitability and market share. This study conducts a comprehensive comparative analysis of machine learning models for customer churn prediction, focusing on the U.S. context. The research evaluates the performance of logistic regression, random forest, and neural networks using industry-specific datasets, considering the economic impact and practical implications of the findings. The exploratory data analysis reveals unique patterns and trends in the U.S. banking and finance industry, such as the age distribution of customers and the prevalence of dormant accounts. The study incorporates macroeconomic factors to capture the potential influence of external conditions on customer churn behavior. The findings highlight the importance of leveraging advanced machine learning techniques and comprehensive customer data to develop effective churn prevention strategies in the U.S. context. By accurately predicting customer churn, financial institutions can proactively identify at-risk customers, implement targeted retention strategies, and optimize resource allocation. The study discusses the limitations and potential future improvements, serving as a roadmap for researchers and practitioners to further advance the field of customer churn prediction in the evolving landscape of the U.S. banking and finance industry.
文摘How organizations analyze and use data for decision-making has been changed by cognitive computing and artificial intelligence (AI). Cognitive computing solutions can translate enormous amounts of data into valuable insights by utilizing the power of cutting-edge algorithms and machine learning, empowering enterprises to make deft decisions quickly and efficiently. This article explores the idea of cognitive computing and AI in decision-making, emphasizing its function in converting unvalued data into valuable knowledge. It details the advantages of utilizing these technologies, such as greater productivity, accuracy, and efficiency. Businesses may use cognitive computing and AI to their advantage to obtain a competitive edge in today’s data-driven world by knowing their capabilities and possibilities [1].
