Role of machine learning techniques in classification of peripheral pulse morphology

Recording of peripheral pulse serves as a very important and essential non-invasive tool used widely by doctors for the diagnosis of various diseases. The morphology of pulse is seen to vary as a function of time in a given individual and also from individual to individual. There are many variations in morphological patterns of peripheral pulse in different disease conditions, which lead to difficulty in accurate diagnosis. The peripheral pulse waveforms are extracted from radial arteries as time series data using a peripheral pulse analyzer which is designed on the principle of impedance plethysmography. It was first introduced by Nyober in the mid-nineteen hundreds and ameliorated further by Kubicek. It involves the recording of the instantaneous blood volume by the measurement of electrical impedance as a function of time. Therefore, the study of peripheral pulse morphology has gained much attention in the past few years among researchers. Physiological variability is one of the recent investigations added during the last two decades for the objective assessment of autonomic function and the assessment of prognosis in severe sicknesses namely myocardial infarction, diabetic neuropathy, etc. In addition to heart rate variability studied worldwide, few researchers have studied blood pressure variability and peripheral blood flow variability. In this computer era, artificial intelligence and machine learning techniques have become more important day-by- day, and different types of algorithms were used for the identification of hidden patterns from plethysmographic observations on the radial pulse such as support vector machine as well as crisp and fuzzy clustering. Eight patterns were classified with a yield of 80%–90% and helped with the diagnosis of disorders such as myocardial infarction, pulmonary tuberculosis, coronary artery disorders, cirrhosis of the liver, and bronchial asthma. This paper briefly describes the use of machine learning techniques for the classification of peripheral pulse morphologies.

Research on the influencing factors of the organizational ability of medical ethics committee: a case study of tertiary hospitals in eastern China / 中华医院管理杂志

Objective:Given the indispensable role played by medical ethics committees in protecting the rights and interests of subjects and researchers, such committees in China are found with weak organizational capacity. This study was designed to analyze the driving factors to improve their organizational capacity.Methods:Form July to September 2020, a survey and interview of 71 tertiary A hospitals in eastern China were conducd, and a total 60 valid samples were obtained. Through crisp-se qualitative comparative analysis(csQCA), the medical ethics committees of 60 tertiary hospitals were analyzed, and the factors affecting the organizational capabilities of the medical ethics committees and their combinations were explored.Results:Protection of rights and interests was key to medical ethics committee′s organizational capacity, while incompetency of the members and the irregular review process were sufficient reasons for the weak organizational capacity. There were six combinations of sufficient conditions for the medical ethics committee to be strong in organizational capacities, which could be explained by the three models of member capacity-orientation, system-process-orientation, and resource-system-orientation.Conclusions:The medical ethics committees are recommended to enhance members capacity, enhance team building; complete rules and regulations, and process mechanism; clarify organization structure, and optimize resource matching, in an effort to enhance the organizational capacity of these committees in China.

Técnicas de minería de datos aplicadas al diagnóstico de entidades clínicas / Data mining techniques aplied to diagnosys of clinical entities

Disminuir el error médico y mejorar los procesos de salud es prioridad de todo el personal sanitario. En este contexto surgen los Sistemas Clínicos de Soporte para la Toma de Decisiones (CDSS), los cuales son un componente fundamental en la informatización de la capa clínica. Con la evolución de las tecnologías gran cantidad de datos han podido ser estudiados y clasificados a partir de la minería de datos. Una de las principales ventajas de la utilización de esta, en los CDSS, ha sido su capacidad de generar nuevos conocimientos. Con este fin se propone, mediante la combinación de dos modelos matemáticos, cómo se puede contribuir al diagnóstico de enfermedades usando técnicas de minería de datos. Para mostrar los modelos utilizados se tomó como caso de estudio la hipertensión arterial. El desarrollo de la investigación se rige por la metodología más utilizada actualmente en los procesos de Descubrimiento de Conocimiento en Bases de Datos: CRISP-DM 1.0, y se apoya en la herramienta de libre distribución WEKA 3.6.2, de gran prestigio entre las utilizadas para el modelado de minería de datos. Como resultados se obtuvieron diversos patrones de comportamiento con relación a los factores de riesgo a sufrir hipertensión mediante técnicas de minería de datos(AU)

Reduce medical errors and improve health processes is a priority of all health personnel. In this context arise the Clinical Support Systems for Decision Making (CDSS), which are a key component in computerization of the clinical layer. With the evolution of technologies, large amounts of data have been studied and classified based on data mining. One of the main advantages of using this in the CDSS, has been its ability to generate new knowledge. For this purpose, this paper presents, by combining two mathematical models, a way to contribute to the diagnosis of diseases using data mining techniques. Hypertension was taken as a case study to show the models used. The research development methodology follows the most used processes of knowledge discovery in databases: CRISP-DM 1.0, and relies on the free distribution tool WEKA 3.6.2. We obtained different patterns of behavior in relation to risk factors for developing hypertension using data mining techniques(AU)

Cysteine-Rich Secretory Proteins (CRISP) and their role in mammalian fertilization

Epididymal protein CRISPI is a member of the CRISP (Cysteine-RIch Secretory proteins) family and is involved in sperm-egg fusion through its interaction with complementary sites on the egg surface. Results from our laboratory have shown that this binding ability resides in a 12-amino-acid region corresponding to a highly conserved motif of the CRISP family, named Signature 2 (S2). In addition to this, our results revealed that CRISP1 could also be involved in the previous step of sperm binding to the zona pellucida, identifying a novel role for this protein in fertilization. As another approach to elucidate the participation of CRISP1 in fertilization, a mouse line containing a targeted disruption of CRISP1 was generated. Although CRISP1-deficient mice exhibited normal fertility, CRISP1-defficient sperm presented a decreased level of protein tyrosine phosphorylation during capacitation, and an impaired ability to fertilize both zona-intact and zona-free eggs in vitro, confirming the proposed roles for the protein in fertilization. Evidence obtained in our laboratory indicated that testicular CRISP2 would also be involved in sperm-egg fusion. Competition assays between CRISP1 and CRISP2, as well as the comparison of their corresponding S2 regions, suggest that both proteins bind to common complementary sites in the egg. Together, these results suggest a functional cooperation between CRISP1 and CRISP2 to ensure the success of fertilization.