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Technology and Health Care is intended to serve as a forum for the presentation of original articles and technical notes, observing rigorous scientific standards. Furthermore, upon invitation, reviews, tutorials, discussion papers and minisymposia are featured.
The following types of contributions and areas are considered:
1. Original articles:
Technology development in medicine: New concepts, procedures and devices associated with the use of technology in medical research and clinical practice are presented to a readership with a widespread background in engineering and/or medicine.
Significance of medical technology and informatics for healthcare: The appropriateness, efficacy and usefulness deriving from the application of engineering methods, devices and informatics in medicine and with respect to public health are discussed.
2. Technical notes:
Short communications on novel technical developments with relevance for clinical medicine.
3. Reviews and tutorials (upon invitation only):
Tutorial and educational articles for persons with a primarily medical background on principles of engineering with particular significance for biomedical applications and vice versa are presented.
4. Minisymposia (upon invitation only):
Under the leadership of a Special Editor, controversial issues relating to healthcare are highlighted and discussed by various authors.
Abstract: BACKGROUND: This research studies a medical staff scheduling problem, which includes government regulations and hospital regulations (hard constraints) and the medical staff’s preferences (soft constraints). OBJECTIVE: The objective function is to minimize the violations (or dissatisfaction) of medical staff’s preferences. METHODS: This study develops three variants of the three-phase modified bat algorithms (BAs), named BA1, BA2, and BA3, in order to satisfy the hard constraints, minimize the dissatisfaction of the medical staff and balance the workload of the medical staff. To ensure workload balance, this study balances the workload among medical staff without…increasing the objective function values. RESULTS: Based on the numerical results, the BA3 outperforms the BA1, BA2, and particle swarm optimization (PSO). The robustness of the BA1, BA2, and BA3 is verified. Finally, conclusions are drawn, and directions for future research are highlighted. CONCLUSIONS: The framework of this research can be used as a reference for other hospitals seeking to determine their future medical staff schedule.
Keywords: Bat algorithm (BA), medical staff scheduling, nurse scheduling/rostering, medical staff’s preferences, workload balance
Abstract: BACKGROUND: Venous oxygen saturation reflects venous oxygenation status and can be used to assess treatment and prognosis in critically ill patients. A novel method that can measure central venous oxygen saturation (ScvO 2 ) non-invasively may be beneficial and has the potential to change the management routine of critically ill patients. OBJECTIVE: The study aims to evaluate the potential of sublingual venous oxygen saturation (SsvO 2 ) to be used in the estimation of ScvO 2 . METHODS: We have developed two different approaches…to calculate SsvO 2 . In the first one, near-infrared spectroscopy (NIRS) measurements were performed directly on the sublingual veins. In the second approach, NIRS spectra were acquired from the sublingual tissue apart from the sublingual veins, and arterial oxygen saturation was measured using a pulse oximeter on the fingertip. RESULTS: Twenty-six healthy subjects were included in the study. In the first and second approaches, average SsvO 2 values were 75.0% ± 1.8 and 75.8% ± 2.1, respectively. The results of the two different approaches were close to each other and similar to ScvO 2 of healthy persons (> 70%). CONCLUSION: Oxygen saturation of sublingual veins has the potential to be used in intensive care units, non-invasively and in real-time, to estimate ScvO 2 .
Abstract: BACKGROUND: Current Electronic Health Record (EHR) systems are built using different data representation and information models, which makes difficult achieving information exchange. OBJECTIVE: Our aim was to propose a scalable architecture that allows the integration of information from different EHR systems. METHODS: A cloud-based EHR interoperable architecture is proposed through the standardization and integration of patient electronic health records. The data is stored in a cloud repository with high availability features. Stakeholders can retrieve the patient EHR by requesting only to the integrated data repository. The OpenEHR two-level approach is applied according to…the HL7-FHIR standards. We validated our architecture by comparing it with 5 different works (CHISTAR, ARIEN, DIRAYA, LLPHR and INEHRIS) using a set of selected axes and a scoring method. RESULTS: The problem was reduced to a single point of communication between each EHR system and the integrated data repository. By combining cloud computing paradigm with selected health informatics standards, we obtained a generic and scalable architecture that complies 100% with interoperability requisites according to the evaluation framework applied. CONCLUSIONS: The architecture allowed the integration of several EHR systems, adapting them with the use of standards and ensuring the availability thanks to cloud computing features.
Keywords: Electronic health records, Cloud EHR, interoperability, health informatics standards, HL7 FHIR, OpenEHR
Abstract: BACKGROUND: Breast cancer is a major disease causing panic among women worldwide. Since gene mutations are the root cause for cancer development, analyzing gene expressions can give more insights into various phenotype of cancer treatments. Breast Cancer subtype prediction from gene expression data can provide more information for cancer treatment decisions. OBJECTIVE: Gene expressions are complex for analysis due to its high dimensional nature. Machine learning algorithms such as k-Nearest Neighbors, Support Vector Machine (SVM) and Random Forest are used with selection of features for prediction of breast cancer subtypes. Prediction accuracy of the existing methods…are affected due to high dimensional nature of gene expressions. The objective of the work is to propose an efficient algorithm for the prediction of breast cancer subtypes from gene expression. METHODS: For subtype prediction, a novel Hubness Weighted Support Vector machine algorithm (HWSVM) using bad hubness score as a weight measure to handle the outliers in the data has been proposed. Based on the various subtypes, features are projected into seven different feature sets and Ensemble based Hubness Aware Weighted Support Vector Machine (HWSVMEns) is implemented for breast cancer subtype prediction. RESULTS: The proposed algorithms have been compared with the classical SVM and other traditional algorithms such as Random Forest, k-Nearest Neighbor algorithms and also with various gene selection methods. CONCLUSIONS: Experimental results show that the proposed HWSVM outperforms other algorithms in terms of accuracy, precision, recall and F1 score due to the hubness weightage scheme and the ensemble approach. The experiments have shown an average accuracy of 92% across various gene expression datasets.
Keywords: Breast cancer subtypes, high-dimensional data, hubness, gene selection, support vector machine
Abstract: BACKGROUND: Autonomic function can be estimated non-invasively using heart rate variability (HRV). HRV of patients undergoing coronary artery bypass grafting (CABG) is investigated in time-domain and frequency-domain before and after CABG to study the effect of operation on the status of patients. OBJECTIVE: The main purpose of this work is to evaluate the effect of CABG surgery on patients with ischemic heart disease (IHD) before operation, and to monitor the status of patients on day 6 and day 30 after the CABG operation. METHODS: The statistical signal characterization (SSC) technique is used in…this work in order to derive different morphology-based parameters to indirectly describe time-domain and frequency-domain HRV parameters in 24 patients undergoing CABG operation, before the operation (Group 1: G1), 6 days after operation (Group 2: G2) and 30 days after operation (Group 3: G3). The data is obtained from the Sultan Qaboos University Hospital in Oman. RESULTS: The SSC parameters Mean(mt) and Mean(dt) are reduced in all 24 patients and in 23 out of 24 patients in G2 compared to G1 (6-days after operation compared with before operation), respectively. Comparing G3 to G1 the reduction in Mean(mt) and Mean(dt) is noted in 18 of the 24 patients. CONCLUSIONS: The parameters Mean(mt) and Mean(dt) are successful parameters to follow the HRV for patients undergoing CABG surgery. A relation between those SSC parameters and the HRV time-domain and frequency-domain parameters is investigated in this paper to understand the physiological behavior of the patients.
Keywords: HRV, CABG, statistical signal characterization, signal morphology, time-domain, frequency-domain
Abstract: BACKGROUND: Intracranial pressure (ICP) and arterial blood pressure (ABP) are related to each other through cerebral autoregulation. Central venous pressure (CVP) is often measured to estimate cardiac filling pressures as an approximate measure for the volume status of a patient. Prior modelling efforts have formalized the functional relationship between CVP, ICP and ABP. However, these models were used to explain short segments of data during controlled experiments and have not yet been used to explain the slowly evolving ICP increase that occurs typically in patients after aneurysmal subarachnoid hemorrhage (SAH). OBJECTIVE: To analyze the functional relationship…between ICP, ABP and CVP recorded from SAH patients in the first five days after aneurysm. METHODS: Two methods were used to elucidate this relationship on the running average of the signals: First, using Spearman correlation coefficients calculated over 30 min segments Second, for each patient, linear state space models of ICP as the output and ABP and CVP as inputs were estimated. RESULTS: The mean and variance of the data and the correlation coefficients between ICP-ABP and ICP-CVP vary over time as the patient progresses through their stay in the ICU. On average, after an SAH event, the models show that a) ABP is the bigger driver of changes in ICP than CVP and that increasing ABP leads to reduction in ICP and (b) increasing CVP leads to an increase in ICP. CONCLUSIONS: Finding a) agrees with the hypothesis that patients with subarachnoid hemorrhage have defective autoregulation, and b) agrees with the positive correlation observed between central venous pressure and intracranial pressure in the literature.
Keywords: Intracranial pressure, arterial blood pressure, central venous pressure, relationship, correlation, state space models
Abstract: BACKGROUND: Autistic Spectrum Disorder (ASD) is a neurodevelopment condition that is normally linked with substantial healthcare costs. Typical ASD screening techniques are time consuming, so the early detection of ASD could reduce such costs and help limit the development of the condition. OBJECTIVE: We propose an automated approach to detect autistic traits that replaces the scoring function used in current ASD screening with a more intelligent and less subjective approach. METHODS: The proposed approach employs deep neural networks (DNNs) to detect hidden patterns from previously labelled cases and controls, then applies the knowledge…derived to classify the individual being screened. Specificity, sensitivity, and accuracy of the proposed approach are evaluated using ten-fold cross-validation. A comparative analysis has also been conducted to compare the DNNs’ performance with other prominent machine learning algorithms. RESULTS: Results indicate that deep learning technologies can be embedded within existing ASD screening to assist the stakeholders in the early identification of ASD traits. CONCLUSION: The proposed system will facilitate access to needed support for the social, physical, and educational well-being of the patient and family by making ASD screening more intelligent and accurate.
Keywords: Autism, ASD screening, detection systems, machine learning, medical screening, deep neural network
Abstract: BACKGROUND: The analysis of brain activity in different conditions is an important research area in neuroscience. OBJECTIVE: This paper analyzed the correlation between the brain and skin activities in rest and stimulations by information-based analysis of electroencephalogram (EEG) and galvanic skin resistance (GSR) signals. METHODS: We recorded EEG and GSR signals of eleven subjects during rest and auditory stimulations using three pieces of music that were differentiated based on their complexity. Then, we calculated the Shannon entropy of these signals to quantify their information contents. RESULTS: The results showed that…music with greater complexity has a more significant effect on altering the information contents of EEG and GSR signals. We also found a strong correlation (r = 0.9682) among the variations of the information contents of EEG and GSR signals. Therefore, the activities of the skin and brain are correlated in different conditions. CONCLUSION: This analysis technique can be utilized to evaluate the correlation among the activities of various organs versus brain activity in different conditions.
Abstract: BACKGROUND: The early detection of human breast cancer represents a great chance of survival. Malignant tissues have more water content and higher electrolytes concentration while they have lower fat content than the normal. These cancer biochemical characters provide malignant tissue with high electric permittivity (ε ´ ) and conductivity (σ ). OBJECTIVE: To examine if the dielectric behavior of normal and malignant tissues at low frequencies (α dispersion) will lead to the threshold (separating) line between them and find the threshold values of capacitance and resistance. These data…are used as input for deep learning neural networks, and the outcomes are normal or malignant. METHODS: ε ´ and σ in the range of 50 Hz to 100 KHz for 15 human malignant tissues and their corresponding normal ones have been measured. The separating line equation between the two classes is found by mathematical calculations and verified via support vector machine (SVM). Normal range and the threshold value of both normal capacitance and resistance are calculated. RESULTS: Deep learning analysis has an accuracy of 91.7%, 85.7% sensitivity, and 100% specificity for instant and automatic prediction of the type of breast tissue, either normal or malignant. CONCLUSIONS: These data can be used in both cancer diagnosis and prognosis follow-up.
Keywords: Breast cancer, dielectric properties, deep learning neural network, α dispersion