Structural analysis and evolutionary exploration based on the research topic network of a field: a case in high-frequency trading

This study aims to systematically analyze the distribution dynamics of research topics and uncover the development state of the research in the specific field, which will provide a practical reference for developing professional subject knowledge services in the era of big data. The research topic network is constructed and analyzed using methods and tools of scientometrics. Basic statistics on network characteristics are performed to reveal the research status. Community detection, node ordering, and other steps are conducted to generate the evolutionary alluvial diagram. Then, relevant results are analyzed to explore the knowledge structure of the specific field and evolutionary context of research topics. Visualization analysis on the network structure of the latest period is executed to distinguish related concepts and predict the research trends. Taking high-frequency trading (HFT) as a case, this study achieves diversified scientometrics analysis of the research topic network and multi-dimensional evolution exploration of the relevant research topics in the specific field, which obtaining some knowledge insights. (1) Six major topics in HFT: liquidity & market microstructure, market efficiency, financial market, incomplete market, cointegration & price discovery, and event study. (2) The research focus about markets gradually transferred from international to emerging, meanwhile continuous attention to volatility/risk related issues. (3) The emphasis will change from theory to practice, technologies (big data, etc.) and theories (behavioral finance, etc.) will have more interaction with HFT. An effective research idea is proposed to reveal the knowledge structure of field and analyze the evolutionary context of research topics, which demonstrating the knowledge insights

The role of artificial intelligence in healthcare: a structured literature review

BACKGROUND/INTRODUCTION: Artificial intelligence (AI) in the healthcare sector is receiving attention from researchers and health professionals. Few previous studies have investigated this topic from a multi-disciplinary perspective, including accounting, business and management, decision sciences and health professions. METHODS: The structured literature review with its reliable and replicable research protocol allowed the researchers to extract 288 peer-reviewed papers from Scopus. The authors used qualitative and quantitative variables to analyse authors, journals, keywords, and collaboration networks among researchers. Additionally, the paper benefited from the Bibliometrix R software package. RESULTS: The investigation showed that the literature in this field is emerging. It focuses on health services management, predictive medicine, patient data and diagnostics, and clinical decision-making. The United States, China, and the United Kingdom contributed the highest number of studies. Keyword analysis revealed that AI can support physicians in making a diagnosis, predicting the spread of diseases and customising treatment paths. CONCLUSIONS: The literature reveals several AI applications for health services and a stream of research that has not fully been covered. For instance, AI projects require skills and data quality awareness for data-intensive analysis and knowledge-based management. Insights can help researchers and health professionals understand and address future research on AI in the healthcare field

Digital psykisk helse - Finnes det en sammenheng mellom behandlers intensjon til og faktiske bruk av digital behandling i psykisk helse og avhengighetsbehandling?

Etterspørselen fra pasienter med behov for psykisk helse og avhengighetsbehandling øker. Digital behandling fremheves av helsemyndigheter som en moderne og innovativ måte å møte dette behovet på. Imidlertid er det mer uklart hvordan klinikere i poliklinikker oppfatter bruken av digital behandling. Hensikten med studien har vært å identifisere hvor det er muligheter for intervensjon til å kunne påvirke bruken av digital behandling. Studien har undersøkt om det er sammenheng mellom behandleres intensjon til, og faktisk bruk av digital behandling. Studien har undersøkt hvilke bakgrunnsvariabler som påvirker bruken av digital behandling, og om det er en sammenheng mellom digital helsekompetanse og bruken av digital behandling. Studien har en kvantitativ metode, med Theory of planned behavoir (TPB) som teoretisk rammeverk. Behandlere (n=31) ved poliklinikker inne psykisk helse og avhengighetsbehandling på Sørlandet Sykehus har besvart et spørreskjema, og informasjon om bruk av digital behandling ble hentet fra DIPS. Sti-analyse, multippel lineær regresjonsanalyse og Goodman and Kruskal's gamma ble benyttet for å analysere data. Det er funnet en signifikant sammenheng mellom holdninger og intensjon til å benytte digital behandling, men det er ikke funnet en sammenheng mellom behandleres intensjon til og faktisk bruk av digital behandling. Studien viser at høyere alder øker sannsynligheten for å benytte digital behandling, og at digital behandling brukes mest i fagområdet psykisk helse for voksne. Videre studier trengs for å undersøke om bruken av digital behandling endrer seg, dersom mulighetene for intervensjon påvirkes

Comportamiento del efecto clúster hospital y los factores asociados a la mortalidad a largo plazo, después de un ingreso por exacerbación en epoc

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Medicina, Departamento de Medicina Preventiva y Salud Pública y Microbiología. Fecha de lectura: 23-09-2020INTRODUCCIÓN: Resultado del análisis exhaustivo de cohortes multicéntricas, históricas y periódicas de casos, usando aproximaciones multivariables multinivel, hemos abordado el tema de la variabilidad de los datos y encontrado, la presencia de un claro efecto clúster de hospital, que reduce drásticamente la variabilidad de los desenlaces encontrados (duración del ingreso, mortalidad y reingresos a 90 días(1)) en los datos crudos. Reconociendo la advertencia de Juan Merlo en su trabajo (1-3), referida a que la OR promedio es solo una aproximación inexacta y quizá no represente completamente la variabilidad geográfica real en áreas sanitarias, postulamos como hipótesis que el efecto clúster hospital, se mantiene a largo plazo sobre la mortalidad y que este efecto, en parte, se debe a factores asociados al contexto territorial y ambiental del Área de Salud, como la calidad del aire respirado. METODOLOGIA Con el objetivo de demostrar el efecto diferencial del clúster hospital, particularmente en relación con la mortalidad a largo plazo, en el paciente con EPOC, se plantea un estudio descriptivo observacional, con seguimiento prospectivo de mortalidad a largo plazo, para una cohorte de pacientes con EPOC, identificados durante un ingreso hospitalario por exacerbación de su enfermedad. La Tabla de datos contiene información disociada y mortalidad a largo plazo de 10.449 casos procedentes de 142 hospitales públicos españoles, a la que se han asociado datos agregados por localidad, de los registros diarios de emisiones obtenidos entre 2008 y 2011 (Período de reclutamiento de la cohorte) por las diferentes estaciones. La mortalidad a corto plazo (a 90 días del ingreso), fue informada por los responsables locales de investigación de la red de hospitales participantes, y contrastada con la información obtenida de los registros oficiales del índice nacional de defunciones (INDEF) desde octubre de 2008 a diciembre de 2015. Todas las variables fueron evaluadas respecto de la significancia (valor P) en la diferencia de su distribución por mortalidad intrahospitalaria, a 90 días, al año y a los 5 años, usando como estadísticos el chi-cuadrado de independencia y log-Rank test. Se construyó un modelo de supervivencia de riesgos proporcionales (Cox), y un modelo en regresión logística de mortalidad, calculando los coeficientes estandarizados y la curva ROC. RESULTADOS La media de seguimiento fue de 304·5 días posteriores al ingreso hospitalario, con un máximo de 7 años. Casi la mitad de la mortalidad total de la cohorte se produjo dentro de los 90 días posteriores al ingreso hospitalario a partir del cual fueron reclutados. La ponderación del efecto de cada uno de las variables finalmente retenidas por los modelos explicativos, a través de los coeficientes estandarizados obtenidos en la regresión, enfatiza el peso del perfil clínico grave (dimensión paciente), seguido de cerca por la exposición de micro partículas (dimensión local territorio) y las características del hospital (dimensión local hospital). El modelo obtenido logró discriminar la mortalidad a largo plazo, con un área de 0·71 y un IC 95% entre 0·69-0·72. CONCLUSIONES: Además de los determinantes clínicos de enfermedad, otros factores del contexto espacio/temporal externo al individuo, sumados a las condiciones de salud y atención sanitaria recibida, afectan la supervivencia/mortalidad a largo plazo y configuran lo que hemos llamado en nuestros trabajos previos efecto clúster hospitalEste trabajo ha sido financiado principalmente por fondos destinados al Grupo de investigación de la Red temática Enfermedades Respiratorias del Consorcio CIBER M. P, en el Hospital Universitario 12 de Octubre. También obtuvo financiación de ayudas a los proyectos FIS número PS 09/01763, PS 09/01787 y PS 09/00629 (Instituto de Salud Carlos III, Secretaría de Estado de Investigación, Desarrollo e Innovación y FEDER/FSE

Approche de prédiction par télésurveillance à base de Data Mining

Following the technological evolution, in particular the mobile approach, scientific research has been oriented towards the exploitation of these advances for remote predictive decision support. A major interest of researchers has had a great impact in the medical field because of its very positive influence for the care of the patient aimed at its assistance and the reduction of cases of death due to follow-up and the problem of time of treatment. emergency action. This is how telemedicine has become an issue of great importance, it is based on the manipulation and analysis of a large volume of medical data. The aim of this thesis is firstly to exploit a new approach to data analysis, namely Symbiotic Organisms Search (SOS) for Data Mining for data classification, and secondly, to propose improvements to this metaheuristic. This improvement relies on the integration of speed in SOS as a new parameter to explore the search space efficiently and avoiding premature convergence. We also develop a conceptual and practical architecture for applied telemedicine for decision support for the knowledge of the type of breast cancer (benign or malignant). This study allowed us to achieve excellent results and findings in terms of data classification

Enhancing the Benefits Management Model for Complex eHealth Efforts

This thesis suggests five ways to improve BM in complex eHealth efforts. First, the concept of BR was defined to clarify the existing conflation of the BR and BM concepts. Second, an extended and enhanced BMM was developed that incorporated the BM context, levels of complexity for both organizational and interorganizational initiatives, and the critical aspects of learning and governance. Third, three propositions concerning learning and governance in BM were suggested based on the new model, which can be used to inform future BM studies and guide empirical work. Fourth, the propositions were further translated into a six-question checklist to stimulate learning from the BM process itself. Finally, I provide suggestions for BM governance in interorganizational ICT efforts aiming to realize societal benefits.publishedVersio