Exploring inter-trial coherence for inner speech classification in EEG-based brain-computer interface.

Objective. In recent years, electroencephalogram (EEG)-based brain-computer interfaces (BCIs) applied to inner speech classification have gathered attention for their potential to provide a communication channel for individuals with speech disabilities. However, existing methodologies for this task fall short in achieving acceptable accuracy for real-life implementation. This paper concentrated on exploring the possibility of using inter-trial coherence (ITC) as a feature extraction technique to enhance inner speech classification accuracy in EEG-based BCIs.Approach. To address the objective, this work presents a novel methodology that employs ITC for feature extraction within a complex Morlet time-frequency representation. The study involves a dataset comprising EEG recordings of four different words for ten subjects, with three recording sessions per subject. The extracted features are then classified using k-nearest-neighbors (kNNs) and support vector machine (SVM).Main results. The average classification accuracy achieved using the proposed methodology is 56.08% for kNN and 59.55% for SVM. These results demonstrate comparable or superior performance in comparison to previous works. The exploration of inter-trial phase coherence as a feature extraction technique proves promising for enhancing accuracy in inner speech classification within EEG-based BCIs.Significance. This study contributes to the advancement of EEG-based BCIs for inner speech classification by introducing a feature extraction methodology using ITC. The obtained results, on par or superior to previous works, highlight the potential significance of this approach in improving the accuracy of BCI systems. The exploration of this technique lays the groundwork for further research toward inner speech decoding.

European Nephrologists' Attitudes toward the Application of Artificial Intelligence in Clinical Practice: A Comprehensive Survey.

INTRODUCTION: The rapid advancement of artificial intelligence and big data analytics, including descriptive, diagnostic, predictive, and prescriptive analytics, has the potential to revolutionize many areas of medicine, including nephrology and dialysis. Artificial intelligence and big data analytics can be used to analyze large amounts of patient medical records, including laboratory results and imaging studies, to improve the accuracy of diagnosis, enhance early detection, identify patterns and trends, and personalize treatment plans for patients with kidney disease. Additionally, artificial intelligence and big data analytics can be used to identify patients' treatment who are not receiving adequate care, highlighting care inefficiencies in the dialysis provider, optimizing patient outcomes, reducing healthcare costs, and consequently creating values for all the involved stakeholders. OBJECTIVES: We present the results of a comprehensive survey aimed at exploring the attitudes of European physicians from eight countries working within a major hemodialysis network (Fresenius Medical Care NephroCare) toward the application of artificial intelligence in clinical practice. METHODS: An electronic survey on the implementation of artificial intelligence in hemodialysis clinics was distributed to 1,067 physicians. Of the 1,067 individuals invited to participate in the study, 404 (37.9%) professionals agreed to participate in the survey. RESULTS: The survey showed that a substantial proportion of respondents believe that artificial intelligence has the potential to support physicians in reducing medical malpractice or mistakes. CONCLUSION: While artificial intelligence's potential benefits are recognized in reducing medical errors and improving decision-making, concerns about treatment plan consistency, personalization, privacy, and the human aspects of patient care persist. Addressing these concerns will be crucial for successfully integrating artificial intelligence solutions in nephrology practice.

Advancements in Home-Based Devices for Detecting Obstructive Sleep Apnea: A Comprehensive Study.

Obstructive Sleep Apnea (OSA) is a respiratory disorder characterized by frequent breathing pauses during sleep. The apnea-hypopnea index is a measure used to assess the severity of sleep apnea and the hourly rate of respiratory events. Despite numerous commercial devices available for apnea diagnosis and early detection, accessibility remains challenging for the general population, leading to lengthy wait times in sleep clinics. Consequently, research on monitoring and predicting OSA has surged. This comprehensive paper reviews devices, emphasizing distinctions among representative apnea devices and technologies for home detection of OSA. The collected articles are analyzed to present a clear discussion. Each article is evaluated according to diagnostic elements, the implemented automation level, and the derived level of evidence and quality rating. The findings indicate that the critical variables for monitoring sleep behavior include oxygen saturation (oximetry), body position, respiratory effort, and respiratory flow. Also, the prevalent trend is the development of level IV devices, measuring one or two signals and supported by prediction software. Noteworthy methods showcasing optimal results involve neural networks, deep learning, and regression modeling, achieving an accuracy of approximately 99%.

Local Weather Station Design and Development for Cost-Effective Environmental Monitoring and Real-Time Data Sharing.

Current weather monitoring systems often remain out of reach for small-scale users and local communities due to their high costs and complexity. This paper addresses this significant issue by introducing a cost-effective, easy-to-use local weather station. Utilizing low-cost sensors, this weather station is a pivotal tool in making environmental monitoring more accessible and user-friendly, particularly for those with limited resources. It offers efficient in-site measurements of various environmental parameters, such as temperature, relative humidity, atmospheric pressure, carbon dioxide concentration, and particulate matter, including PM 1, PM 2.5, and PM 10. The findings demonstrate the station's capability to monitor these variables remotely and provide forecasts with a high degree of accuracy, displaying an error margin of just 0.67%. Furthermore, the station's use of the Autoregressive Integrated Moving Average (ARIMA) model enables short-term, reliable forecasts crucial for applications in agriculture, transportation, and air quality monitoring. Furthermore, the weather station's open-source nature significantly enhances environmental monitoring accessibility for smaller users and encourages broader public data sharing. With this approach, crucial in addressing climate change challenges, the station empowers communities to make informed decisions based on real-time data. In designing and developing this low-cost, efficient monitoring system, this work provides a valuable blueprint for future advancements in environmental technologies, emphasizing sustainability. The proposed automatic weather station not only offers an economical solution for environmental monitoring but also features a user-friendly interface for seamless data communication between the sensor platform and end users. This system ensures the transmission of data through various web-based platforms, catering to users with diverse technical backgrounds. Furthermore, by leveraging historical data through the ARIMA model, the station enhances its utility in providing short-term forecasts and supporting critical decision-making processes across different sectors.

Implications of Artificial Intelligence Algorithms in the Diagnosis and Treatment of Motor Neuron Diseases-A Review.

Motor neuron diseases (MNDs) are a group of chronic neurological disorders characterized by the progressive failure of the motor system. Currently, these disorders do not have a definitive treatment; therefore, it is of huge importance to propose new and more advanced diagnoses and treatment options for MNDs. Nowadays, artificial intelligence is being applied to solve several real-life problems in different areas, including healthcare. It has shown great potential to accelerate the understanding and management of many health disorders, including neurological ones. Therefore, the main objective of this work is to offer a review of the most important research that has been done on the application of artificial intelligence models for analyzing motor disorders. This review includes a general description of the most commonly used AI algorithms and their usage in MND diagnosis, prognosis, and treatment. Finally, we highlight the main issues that must be overcome to take full advantage of what AI can offer us when dealing with MNDs.

Low limb prostheses and complex human prosthetic interaction: A systematic literature review.

A few years ago, powered prostheses triggered new technological advances in diverse areas such as mobility, comfort, and design, which have been essential to improving the quality of life of individuals with lower limb disability. The human body is a complex system involving mental and physical health, meaning a dependant relationship between its organs and lifestyle. The elements used in the design of these prostheses are critical and related to lower limb amputation level, user morphology and human-prosthetic interaction. Hence, several technologies have been employed to accomplish the end user's needs, for example, advanced materials, control systems, electronics, energy management, signal processing, and artificial intelligence. This paper presents a systematic literature review on such technologies, to identify the latest advances, challenges, and opportunities in developing lower limb prostheses with the analysis on the most significant papers. Powered prostheses for walking in different terrains were illustrated and examined, with the kind of movement the device should perform by considering the electronics, automatic control, and energy efficiency. Results show a lack of a specific and generalised structure to be followed by new developments, gaps in energy management and improved smoother patient interaction. Additionally, Human Prosthetic Interaction (HPI) is a term introduced in this paper since no other research has integrated this interaction in communication between the artificial limb and the end-user. The main goal of this paper is to provide, with the found evidence, a set of steps and components to be followed by new researchers and experts looking to improve knowledge in this field.

Effectiveness of cold HD for the prevention of HD hypotension and mortality in the general HD population.

BACKGROUND: Cold hemodialysis (HD) prevented intradialysis hypotension (IDH) in small, short-term, randomized trials in selected patients with IDH. Whether this treatments prevents IDH and mortality in the HD population at large is unknown. METHODS: We investigated the relationship between dialysate temperature and the risk of IDH, i.e. nadir blood pressure <90 mmHg (generalized estimating equation model) and all-cause mortality (Cox's regression) in an incident cohort of HD patients (n = 8071). To control for confounding by bias by indication and other factors we applied instrumental variables adjusting for case mix at facility level. RESULTS: Twenty-seven percent of patients in the study cohort were systematically treated with a dialysate temperature ≤35.5°C. Over a median follow-up of 13.6 months (interquartile range 5.2-26.1 months), a 0.5°C reduction of the dialysate temperature was associated with a small (-2.4%) reduction of the risk of IDH [odds ratio (OR) 0.976, 95% confidence interval (CI) 0.957-0.995, P = .013]. In case-mix, facility-level adjusted analysis, the association became much stronger (OR 0.67, 95% CI 0.63-0.72, risk reduction = 33%, P < .001). In contrast, colder dialysate temperature had no effect on mortality both in the unadjusted [hazard ratio (HR) (0.5°C decrease) 1.074, 95% CI 0.972-1.187, P = .16] and case-mix-adjusted analysis at facility level (HR 1.01, 95% CI 0.88-1.16, P = .84). Similar results were registered in additional analyses by instrumental variables applying the median dialysate temperature or the facility percentage of patients prescribed a dialysate temperature <36°C. Further analyses restricted to patients with recurrent IDH fully confirmed these findings. CONCLUSIONS: Cold HD was associated with IDH in the HD population but had no association with all-cause mortality.

A systematic mapping literature review of education around sexual and gender diversities.

Education around sexual and gender identities is highly important to understand diversity and prevent discrimination, violence, and even murder. Nevertheless, educational institutions around the world are lacking a curriculum that explicitly includes diversity and acknowledges the LGBTQ+ community, a minority that over the years has been facing consequences from this exclusion. This study presents a detailed description of the process applied to analyze the studies using a systematic mapping literature review, as well as the positive results found from those educational institutions that started their path to inclusion around sexual and gender diversities through their curricula. The research questions targeted in this work are: What is being taught in educational institutions regarding sexual and gender diversities? What are the approaches used inside the classrooms to teach sexual and gender diversities? Which students are receiving education regarding sexual and gender diversities? Is there a technological approach and/or tool used to teach sexual and gender diversities? After applying the filtering processes, 69 studies were selected from five different online libraries: ACM, DOAJ, Lens.org, SCOPUS, and SpringerLink. The conclusions made from the findings of this review are that those studies that do tackle concerns around the topic have proven to benefit the LGBTQ+ community, the education around sexual and gender diversities predominates within the healthcare field, there are a lack of studies around this topic in Latin American countries, and technological tools are minimally used during the teaching processes.

Vaccination against SARS-CoV-2 in Haemodialysis Patients: Spike's Ab Response and the Influence of BMI and Age.

Patients with chronic kidney disease (CKD-5D) in dialysis have been associated with higher rates of SARS-CoV-2 infection. Objective: To identify the CKD-5D patients' immune system behavior regarding the Pfizer-BioNTech (BNT162b2 mRNA) vaccine (Comirnaty©). This was a multicenter study carried out in 38 dialysis units in NephroCare Portugal. Eligible patients from two cohorts­one composed of completely vaccinated patients with Comirnaty© (vaccinated group) against a second cohort of patients who recovered from SARS-CoV-2 infection (control group)­were selected through representative sampling for each cohort. Humoral response was assessed at 3 (t0) and 6 months (t1) after complete vaccination and, in the control group, 6 months after COVID-19 recovery. In the vaccinated group, at t0, the median anti-Spike IgG level was 1120 AU/mL and, at t1, all participants' antibody level decreased to a median of 455 AU/mL. In the control group, the median serum SARS-CoV-2 antibodies level was 1836 AU/mL. In the vaccinated group, at t0, patients < 70 years presented a significantly (p = 0.002) higher level of anti-Spike IgG titres. In contrast, older patients from the control group presented a significantly (p = 0.038) higher IgG. No correlation was found between age and anti-Spike IgG antibodies level in any of the studied groups. Patients with a higher body mass index showed a greater immune response in both the vaccinated and control group, although without significance. We concluded that, in the vaccinated group, elderly patients developed a lower immune response than younger patients and the levels of anti-Spike IgG antibodies declined faster between t0 and t1, while in the control group, the oldest and overweight patients developed the best humoral response.

Development of a Sensing Platform Based on Hands-Free Interfaces for Controlling Electronic Devices.

Hands-free interfaces are essential to people with limited mobility for interacting with biomedical or electronic devices. However, there are not enough sensing platforms that quickly tailor the interface to these users with disabilities. Thus, this article proposes to create a sensing platform that could be used by patients with mobility impairments to manipulate electronic devices, thereby their independence will be increased. Hence, a new sensing scheme is developed by using three hands-free signals as inputs: voice commands, head movements, and eye gestures. These signals are obtained by using non-invasive sensors: a microphone for the speech commands, an accelerometer to detect inertial head movements, and an infrared oculography to register eye gestures. These signals are processed and received as the user's commands by an output unit, which provides several communication ports for sending control signals to other devices. The interaction methods are intuitive and could extend boundaries for people with disabilities to manipulate local or remote digital systems. As a study case, two volunteers with severe disabilities used the sensing platform to steer a power wheelchair. Participants performed 15 common skills for wheelchair users and their capacities were evaluated according to a standard test. By using the head control they obtained 93.3 and 86.6%, respectively for volunteers A and B; meanwhile, by using the voice control they obtained 63.3 and 66.6%, respectively. These results show that the end-users achieved high performance by developing most of the skills by using the head movements interface. On the contrary, the users were not able to develop most of the skills by using voice control. These results showed valuable information for tailoring the sensing platform according to the end-user needs.

A State-of-the-Art Review of EEG-Based Imagined Speech Decoding.

Currently, the most used method to measure brain activity under a non-invasive procedure is the electroencephalogram (EEG). This is because of its high temporal resolution, ease of use, and safety. These signals can be used under a Brain Computer Interface (BCI) framework, which can be implemented to provide a new communication channel to people that are unable to speak due to motor disabilities or other neurological diseases. Nevertheless, EEG-based BCI systems have presented challenges to be implemented in real life situations for imagined speech recognition due to the difficulty to interpret EEG signals because of their low signal-to-noise ratio (SNR). As consequence, in order to help the researcher make a wise decision when approaching this problem, we offer a review article that sums the main findings of the most relevant studies on this subject since 2009. This review focuses mainly on the pre-processing, feature extraction, and classification techniques used by several authors, as well as the target vocabulary. Furthermore, we propose ideas that may be useful for future work in order to achieve a practical application of EEG-based BCI systems toward imagined speech decoding.

Use of Robotic Platforms as a Tool to Support STEM and Physical Education in Developed Countries: A Descriptive Analysis.

The lack of interest of children at school is one of the biggest problems that Mexican education faces. Two important factors causing this lack of interest are the predominant methodology used in Mexican schools and the technology as a barrier for attention. The methodology that institutions have followed has become an issue because of its very traditional approach, with the professor giving all the theoretical material to the students while they listen and memorize the contents, and, if we add the issue of the growing access to technological devices for students, children carrying a phone are more likely to be distracted. This study aims to integrate technology through assistive robots as a beneficial tool for educators, in order to improve the attention span of students by making the learning process in multiple areas of the Mexican curriculum more dynamic, therefore obtaining better results. To prove this, four different approaches were implemented; three in elementary schools and one in higher education: the LEGO® robotic kit and the NAO robot for STEM (science, technology, engineering, and mathematics) teaching, the NAO robot for physical education (PE), and the PhantomX Hexapod, respectively. Each of these technological approaches was applied by considering both control and experimental groups, in order to compare the data and provide conclusions. Finally, this study proves that the attention span is indeed improved as a result of implementing robotic platforms during the teaching process, allowing the children to become more motivated during their PE class and become more proactive and retain more information during their STEM classes.

Effectiveness of COVID-19 vaccines in a large European hemodialysis cohort.

Background: Hemodialysis patients have high-risk of severe SARS-CoV-2 infection but were unrepresented in randomized controlled trials evaluating the safety and efficacy of COVID-19 vaccines. We estimated the real-world effectiveness of COVID-19 vaccines in a large international cohort of hemodialysis patients. Methods: In this historical, 1:1 matched cohort study, we included adult hemodialysis patients receiving treatment from December 1, 2020, to May 31, 2021. For each vaccinated patient, an unvaccinated control was selected among patients registered in the same country and attending a dialysis session around the first vaccination date. Matching was based on demographics, clinical characteristics, past COVID-19 infections and a risk score representing the local background risk of infection at vaccination dates. We estimated the effectiveness of mRNA and viral-carrier COVID-19 vaccines in preventing infection and mortality rates from a time-dependent Cox regression stratified by country. Results: In the effectiveness analysis concerning mRNA vaccines, we observed 850 SARS-CoV-2 infections and 201 COVID-19 related deaths among the 28110 patients during a mean follow up of 44 ± 40 days. In the effectiveness analysis concerning viral-carrier vaccines, we observed 297 SARS-CoV-2 infections and 64 COVID-19 related deaths among 12888 patients during a mean follow up of 48 ± 32 days. We observed 18.5/100-patient-year and 8.5/100-patient-year fewer infections and 5.4/100-patient-year and 5.2/100-patient-year fewer COVID-19 related deaths among patients vaccinated with mRNA and viral-carrier vaccines respectively, compared to matched unvaccinated controls. Estimated vaccine effectiveness at days 15, 30, 60 and 90 after the first dose of a mRNA vaccine was: for infection, 41.3%, 54.5%, 72.6% and 83.5% and, for death, 33.1%, 55.4%, 80.1% and 91.2%. Estimated vaccine effectiveness after the first dose of a viral-carrier vaccine was: for infection, 38.3% without increasing over time and, for death, 56.6%, 75.3%, 92.0% and 97.4%. Conclusion: In this large, real-world cohort of hemodialyzed patients, mRNA and viral-carrier COVID-19 vaccines were associated with reduced COVID-19 related mortality. Additionally, we observed a strong reduction of SARS-CoV-2 infection in hemodialysis patients receiving mRNA vaccines.

Multiple single cannulation technique of arteriovenous fistula: A randomized controlled trial.

INTRODUCTION: Despite the impact needling has had on vascular access survival and patient outcome, there is no universal or standardized method proposed for proper cannulation. Rigorous studies are needed, examining cannulation practices, and challenges to achieving complication-free cannulation. METHODS: This randomized, open-label trial was conducted at 18 dialysis units owned by a large private dialysis provider operating in Portugal. Eligible patients were adults on chronic hemodialysis, with a new arteriovenous fistula (AVF); cannulated for at least 4 weeks complication-free. Patients were randomly assigned in a 1:1 ratio to one of three cannulation techniques (CT): Multiple Single cannulation Technique (MuST), rope-ladder (RLC), and buttonhole (BHC). The primary endpoint was AVF primary patency at 1 year. FINDINGS: One hundred seventy-two patients were enrolled between March 2014 and March 2017. Fifty-nine patients were allocated to MuST, 56 to RLC, and 57 to BHC. MuST and RLC were associated with a better AVF primary patency than BHC. Primary patency at 12 months was 76.3% in MuST, 59.6% in BHC, and 76.8% in RLC group. Mean AVF survival times were 10.5 months (95% CI = 9.6, 11.3) in the MuST group, 10.4 months (95% CI = 9.5, 11.2) in RLC, and 9.5 months (95% CI = 8.6, 10.4) in BHC. BHC was a significant risk predictor for AVF survival with 2.13 times more events than the other two CT (HR 2.13; 95% CI = 1.07, 4.21; p = 0.03). DISCUSSION: MuST was easy to implement without a diagram and there is no need to use blunt needles. This study showed MuST was efficacious and safe in maintaining the longevity of AVF in dialysis patients.

[Vaccination Against COVID-19 in a Network of Hemodialysis Units in Portugal: A Promising Experience].

INTRODUCTION: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) responsible for coronavirus disease 2019 (COVID-19) seriously affected Portugal, particularly in the elderly population with a high number of comorbidities, including patients with chronic kidney disease in stage 5 (CKD-5D) undergoing a regular dialysis program. The aims of this study were to identify the impact of vaccination on the incidence of new daily cases and mortality in the CKD-5D population. MATERIAL AND METHODS: Prospective, observational study, involving patients with CKD-5D from 38 NephroCare clinics, Portugal. Daily SARS-CoV-2 infections and mortality among these patients was compared with the incidence in the general population. Three periods were analysed: before vaccination, during the vaccination process, and a third period after complete vaccination with the Pfizer-BioNTech' Comirnaty® vaccine. The primary outcome was infection by the SARS-CoV-2 virus and the secondary outcome was death associated with the infection. RESULTS: A total of 4617 patients (average of 69.37 years of age) were analysed. During the first period, there was a significantly higher COVID-19 incidence of 14.9% in patients with CKD-5D compared with the general population (7.9%; p < 0.001). During the fifteen days after the complete vaccination, results reverted to a significant decrease in COVID-19 cases was observed (p < 0.001). The mortality rate among CKD-5D was significantly higher than in the general population (p < 0.001). DISCUSSION: A high incidence rate of infection was observed in CKD-5D patients, probably due to several factors such as advanced age, number of comorbidities, inability to remain in confinement, among others. After vaccination there was a significant reduction of new cases. CONCLUSION: Vaccination significantly reduced SARS-CoV-2 infection in patients with CKD-5D.

Introdução: Portugal foi atingido severamente pela síndrome respiratória aguda grave (SARS-CoV-2), responsável pela doença por coronavírus 2019 (COVID-19). A população idosa e com comorbilidades, na qual estão incluídas as pessoas com doença renal crónica em estádio 5 em programa regular de hemodiálise (CKD-5D), foi particularmente afetada. Definimos como objetivos deste estudo a identificação do impacto da vacinação no surgimento de novos casos diários e a sua influência na mortalidade na população CKD-5D.Material e Métodos: Estudo observacional, prospetivo, envolvendo doentes com CKD-5D de 38 clínicas da NephroCare, Portugal. A infeção e a mortalidade diárias por SARS-CoV-2 destes doentes foi comparada com a taxa de incidência na população em geral. Foram analisados três períodos: antes da vacinação, durante, e um terceiro período após a vacinação completa pela vacina Comirnaty ®, da Pfizer-BioNTech. Foi considerado desfecho primário a infeção pelo vírus SARS-CoV-2 e como desfecho secundário, a morte associada à infeção.Resultados: Foram analisados 4617 doentes com uma média de 69,37 anos. Durante o primeiro período, verificou-se uma taxa de incidência de COVID 19 de 14,9%, significativamente maior nos doentes CKD-5D quando comparado com a taxa de incidência de 7,9% da população em geral (p < 0,001). Durante os quinze dias após a vacinação completa observámos uma reversão, com diminuição significativa de casos de COVID-19 (p < 0,001). A mortalidade atribuível a COVID-19 no grupo CKD-5D foi significativamente superior ao da população em geral (p < 0,001).Discussão: Observámos uma elevada taxa de incidência de infeção nos doentes CKD-5D, presumivelmente correlacionada com vários fatores como a idade avançada, as comorbilidades, a impossibilidade de confinar, entre outros. Após a vacinação verificou-se uma redução significativa de novos casos.Conclusão: A vacinação reduziu significativamente a infeção por SARS-CoV-2 nos doentes CKD-5D.

Potential Applications and Functional Roles of Exosomes in Cardiometabolic Disease.

Despite diagnostic and therapeutic advances, cardiometabolic disease remains the leading cause of death worldwide. Extracellular vesicles (EVs), which include exosomes and microvesicles, have gained particular interest because of their role in metabolic homeostasis and cardiovascular physiology. Indeed, EVs are recognized as critical mediators of intercellular communication in the cardiovascular system. Exosomes are naturally occurring nanocarriers that transfer biological information in the setting of metabolic abnormalities and cardiac dysfunction. The study of these EVs can increase our knowledge on the pathophysiological mechanisms of metabolic disorders and their cardiovascular complications. Because of their inherent properties and composition, exosomes have been proposed as diagnostic and prognostic biomarkers and therapeutics for specific targeting and drug delivery. Emerging fields of study explore the use exosomes as tools for gene therapy and as a cell-free alternative for regenerative medicine. Furthermore, innovative biomaterials can incorporate exosomes to enhance tissue regeneration and engineering. In this work, we summarize the most recent knowledge on the role of exosomes in cardiometabolic pathophysiology while highlighting their potential therapeutic applications.

Development and Validation of a Machine Learning Model Predicting Arteriovenous Fistula Failure in a Large Network of Dialysis Clinics.

Background: Vascular access surveillance of dialysis patients is a challenging task for clinicians. We derived and validated an arteriovenous fistula failure model (AVF-FM) based on machine learning. Methods: The AVF-FM is an XG-Boost algorithm aimed at predicting AVF failure within three months among in-centre dialysis patients. The model was trained in the derivation set (70% of initial cohort) by exploiting the information routinely collected in the Nephrocare European Clinical Database (EuCliD®). Model performance was tested by concordance statistic and calibration charts in the remaining 30% of records. Features importance was computed using the SHAP method. Results: We included 13,369 patients, overall. The Area Under the ROC Curve (AUC-ROC) of AVF-FM was 0.80 (95% CI 0.79-0.81). Model calibration showed excellent representation of observed failure risk. Variables associated with the greatest impact on risk estimates were previous history of AVF complications, followed by access recirculation and other functional parameters including metrics describing temporal pattern of dialysis dose, blood flow, dynamic venous and arterial pressures. Conclusions: The AVF-FM achieved good discrimination and calibration properties by combining routinely collected clinical and sensor data that require no additional effort by healthcare staff. Therefore, it can potentially enable risk-based personalization of AVF surveillance strategies.

Smart Homes as Enablers for Depression Pre-Diagnosis Using PHQ-9 on HMI through Fuzzy Logic Decision System.

Depression is a common mental illness characterized by sadness, lack of interest, or pleasure. According to the DSM-5, there are nine symptoms, from which an individual must present 4 or 5 in the last two weeks to fulfill the diagnosis criteria of depression. Nevertheless, the common methods that health care professionals use to assess and monitor depression symptoms are face-to-face questionnaires leading to time-consuming or expensive methods. On the other hand, smart homes can monitor householders' health through smart devices such as smartphones, wearables, cameras, or voice assistants connected to the home. Although the depression disorders at smart homes are commonly oriented to the senior sector, depression affects all of us. Therefore, even though an expert needs to diagnose the depression disorder, questionnaires as the PHQ-9 help spot any depressive symptomatology as a pre-diagnosis. Thus, this paper proposes a three-step framework; the first step assesses the nine questions to the end-user through ALEXA or a gamified HMI. Then, a fuzzy logic decision system considers three actions based on the nine responses. Finally, the last step considers these three actions: continue monitoring through Alexa and the HMI, suggest specialist referral, and mandatory specialist referral.