Query: Health assessment

title: Health evaluation: health assessment for people with ostomies.
abstract: OBJECTIVES to perform an evaluation study of the Ostomy Care Service in Brazil. METHODS a qualitative methodology was used, with a collaborative approach of the participants using the service, based on the seven elements proposed by Thurston and Ramaliu, which include service modeling and evaluation model proposal. Document analysis, direct observation, consensus workshops, and interviews with key informants were used. RESULTS the description and construction of the theoretical logical model of the ostomy care service, the development of evaluative questions, the construction of the analysis and judgment matrix, and the evaluation proposal were presented. Final Considerations: it was found that it is feasible to systematically evaluate the service based on its structural elements, such as its theory, material and human resources. The proposed evaluation aims to provide support for improving the quality of care.

title: TeleVital: Enhancing the quality of contactless health assessment
abstract: In the midst of rising positive cases of COVID 19, the hospitals face a newfound difficulty to prioritize on their patients and accommodate them. Moreover, crowding of patients at hospitals pose a threat to the healthcare workers and other patients at the hospital. With that in mind, a non contact method of measuring the necessary vitals such as heart rate, respiratory rate and SPO$_2$ will prove highly beneficial for the hospitals to tackle this issue. This paper discusses our approach in achieving the non contact measurement of vitals with the sole help of a webcam and further our design of an e hospital platform for doctors and patients to attend appointments virtually. The platform also provides the doctor with an option to provide with voice based prescriptions or digital prescriptions, to simplify the daily, exhausting routine of a doctor.

title: Data Driven Health Assessment in Flight Control System
abstract: The aircraft critical system's health state will affect flight safety dramatically, such as flight control system, and its health state awareness or assessment is very important to avoid flight accident. A data driven health assessment based on fuzzy comprehensive evaluation and rough set reduction is proposed for flight control system. Through the working principle and failure mode analysis, the system's characteristic parameters are constructed to represent health state, and then the comprehensive health index construction is proposed to quantify health state. In the end, case calculation based on some aircraft's flight data is presented to show the effectiveness of the proposed method.

title: Equipment Health Assessment Based on Improved Incremental Support Vector Data Description
abstract: With the rapid development of Internet of Things and big data, health assessment of equipment is receiving more attention in recent years. It is critical to bridge the gap between real time production data and health status evaluation, which helps maintenance team understand the health status of equipment exactly, and then make rational maintenance plans. For this purpose, this paper proposes a framework to realize real time equipment health assessment with health status quantitatively characterized by health degree (HD) The proposed framework begins with removing redundant features using a principal component analysis (PCA) method. Then, to represent the optimal operation status, a support vector data description (SVDD) algorithm is employed for extracting normal observations in the offline part. Thereafter, HD is introduced based on the Euclidean distance between current observation and the normal sample set. In order to achieve online updating of the normal sample set, and promote accuracy and computational efficiency of the offline part, an improved incremental SVDD algorithm based on adaptive threshold {N} (NISVDD) is proposed. A case study is used to demonstrate the effectiveness of the proposed framework and model using a benchmark dataset of rolling bearing. Results suggest that the proposed framework is effective, and PCA shows good potential to extract features and keep most of the original information. The proposed NISVDD model is able to trace the dynamics of equipment health status for whole run to failure process, and outperforms other models in both accuracy and computational efficiency.

title: Structural Health Assessment of Gas Turbine Engine Carcass
abstract: Gas turbine engine carcass is one of the major structures that supports/houses the main engine rotors, stator blade rings, interface features to mount the engine onto the airframe and accessories, viz. gearbox, electronic control units, pipelines, actuators, and so on. All the reaction forces generated by the engine are finally grounded to the airframe through this engine carcass. Specific to aero engines, designers make every effort to reduce the weight of this engine carcass so as to maximize thrust to weight ratio of the engine while maintaining the stiffness requirement. The present paper deals with an integrated approach for health assessment of the engine carcass. The theoretical and experimental studies undertaken for the engine carcass and rotor for enhancing the diagnostic reliability have been put forward. The requisite instrumentation and signal processing to assess the health have also been included. The decision making for furthering of the test and remedial measures are also brought out.

title: State of Health Assessment for Aero Engine Based on Density Distance Clustering and Fuzzy Bayesian Risk
abstract: State of health (SOH) assessment for aero engine can effectively reduce maintenance cost and operational risk, and is also a significant part of the prognostics and health management (PHM) system. However, the current SOH assessment is usually closely coordinated with other parts of PHM to achieve specific functions. This is not conducive to generalizing the function of SOH assessment. Therefore, this paper proposes a data driven framework of SOH assessment that mainly includes data preprocessing, pseudo label generation, weight assignment and feature selection, and assessment, which enhances the systematicness of SOH assessment. A combination model based on density distance clustering and fuzzy Bayesian risk models is designed to generate a pseudo label, select optimal parameter subset, and assign weight. Then, two assessment indicators including state membership degree and health degree are produced based on two fuzzy models for horizontal and vertical comparisons. These two indicators expand the dimensions and perspectives of SOH measurement, which can more comprehensively characterize the health state of the engine. Finally, the correctness and effectiveness of the proposed methodology are verified by the widely used Commercial Modular Aero Propulsion System Simulation (C MAPSS) dataset.

title: Inherent occupational health assessment index for research and development stage of process design
abstract: Occupational health analyses the job and health relationships, in particular the chronic health influence of chemicals to workers. In most of the previous works for inherent hazards assessment, the focus was put on safety aspect; with very minor elements of health are taken into account. Most of the earlier design stage assessment methods focus only on the hazard identification and assessment part; but not the risk mitigation element. This study aims to create an assessment method that integrate the existing inherent hazards assessment index which is the Inherent Occupational Health Index (IOHI) with suitable Layers of Protection (LOP) strategies for each of the enlisted hazard parameters. The IOHI considers the health hazards existed during research and development stage. Risk prevention and minimization strategies are proposed according to each hazard parameters and levels. Scores are also assigned to each LOP strategies. Integration of occupational health hazard assessment index with the prevention strategy is achieved through a designed formula. The proposed method provides users with list of suitable counter measures for each hazard parameter, and each option offers varied level of effectiveness. The proposed index is applied to two process routes of methyl methacrylate production which are ethylene via propionaldehyde based route (C2/PA) and propylene based route (C3) The results revealed that the process routes are in the category of "safe" when the proposed control strategies are evaluated in the assessment.

title: Regression Approach to Power Transformer Health Assessment Using Health Index

title: A novel weight derived method and its application in graduate students' physical health assessment
abstract: In multiattribute decision making, the analytic network process (ANP) is an important methodology to derive the subjective weights of attributes when the dependence and feedback relations exist between attributes, and the number of attributes should be no more than seven in a comparison matrix. To reduce the dimensions of attributes, we propose a hybrid hesitant fuzzy linguistic factor analysis method to cluster the attributes into main factors. The method takes multiple forms of decision making information into consideration, such as single linguistic terms, hesitant fuzzy linguistic terms, and numeric values. Meanwhile, the objective weights of the main factors are obtained as well. As for the subjective weights of main factors, the incomplete probabilistic linguistic ANP is developed after improving the incomplete probabilistic linguistic preference relation with multiplicative consistency. At last, the final weights of the main factors are calculated by combining the objective and subjective weights. A questionnaire survey about assessing the weights of the main factors influencing graduate students' physical health is designed to explain the application of the proposed methodology. To sum up, the main importance and contributions of this study are as following: (1) developing a hybrid hesitant fuzzy linguistic factor analysis method and incomplete probabilistic linguistic ANP, (2) proposing a novel weight derived method from both objective and subjective perspectives, and (3) applying it to graduate students' physical health assessment.

title: Ambient context based modeling for health risk assessment using deep neural network
abstract: Context computing is a branch of ambient intelligence (AmI) research, which has been rapidly emerging in the support of intelligent smart health platform solution. To develop reliable ambient computing using the hybrid peer to peer network and Internet of Things, machine learning, deep learning, artificial intelligence, and context awareness have been applied. This study proposes an ambient context based modeling for a health risk assessment using deep neural network. In the proposed method, we collected medical information from chronic disease patients such as EMR, PHR, and medical histories, as well as environmental data from a health platform. Subsequently, heterogeneous data are integrated through selecting, cleaning, modeling, and evaluating the collected raw data and then the context is created. The structured input data such as a sensor data are normalized by transforming the time domain data to the frequency domain information. Using a deep neural network, the normalized data are applied to create an ambient context. A deep neural network is composed of the following three layers: input layers with treated and untreated data; hidden layers where connection strength is trained as a weight; and output layers of trained results. In the deep neural network layers, the control of the weight of training data enables repeated learning to create an ambient context pattern. Using an ontology inference engine, unstructured/structured data, including individual health data and environmental information, and their context is presented as ontology metadata. In the knowledge base, hidden association relationships are discovered through mining. To inform the individual health conditions exposed to the individual environmental contexts, a health risk assessment model is developed with a set of the ambient context pattern learned with metadata and a deep neural network. The Minkowski distance formula, which defines a normalized geometrical distance between two nodes, is used to measure the similarity between the patients with chronical disease and the individual user based on the context. In the proposed model, the risk is represented as a similarity based index. The risk assessment model can be implemented into the individual risk alert/prevention system. The model may significantly impact the healthcare industry as well as ambient intelligence research, thus contributing to improve the quality of human life of the future society.