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1.
J Vasc Res ; : 1-15, 2024 May 15.
Artículo en Inglés | MEDLINE | ID: mdl-38749406

RESUMEN

INTRODUCTION: Acquisition of a deeper understanding of microvascular function across physiological and pathological conditions can be complicated by poor accessibility of the vascular networks and the necessary sophistication or intrusiveness of the equipment needed to acquire meaningful data. Laser Doppler fluximetry (LDF) provides a mechanism wherein investigators can readily acquire large amounts of data with minor inconvenience for the subject. However, beyond fairly basic analyses of erythrocyte perfusion (fluximetry) data within the cutaneous microcirculation (i.e., perfusion at rest and following imposed challenges), a deeper understanding of microvascular perfusion requires a more sophisticated approach that can be challenging for many investigators. METHODS: This manuscript provides investigators with clear guidance for data acquisition from human subjects for full analysis of fluximetry data, including levels of perfusion, single- and multiscale Lempel-Ziv complexity (LZC) and sample entropy (SampEn), and wavelet-based analyses for the major physiological components of the signal. Representative data and responses are presented from a recruited cohort of healthy volunteers, and computer codes for full data analysis (MATLAB) are provided to facilitate efforts by interested investigators. CONCLUSION: It is anticipated that these materials can reduce the challenge to investigators integrating these approaches into their research programs and facilitate translational research in cardiovascular science.

2.
BMC Med Imaging ; 24(1): 86, 2024 Apr 10.
Artículo en Inglés | MEDLINE | ID: mdl-38600525

RESUMEN

Medical imaging AI systems and big data analytics have attracted much attention from researchers of industry and academia. The application of medical imaging AI systems and big data analytics play an important role in the technology of content based remote sensing (CBRS) development. Environmental data, information, and analysis have been produced promptly using remote sensing (RS). The method for creating a useful digital map from an image data set is called image information extraction. Image information extraction depends on target recognition (shape and color). For low-level image attributes like texture, Classifier-based Retrieval(CR) techniques are ineffective since they categorize the input images and only return images from the determined classes of RS. The issues mentioned earlier cannot be handled by the existing expertise based on a keyword/metadata remote sensing data service model. To get over these restrictions, Fuzzy Class Membership-based Image Extraction (FCMIE), a technology developed for Content-Based Remote Sensing (CBRS), is suggested. The compensation fuzzy neural network (CFNN) is used to calculate the category label and fuzzy category membership of the query image. Use a basic and balanced weighted distance metric. Feature information extraction (FIE) enhances remote sensing image processing and autonomous information retrieval of visual content based on time-frequency meaning, such as color, texture and shape attributes of images. Hierarchical nested structure and cyclic similarity measure produce faster queries when searching. The experiment's findings indicate that applying the proposed model can have favorable outcomes for assessment measures, including Ratio of Coverage, average means precision, recall, and efficiency retrieval that are attained more effectively than the existing CR model. In the areas of feature tracking, climate forecasting, background noise reduction, and simulating nonlinear functional behaviors, CFNN has a wide range of RS applications. The proposed method CFNN-FCMIE achieves a minimum range of 4-5% for all three feature vectors, sample mean and comparison precision-recall ratio, which gives better results than the existing classifier-based retrieval model. This work provides an important reference for medical imaging artificial intelligence system and big data analysis.


Asunto(s)
Inteligencia Artificial , Tecnología de Sensores Remotos , Humanos , Ciencia de los Datos , Almacenamiento y Recuperación de la Información , Redes Neurales de la Computación
3.
Int J Occup Saf Ergon ; 30(2): 559-570, 2024 Jun.
Artículo en Inglés | MEDLINE | ID: mdl-38576355

RESUMEN

The use of data analytics has seen widespread application in fields such as medicine and supply chain management, but their application in occupational safety has only recently become more common. The purpose of this scoping review was to summarize studies that employed analytics within establishments to reveal insights about work-related injuries or fatalities. Over 300 articles were reviewed to survey the objectives, scope and methods used in this emerging field. We conclude that the promise of analytics for providing actionable insights to address occupational safety concerns is still in its infancy. Our review shows that most articles were focused on method development and validation, including studies that tested novel methods or compared the utility of multiple methods. Many of the studies cited various challenges in overcoming barriers caused by inadequate or inefficient technical infrastructures and unsupportive data cultures that threaten the accuracy and quality of insights revealed by the analytics.


Asunto(s)
Salud Laboral , Humanos , Accidentes de Trabajo/prevención & control , Traumatismos Ocupacionales/prevención & control , Traumatismos Ocupacionales/epidemiología , Administración de la Seguridad/métodos
4.
J Pediatr ; 271: 114069, 2024 Apr 19.
Artículo en Inglés | MEDLINE | ID: mdl-38642884
5.
J Clin Anesth ; 95: 111441, 2024 Aug.
Artículo en Inglés | MEDLINE | ID: mdl-38452428

RESUMEN

STUDY OBJECTIVE: To examine the effects of a non-reactive carbon dioxide absorbent (AMSORB® Plus) versus a traditional carbon dioxide absorbent (Medisorb™) on the FGF used by anesthesia providers and an electronic educational feedback intervention using Carestation™ Insights (GE HealthCare) on provider-specific change in FGF. DESIGN: Prospective, single-center cohort study set in a greening initiative. SETTING: Operating room. PARTICIPANTS: 157 anesthesia providers (i.e., anesthesiology trainees, certified registered nurse anesthetists, and solo anesthesiologists). INTERVENTIONS: Intervention #1 was the introduction of AMSORB® Plus into 8 Aisys CS2, Carestation™ Insights-enabled anesthesia machines (GE HealthCare) at the study site. At the end of week 6, anesthesia providers were educated and given an environmentally oriented electronic feedback strategy for the next 12 weeks of the study (Intervention #2) using Carestation™ Insights data. MEASUREMENTS: The dual primary outcomes were the difference in average daily FGF during maintenance anesthesia between machines assigned to AMSORB® Plus versus Medisorb™ and the provider-specific change in average fresh gas flows after 12 weeks of feedback and education compared to the historical data. MAIN RESULTS: Over the 18-week period, there were 1577 inhaled anesthetics performed in the 8 operating rooms (528 for intervention 1, 1049 for intervention 2). There were 1001 provider days using Aisys CS2 machines and 7452 provider days of historical data from the preceding year. Overall, AMSORB® Plus was not associated with significantly less FGF (mean - 80 ml/min, 97.5% confidence interval - 206 to 46, P = .15). The environmentally oriented electronic feedback intervention was not associated with a significant decrease in provider-specific mean FGF (-112 ml/min, 97.5% confidence interval - 244 to 21, P = .059). CONCLUSIONS: This study showed that introducing a non-reactive absorbent did not significantly alter FGF. Using environmentally oriented electronic feedback relying on data analytics did not result in significantly reduced provider-specific FGF.


Asunto(s)
Anestésicos por Inhalación , Dióxido de Carbono , Quirófanos , Humanos , Estudios Prospectivos , Anestésicos por Inhalación/administración & dosificación , Retroalimentación , Anestesiólogos , Anestesiología/instrumentación , Anestesiología/educación , Enfermeras Anestesistas , Anestesia por Inhalación/instrumentación , Anestesia por Inhalación/métodos , Depuradores de Gas , Femenino
7.
Rev. esp. med. legal ; 50(1): 29-39, Ene.-Mar. 2024. tab, graf
Artículo en Inglés, Español | IBECS | ID: ibc-229295

RESUMEN

Introducción/objetivos la violencia contra la mujer sigue siendo un grave problema social y de salud a pesar de las medidas puestas en marcha en los últimos años. La exploración de las víctimas por el médico forense en los juzgados es de gran interés puesto que recibe información relacionada no solo con la agresión, sino también de su entorno social, familiar y económico. El objetivo es utilizar dicha información para identificar grupos de riesgo y mejorar/obtener las medidas necesarias. Material y métodos en este trabajo, el forense ha recogido, durante 8 años, una toma abundante de datos sobre las víctimas exploradas en L’Hospitalet de Llobregat. La muestra incluye 1.622 casos de mujeres víctimas de violencia de género. Se realiza un estudio descriptivo poblacional y de las lesiones. Resultados se exponen las principales variables estudiadas tanto socioeconómicas como referentes a la agresión en sí. Se trabaja también con base en la reentrada de las víctimas o repetición de las agresiones (revictimización), que son el 10,9% de la muestra. Finalmente, se presentan los resultados obtenidos tras aplicar técnicas de inteligencia artificial, en este caso, árboles de clasificación CaRT. Conclusiones con los resultados obtenidos concluimos que el tratamiento de la información recogida y sistematizada de la intervención médico-forense permite una mejor comprensión de la violencia sobre la mujer, de la que podemos extraer sugerencias sobre la adopción de medidas de atención y soporte a las víctimas y a los colectivos más vulnerables, así como sobre los recursos administrativos y la optimización de programas de prevención. (AU)


Introduction/objectives Violence against women is still a serious social and health problem, despite the measures implemented in recent years. The examination of the victims by the forensic doctor in the courts is of great interest since it provides information related not only to the aggression, but also to their social, family and economic environment. The objective is to use this information to identify groups at risk and improve/implement the necessary measures. Material and methods In this work, the forensic has collected, for eight years, abundant data on the victims examined in L'Hospitalet de Llobregat. The sample includes 1,622 cases of women who have been victims of gender violence. A descriptive study of the population and of the lesions has been carried out. Results The paper presents the main variables studied, both socioeconomic and referring to the aggression itself. This study also analyzes the reentry of the victims, the repetition of aggressions (revictimization), which are 10.9% of the sample. Finally, the results obtained after applying artificial intelligence techniques -in this case, CaRT classification trees- are presented. Conclusions With the results obtained, we conclude that the treatment of the information collected and systematized from the medical-forensic intervention allows a better understanding of Violence Against Women, from which we can extract suggestions on the adoption of care and support measures for the victims and the most vulnerable groups, as well as administrative resources and the optimization of prevention programs. (AU)


Asunto(s)
Humanos , Femenino , Adolescente , Adulto Joven , Adulto , Persona de Mediana Edad , Anciano , Violencia de Género/etnología , Violencia de Género/prevención & control , Inteligencia Artificial , Violencia contra la Mujer , Análisis de Datos , España
9.
Redox Biol ; 70: 103061, 2024 Apr.
Artículo en Inglés | MEDLINE | ID: mdl-38341954

RESUMEN

RATIONALE: MER proto-oncogene tyrosine kinase (MerTK) is a key receptor for the clearance of apoptotic cells (efferocytosis) and plays important roles in redox-related human diseases. We will explore MerTK biology in human cells, tissues, and diseases based on big data analytics. METHODS: The human RNA-seq and scRNA-seq data about 42,700 samples were from NCBI Gene Expression Omnibus and analyzed by QIAGEN Ingenuity Pathway Analysis (IPA) with about 170,000 crossover analysis. MerTK expression was quantified as Log2 (FPKM + 0.1). RESULTS: We found that, in human cells, MerTK is highly expressed in macrophages, monocytes, progenitor cells, alpha-beta T cells, plasma B cells, myeloid cells, and endothelial cells (ECs). In human tissues, MerTK has higher expression in plaque, blood vessels, heart, liver, sensory system, artificial tissue, bone, adrenal gland, central nervous system (CNS), and connective tissue. Compared to normal conditions, MerTK expression in related tissues is altered in many human diseases, including cardiovascular diseases, cancer, and brain disorders. Interestingly, MerTK expression also shows sex differences in many tissues, indicating that MerTK may have different impact on male and female. Finally, based on our proteomics from primary human aortic ECs, we validated the functions of MerTK in several human diseases, such as cancer, aging, kidney failure and heart failure. CONCLUSIONS: Our big data analytics suggest that MerTK may be a promising therapeutic target, but how it should be modulated depends on the disease types and sex differences. For example, MerTK inhibition emerges as a new strategy for cancer therapy due to it counteracts effect on anti-tumor immunity, while MerTK restoration represents a promising treatment for atherosclerosis and myocardial infarction as MerTK is cleaved in these disease conditions.


Asunto(s)
Proteínas Tirosina Quinasas Receptoras , Tirosina Quinasa c-Mer , Femenino , Humanos , Masculino , Apoptosis/genética , Tirosina Quinasa c-Mer/genética , Ciencia de los Datos , Células Endoteliales/metabolismo , Genómica , Neoplasias/metabolismo , Fagocitosis , Proteínas Proto-Oncogénicas/genética , Proteínas Proto-Oncogénicas/metabolismo , Proteínas Tirosina Quinasas Receptoras/genética , Proteínas Tirosina Quinasas Receptoras/metabolismo , Encefalopatías/metabolismo
10.
Cureus ; 16(2): e54144, 2024 Feb.
Artículo en Inglés | MEDLINE | ID: mdl-38357407

RESUMEN

BACKGROUND:  The conventional method of heparin and protamine management during cardiopulmonary bypass (CPB) is based on total body weight which fails to account for the heterogeneous response to heparin in each patient. On the other hand, the literature is inconclusive on whether individualized anticoagulation management based on real-time blood heparin concentration improves post-CBP outcomes. METHODS:  We searched databases of Medline, Excerpta Medica dataBASE (EMBASE), PubMed, Cumulative Index to Nursing and Allied Health Literature (CINHL), and Google Scholar, recruiting randomized controlled trials (RCTs) and prospective studies comparing the outcomes of dosing heparin and/or protamine based on measured heparin concentration versus patient's total body weight for CPB. Random effects meta-analyses and meta-regression were conducted to compare the outcome profiles. Primary endpoints include postoperative blood loss and the correlation with heparin and protamine doses, the reversal protamine and loading heparin dose ratio; secondary endpoints included postoperative platelet counts, antithrombin III, fibrinogen levels, activated prothrombin time (aPTT), incidences of heparin rebound, and re-exploration of chest wound for bleeding. RESULTS:  Twenty-six studies, including 22 RCTs and four prospective cohort studies involving 3,810 patients, were included. Compared to body weight-based dosing, patients of individualized, heparin concentration-based group had significantly lower postoperative blood loss (mean difference (MD)=49.51 mL, 95% confidence interval (CI): 5.33-93.71), lower protamine-to-heparin dosing ratio (MD=-0.20, 95% CI: -0.32 ~ -0.12), and higher early postoperative platelet counts (MD=8.83, 95% CI: 2.07-15.59). The total heparin doses and protamine reversal were identified as predictors of postoperative blood loss by meta-regression. CONCLUSIONS:  There was a significant correlation between the doses of heparin and protamine with postoperative blood loss; therefore, précised dosing of both could be critical for reducing bleeding and transfusion requirements. Data from the enrolled studies indicated that compared to conventional weight-based dosing, individualized, blood concentration-based heparin and protamine dosing may have outcome benefits reducing postoperative blood loss. The dosing calculation of heparin based on the assumption of a one-compartment pharmacokinetic/pharmacodynamic (PK/PD) model and linear relationship between the calculated dose and blood heparin concentration may be inaccurate. With the recent advancement of the technologies of machine learning, individualized, precision management of anticoagulation for CPB may be possible in the near future.

11.
Sensors (Basel) ; 24(3)2024 Feb 01.
Artículo en Inglés | MEDLINE | ID: mdl-38339683

RESUMEN

Managing modern museum content and visitor data analytics to achieve higher levels of visitor experience and overall museum performance is a complex and multidimensional issue involving several scientific aspects, such as exhibits' metadata management, visitor movement tracking and modelling, location/context-aware content provision, etc. In related prior research, most of the efforts have focused individually on some of these aspects and do not provide holistic approaches enhancing both museum performance and visitor experience. This paper proposes an integrated conceptualisation for improving these two aspects, involving four technological components. First, the adoption and parameterisation of four ontologies for the digital documentation and presentation of exhibits and their conservation methods, spatial management, and evaluation. Second, a tool for capturing visitor movement in near real-time, both anonymously (default) and eponymously (upon visitor consent). Third, a mobile application delivers personalised content to eponymous visitors based on static (e.g., demographic) and dynamic (e.g., visitor movement) data. Lastly, a platform assists museum administrators in managing visitor statistics and evaluating exhibits, collections, and routes based on visitors' behaviour and interactions. Preliminary results from a pilot implementation of this holistic approach in a multi-space high-traffic museum (MELTOPENLAB project) indicate that a cost-efficient, fully functional solution is feasible, and achieving an optimal trade-off between technical performance and cost efficiency is possible for museum administrators seeking unfragmented approaches that add value to their cultural heritage organisations.


Asunto(s)
Ciencia de los Datos , Museos , Documentación
12.
Heliyon ; 10(3): e25032, 2024 Feb 15.
Artículo en Inglés | MEDLINE | ID: mdl-38317951

RESUMEN

In the era of big data, data processing capability is key to gaining a competitive advantage for businesses. With appropriate technical and organizational resources in place, enterprises can extract considerable value from the vast amount of available data, thereby increasing their competitive advantage. Therefore, to utilize big data resources effectively, enterprises should focus on improving the intellectual abilities of big data analysts. Big data analytics intellectual capability (BDAIC) refers to the specialized skills and knowledge that employees of the enterprise possess, including technical, technical management, business, and relational knowledge, that would enable them to use analytics tools to accomplish organizational tasks and shape the core competitiveness of an enterprise. This study constructs a theoretical model that focuses on the mediating role of person-tool fit and examines the mechanisms by which BDAIC affects an enterprise's operational performance. The results show that BDAIC, which contains four basic categories of knowledge, positively influences an enterprise's operational efficiency. Additionally, person-tool matching mediates BDAIC's effect on an enterprise's operational performance. These findings explore the latest avenues of exploration in the research paradigm of big data analytics. Furthermore, this study has important implications for practitioners trying to use big data to improve business performance.

13.
Technol Health Care ; 2024 Jan 04.
Artículo en Inglés | MEDLINE | ID: mdl-38339943

RESUMEN

BACKGROUND: The term 'dementia' covers a range of progressive brain diseases from which many elderly people suffer. Traditional cognitive and pathological tests are currently used to detect dementia, however, applications using Artificial Intelligence (AI) methods have recently shown improved results from improved detection accuracy and efficiency. OBJECTIVE: This research paper investigates the efficacy of one type of data analytics called supervised learning to detect Alzheimer's disease (AD) - a common dementia condition. METHODS: The aim is to evaluate cognitive tests and common biological markers (biomarkers) such as cerebrospinal fluid (CSF) to develop predictive classification systems for dementia detection. RESULTS: A data analytics process has been proposed, implemented, and tested against real data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) repository. CONCLUSION: The models showed good power in predicting AD levels, notably from specified cognitive tests' scores and tauopathy related features.

14.
Data Brief ; 53: 110150, 2024 Apr.
Artículo en Inglés | MEDLINE | ID: mdl-38379883

RESUMEN

Poverty is the oldest social problem that ever existed and is difficult to reverse. It is multidimensional and unmeasurable. Thus, measuring by decomposing rural multidimensional poverty is critical. Most poverty studies are usually generic, exposed to large sampling errors, and intended for macroeconomic decisions. Thus, measuring poverty for a specific locality with various configurations is crucial for economic development. This work presents a processed and analyzed dataset from a huge community-based monitoring system of Goa, Camarines Sur. The local is situated in the poorest district, of the poorest province, in the poorest region of Luzon, Philippines. Research about poverty in this area is limited and measuring poverty at specific locality is scarce. The datasets contain the multidimensional poverty indicators, health, and nutrition, housing and settlement, water and sanitation, basic education from elementary to senior high school, income classifications, employment and livelihood, peace and order, summary of calamity occurrences experienced by residents, disaster risk reduction preparedness, figures of diagnostic analytics, tables of descriptive analytics, poverty analytics, measurement of decomposed poverty, summary of disaggregated configurations, graphs of predictive and prescriptive analytics, and population dynamics. This work is vital in analyzing poverty in rural and multidimensional approaches through poverty incidence, poverty gap, severity statistics, watts index, and classifications. It may also serve as a basis for measuring poverty from nearby regions and nations that use complete enumeration of its households and members. By utilizing the analyzed and processed data, further classifications and regressions can be done. It can be freely used by the government, private organizations, charitable institutions, businesses, academia, and researchers to target policies. An advantage of utilizing the dataset is to address multifaceted poverty that requires different interventions. It will facilitate the creation of programs to alleviate poverty and promote local economic development.

15.
Ann Med ; 56(1): 2314237, 2024 12.
Artículo en Inglés | MEDLINE | ID: mdl-38340309

RESUMEN

BACKGROUND: The construction of a robust healthcare information system is fundamental to enhancing countries' capabilities in the surveillance and control of hepatitis B virus (HBV). Making use of China's rapidly expanding primary healthcare system, this innovative approach using big data and machine learning (ML) could help towards the World Health Organization's (WHO) HBV infection elimination goals of reaching 90% diagnosis and treatment rates by 2030. We aimed to develop and validate HBV detection models using routine clinical data to improve the detection of HBV and support the development of effective interventions to mitigate the impact of this disease in China. METHODS: Relevant data records extracted from the Family Medicine Clinic of the University of Hong Kong-Shenzhen Hospital's Hospital Information System were structuralized using state-of-the-art Natural Language Processing techniques. Several ML models have been used to develop HBV risk assessment models. The performance of the ML model was then interpreted using the Shapley value (SHAP) and validated using cohort data randomly divided at a ratio of 2:1 using a five-fold cross-validation framework. RESULTS: The patterns of physical complaints of patients with and without HBV infection were identified by processing 158,988 clinic attendance records. After removing cases without any clinical parameters from the derivation sample (n = 105,992), 27,392 cases were analysed using six modelling methods. A simplified model for HBV using patients' physical complaints and parameters was developed with good discrimination (AUC = 0.78) and calibration (goodness of fit test p-value >0.05). CONCLUSIONS: Suspected case detection models of HBV, showing potential for clinical deployment, have been developed to improve HBV surveillance in primary care setting in China. (Word count: 264).


This study has developed a suspected case detection model for HBV, which can facilitate early identification and treatment of HBV in the primary care setting in China, contributing towards the achievement of WHO's elimination goals of HBV infections.We utilized the state-of-art natural language processing techniques to structure the data records, leading to the development of a robust healthcare information system which enhances the surveillance and control of HBV in China.


Asunto(s)
Macrodatos , Virus de la Hepatitis B , Humanos , Aprendizaje Automático , China/epidemiología , Medición de Riesgo
16.
J Hazard Mater ; 468: 133813, 2024 Apr 15.
Artículo en Inglés | MEDLINE | ID: mdl-38402679

RESUMEN

This systematic review addresses soil contamination by crude oil, a pressing global environmental issue, by exploring effective treatment strategies for sites co-contaminated with heavy metals and polycyclic aromatic hydrocarbons (PAHs). Our study aims to answer pivotal research questions: (1) What are the interaction mechanisms between heavy metals and PAHs in contaminated soils, and how do these affect the efficacy of different remediation methods? (2) What are the challenges and limitations of combined remediation techniques for co-contaminated soils compared to single-treatment methods in terms of efficiency, stability, and specificity? (3) How do various factors influence the effectiveness of biological, chemical, and physical remediation methods, both individually and combined, in co-contaminated soils, and what role do specific agents play in the degradation, immobilization, or removal of heavy metals and PAHs under diverse environmental conditions? (4) Do AI-powered search tools offer a superior alternative to conventional search methodologies for executing an exhaustive systematic review? Utilizing big-data analytics and AI tools such as Litmaps.co, ResearchRabbit, and MAXQDA, this study conducts a thorough analysis of remediation techniques for soils co-contaminated with heavy metals and PAHs. It emphasizes the significance of cation-π interactions and soil composition in dictating the solubility and behavior of these pollutants. The study pays particular attention to the interplay between heavy metals and PAH solubility, as well as the impact of soil properties like clay type and organic matter on heavy metal adsorption, which results in nonlinear sorption patterns. The research identifies a growing trend towards employing combined remediation techniques, especially biological strategies like biostimulation-bioaugmentation, noting their effectiveness in laboratory settings, albeit with potentially higher costs in field applications. Plants such as Medicago sativa L. and Solanum nigrum L. are highlighted for their effectiveness in phytoremediation, working synergistically with beneficial microbes to decompose contaminants. Furthermore, the study illustrates that the incorporation of biochar and surfactants, along with chelating agents like EDTA, can significantly enhance treatment efficiency. However, the research acknowledges that varying environmental conditions necessitate site-specific adaptations in remediation strategies. Life Cycle Assessment (LCA) findings indicate that while high-energy methods like Steam Enhanced Extraction and Thermal Resistivity - ERH are effective, they also entail substantial environmental and financial costs. Conversely, Natural Attenuation, despite being a low-impact and cost-effective option, may require prolonged monitoring. The study advocates for an integrative approach to soil remediation, one that harmoniously balances environmental sustainability, cost-effectiveness, and the specific requirements of contaminated sites. It underscores the necessity of a holistic strategy that combines various remediation methods, tailored to meet both regulatory compliance and the long-term sustainability of decontamination efforts.


Asunto(s)
Restauración y Remediación Ambiental , Metales Pesados , Hidrocarburos Policíclicos Aromáticos , Contaminantes del Suelo , Hidrocarburos Policíclicos Aromáticos/análisis , Contaminantes del Suelo/metabolismo , Metales Pesados/análisis , Biodegradación Ambiental , Suelo/química , Inteligencia Artificial
17.
Big Data ; 2024 Jan 09.
Artículo en Inglés | MEDLINE | ID: mdl-38193755

RESUMEN

The government sector has started adopting big data analytics capability (BDAC) to enhance its service delivery. This study examines the relationship between BDAC and decision-making capability (DMC) in the government sector. It investigates the mediation role of the cognitive style of decision makers and organizational culture in the relationship between BDAC and DMC utilizing the resource-based view of the firm theory. It further investigates the impact of BDAC on organizational performance (OP). This study attempts to extend existing research through significant findings and recommendations to enhance decision-making processes for a successful utilization of BDAC in the government sector. A survey method was adopted to collect data from government organizations in the United Arab Emirates, and partial least-squares structural equation modeling was deployed to analyze the collected data. The results empirically validate the proposed theoretical framework and confirm that BDAC positively impacts DMC via cognitive style and organizational culture, and in turn further positively impacting OP overall.

18.
Heliyon ; 10(1): e23775, 2024 Jan 15.
Artículo en Inglés | MEDLINE | ID: mdl-38226209

RESUMEN

The study unfolds with an acknowledgment of the extensive exploration of TRIZ components, spanning a solid philosophy, quantitative and inductive methods, and practical tools, over the years. While the adoption of Semantic TRIZ (S-TRIZ) in high-tech industries for system development, innovation, and production has increased, the application of AI technologies to specific TRIZ components remains unexplored. This systematic literature review is conducted to delve into the detailed integration of AI with TRIZ, particularly S-TRIZ. The results elucidate the current state of AI applications within TRIZ, identifying focal TRIZ components and areas requiring further study. Additionally, the study highlights the trending AI technologies in this context. This exploration serves as a foundational resource for researchers, developers, and inventors, providing valuable insights into the integration of AI technologies with TRIZ concepts. The study not only paves the way for the development and automation of S-TRIZ but also outlines limitations for future research, guiding the trajectory of advancements in this interdisciplinary field.

19.
Stud Health Technol Inform ; 310: 790-794, 2024 Jan 25.
Artículo en Inglés | MEDLINE | ID: mdl-38269917

RESUMEN

Two similar patients undergoing the same procedure might follow different pathways inside a hospital. Some of this variation is expected, but too much variation is associated with increased adverse events. Currently, there are no standard methods to establish when variability for any given clinical process becomes excessive. In this study we use process mining techniques to describe clinical processes and calculate and visualise clinical variability. We selected a sample of patients undergoing elective coronary bypass surgery from the MIMIC dataset, represented their clinical processes in the form of traces, and calculated variability metrics for each process execution and for the complete set of processes. We then analysed the subset of processes with the highest and lowest relative variability and compared their clinical outcomes. We established that processes with the greatest variability were associated with longer length of stay (LOS) with a dose-response relationship: the higher the variability, the longer the LOS. This study provides an effective way to estimate and visualise clinical variability and to understand its impact on patient relevant outcomes.


Asunto(s)
Instituciones de Salud , Hospitales , Humanos , Benchmarking , Tiempo de Internación
20.
JMIR Med Inform ; 12: e48273, 2024 Jan 12.
Artículo en Inglés | MEDLINE | ID: mdl-38214974

RESUMEN

BACKGROUND: The phenomenon of patients missing booked appointments without canceling them-known as Did Not Show (DNS), Did Not Attend (DNA), or Failed To Attend (FTA)-has a detrimental effect on patients' health and results in massive health care resource wastage. OBJECTIVE: Our objective was to develop machine learning (ML) models and evaluate their performance in predicting the likelihood of DNS for hospital outpatient appointments at the MidCentral District Health Board (MDHB) in New Zealand. METHODS: We sourced 5 years of MDHB outpatient records (a total of 1,080,566 outpatient visits) to build the ML prediction models. We developed 3 ML models using logistic regression, random forest, and Extreme Gradient Boosting (XGBoost). Subsequently, 10-fold cross-validation and hyperparameter tuning were deployed to minimize model bias and boost the algorithms' prediction strength. All models were evaluated against accuracy, sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve metrics. RESULTS: Based on 5 years of MDHB data, the best prediction classifier was XGBoost, with an area under the curve (AUC) of 0.92, sensitivity of 0.83, and specificity of 0.85. The patients' DNS history, age, ethnicity, and appointment lead time significantly contributed to DNS prediction. An ML system trained on a large data set can produce useful levels of DNS prediction. CONCLUSIONS: This research is one of the very first published studies that use ML technologies to assist with DNS management in New Zealand. It is a proof of concept and could be used to benchmark DNS predictions for the MDHB and other district health boards. We encourage conducting additional qualitative research to investigate the root cause of DNS issues and potential solutions. Addressing DNS using better strategies potentially can result in better utilization of health care resources and improve health equity.

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