A genetic fuzzy system for unstable angina risk assessment

BACKGROUND: Unstable Angina (UA) is widely accepted as a critical phase of coronary heart disease with patients exhibiting widely varying risks. Early risk assessment of UA is at the center of the management program, which allows physicians to categorize patients according to the clinical characteristics and stratification of risk and different prognosis. Although many prognostic models have been widely used for UA risk assessment in clinical practice, a number of studies have highlighted possible shortcomings. One serious drawback is that existing models lack the ability to deal with the intrinsic uncertainty about the variables utilized. METHODS: In order to help physicians refine knowledge for the stratification of UA risk with respect to vagueness in information, this paper develops an intelligent system combining genetic algorithm and fuzzy association rule mining. In detail, it models the input information’s vagueness through fuzzy sets, and then applies a genetic fuzzy system on the acquired fuzzy sets to extract the fuzzy rule set for the problem of UA risk assessment. RESULTS: The proposed system is evaluated using a real data-set collected from the cardiology department of a Chinese hospital, which consists of 54 patient cases. 9 numerical patient features and 17 categorical patient features that appear in the data-set are selected in the experiments. The proposed system made the same decisions as the physician in 46 (out of a total of 54) tested cases (85.2%). CONCLUSIONS: By comparing the results that are obtained through the proposed system with those resulting from the physician’s decision, it has been found that the developed model is highly reflective of reality. The proposed system could be used for educational purposes, and with further improvements, could assist and guide young physicians in their daily work

Incorporating comorbidities into latent treatment pattern mining for clinical pathways

AbstractIn healthcare organizational settings, the design of a clinical pathway (CP) is challenging since patients following a particular pathway may have not only one single first-diagnosis but also several typical comorbidities, and thus it requires different disciplines involved to put together their partial knowledge about the overall pathway. Although many data mining techniques have been proposed to discover latent treatment information for CP analysis and reconstruction from a large volume of clinical data, they are specific to extract nontrivial information about the therapy and treatment of the first-diagnosis. The influence of comorbidities on adopting essential treatments is crucial for a pathway but has seldom been explored. This study proposes to extract latent treatment patterns that characterize essential treatments for both first-diagnosis and typical comorbidities from the execution data of a pathway. In particular, we propose a generative statistical model to extract underlying treatment patterns, unveil the latent associations between diagnosis labels (including both first-diagnosis and comorbidities) and treatments, and compute the contribution of comorbidities in these patterns. The proposed model extends latent Dirichlet allocation with an additional layer for diagnosis modeling. It first generates a set of latent treatment patterns from diagnosis labels, followed by sampling treatments from each pattern. We verify the effectiveness of the proposed model on a real clinical dataset containing 12,120 patient traces, which pertain to the unstable angina CP. Three treatment patterns are discovered from data, indicating latent correlations between comorbidities and treatments in the pathway. In addition, a possible medical application in terms of treatment recommendation is provided to illustrate the potential of the proposed model. Experimental results indicate that our approach can discover not only meaningful latent treatment patterns exhibiting comorbidity focus, but also implicit changes of treatments of first-diagnosis due to the incorporation of typical comorbidities potentially

Towards Multi-perspective conformance checking with fuzzy sets

Conformance checking techniques are widely adopted to pinpoint possible discrepancies between process models and the execution of the process in reality. However, state of the art approaches adopt a crisp evaluation of deviations, with the result that small violations are considered at the same level of significant ones. This affects the quality of the provided diagnostics, especially when there exists some tolerance with respect to reasonably small violations, and hampers the flexibility of the process. In this work, we propose a novel approach which allows to represent actors' tolerance with respect to violations and to account for severity of deviations when assessing executions compliance. We argue that besides improving the quality of the provided diagnostics, allowing some tolerance in deviations assessment also enhances the flexibility of conformance checking techniques and, indirectly, paves the way for improving the resilience of the overall process management system.Comment: 15 pages, 5 figure

Identify facilitators and challenges in computerized checklist implementation

Safety checklists have been considered as a promising tool for improving patient safety for decades. Computerized checklists have better performance compared with paper-based checklists, though there are barriers to their adoption. Given previous literature, it is still unclear what assists implementations and their challenges. To address this issue, this paper summarizes the implementation of two successful computerized checklist implementations in two countries for two different clinical scenarios and analyzes their facilitators and challenges.</p

Re-ordered fuzzy conformance checking for uncertain clinical records

Modern hospitals implement clinical pathways to standardize patients’ treatments. Conformance checking techniques provide an automated tool to assess whether the actual executions of clinical processes comply with the corresponding clinical pathways. However, clinical processes are typically characterized by a high degree of uncertainty, both in their execution and recording. This paper focuses on uncertainty related to logging clinical processes. The logging of the activities executed during a clinical process in the hospital information system is often performed manually by the involved actors (e.g., the nurses). However, such logging can occur at a different time than the actual execution time, which hampers the reliability of the diagnostics provided by conformance checking techniques. To address this issue, we propose a novel conformance checking algorithm that leverages principles of fuzzy set theory to incorporate experts’ knowledge when generating conformance diagnostics. We exploit this knowledge to define a fuzzy tolerance in a time window, which is then used to assess the magnitude of timestamp violations of the recorded activities when evaluating the overall process execution compliance. Experiments conducted on a real-life case study in a Dutch hospital show that the proposed method obtains more accurate diagnostics than the state-of-the-art approaches. We also consider how our diagnostics can be used to stimulate discussion with domain experts on possible strategies to mitigate logging uncertainty in the clinical practice.</p

A Highly Active Star Decahedron Cu Nanocatalyst for Hydrocarbon Production at Low Overpotentials

The electrochemical carbon dioxide reduction reaction (CO_2RR) presents a viable approach to recycle CO_2 gas into low carbon fuels. Thus, the development of highly active catalysts at low overpotential is desired for this reaction. Herein, a high‐yield synthesis of unique star decahedron Cu nanoparticles (SD‐Cu NPs) electrocatalysts, displaying twin boundaries (TBs) and multiple stacking faults, which lead to low overpotentials for methane (CH_4) and high efficiency for ethylene (C_2H_4) production, is reported. Particularly, SD‐Cu NPs show an onset potential for CH_4 production lower by 0.149 V than commercial Cu NPs. More impressively, SD‐Cu NPs demonstrate a faradaic efficiency of 52.43% ± 2.72% for C_2H_4 production at −0.993 ± 0.0129 V. The results demonstrate that the surface stacking faults and twin defects increase CO binding energy, leading to the enhanced CO_2RR performance on SD‐Cu NPs

Patients\u27 Acceptance of Smartphone Health Technology for Chronic Disease Management: A Theoretical Model and Empirical Test

Les quatre textes ont en commun de présenter certaines évolutions récentes de l’histoire politique en Allemagne. Ils prennent tous position face à trois tournants historiographiques. La notion d’histoire culturelle du politique peut servir d’emblème au premier de ces tournants : le politique est envisagé non plus comme une succession d’événements ni comme le fruit de déterminations structurelles dont il serait la superstructure ou l’écume, mais comme l’expression de valeurs et de procédures o..

Presión arterial del recién nacido de madres preeclámpticas eclámpticas Hospital Regional de Cajamarca 2016

La presión arterial del recién nacido está sujeta a variaciones por diversas causas entre ellas la patología materna conocida como la preeclampsia El objetivo de la presente investigación fue determinar y analizar la presión arterial durante las primeras en las primeras 36 horas de vida, en forma periódica, de los recién nacidos hijos de madres preeclámpticas y la presión arterial de los recién nacidos hijos de madres no preeclámpticas, atendidos en el Hospital Regional de Cajamarca durante los meses de enero- febrero del año 2016. El estudio fue de tipo descriptivo, comparativo, correlacional, no experimental, de corte transversal y naturaleza prospectiva. Se incluyeron a 75 recién nacidos, hijos de madres preeclámpticas, y 75 recién nacidos de madres no preeclámpticas que cumplían con los criterios de inclusión y exclusión, atendidos en el Hospital Regional de Cajamarca durante los meses de enero y febrero del año 2016. Se realizaron mediciones de presión arterial en tres ocasiones: 12, 24 y 36 horas de nacimiento. Los resultados encontrados en la presente investigación fueron: edad materna promedio de 28.5 años, en gran porcentaje multíparas (49% y 48% en ambos grupos) y la mayoría con el diagnóstico de preeclampsia severa (52%). Los neonatos nacieron predominantemente por vía vaginal (56% y 86,8%), a término (76% y 94,7%), de sexo masculino con un porcentaje de 54,7% en el primer grupo y sexo femenino en el segundo grupo con 42%; con peso y talla al nacer promedio de 3026.7 gramos y 49.2 centímetros, respectivamente, con un puntaje de Apgar en su mayoría de 8 al minuto y 9 a los cinco minutos, con una cantidad mínima de neonatos con administración prenatales de corticoides (0,7%). Se determinó que la presión arterial se incrementó en las primeras 12 horas de vida en los recién nacidos de madres preeclámpticas, regularizándose, la mayoría, a las 36 horas, continuando elevada en un buen porcentaje (30,7%). En cuanto a los recién nacidos de madres no preeclámpticas una considerable cantidad tuvo presión arterial normal y los que tuvieron presión arterial alta en las primeras horas, se regularizaron a las 36 horasTesi

A Highly Active Star Decahedron Cu Nanocatalyst for Hydrocarbon Production at Low Overpotentials

The electrochemical carbon dioxide reduction reaction (CO_2RR) presents a viable approach to recycle CO_2 gas into low carbon fuels. Thus, the development of highly active catalysts at low overpotential is desired for this reaction. Herein, a high‐yield synthesis of unique star decahedron Cu nanoparticles (SD‐Cu NPs) electrocatalysts, displaying twin boundaries (TBs) and multiple stacking faults, which lead to low overpotentials for methane (CH_4) and high efficiency for ethylene (C_2H_4) production, is reported. Particularly, SD‐Cu NPs show an onset potential for CH_4 production lower by 0.149 V than commercial Cu NPs. More impressively, SD‐Cu NPs demonstrate a faradaic efficiency of 52.43% ± 2.72% for C_2H_4 production at −0.993 ± 0.0129 V. The results demonstrate that the surface stacking faults and twin defects increase CO binding energy, leading to the enhanced CO_2RR performance on SD‐Cu NPs