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  • 1
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2021
    In:  ACM Transactions on Knowledge Discovery from Data Vol. 15, No. 3 ( 2021-06-30), p. 1-21
    In: ACM Transactions on Knowledge Discovery from Data, Association for Computing Machinery (ACM), Vol. 15, No. 3 ( 2021-06-30), p. 1-21
    Abstract: Energy disaggregation, a.k.a. Non-Intrusive Load Monitoring, aims to separate the energy consumption of individual appliances from the readings of a mains power meter measuring the total energy consumption of, e.g., a whole house. Energy consumption of individual appliances can be useful in many applications, e.g., providing appliance-level feedback to the end users to help them understand their energy consumption and ultimately save energy. Recently, with the availability of large-scale energy consumption datasets, various neural network models such as convolutional neural networks and recurrent neural networks have been investigated to solve the energy disaggregation problem. Neural network models can learn complex patterns from large amounts of data and have been shown to outperform the traditional machine learning methods such as variants of hidden Markov models. However, current neural network methods for energy disaggregation are either computational expensive or are not capable of handling long-term dependencies. In this article, we investigate the application of the recently developed WaveNet models for the task of energy disaggregation. Based on a real-world energy dataset collected from 20 households over 2 years, we show that WaveNet models outperforms the state-of-the-art deep learning methods proposed in the literature for energy disaggregation in terms of both error measures and computational cost. On the basis of energy disaggregation, we then investigate the performance of two deep-learning based frameworks for the task of on/off detection which aims at estimating whether an appliance is in operation or not. The first framework obtains the on/off states of an appliance by binarising the predictions of a regression model trained for energy disaggregation, while the second framework obtains the on/off states of an appliance by directly training a binary classifier with binarised energy readings of the appliance serving as the target values. Based on the same dataset, we show that for the task of on/off detection the second framework, i.e., directly training a binary classifier, achieves better performance in terms of F1 score.
    Type of Medium: Online Resource
    ISSN: 1556-4681 , 1556-472X
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2021
    detail.hit.zdb_id: 2257358-6
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  • 2
    In: JMIR Formative Research, JMIR Publications Inc., Vol. 7 ( 2023-1-23), p. e43905-
    Abstract: The lack of an international standard for assessing and communicating health app quality and the lack of consensus about what makes a high-quality health app negatively affect the uptake of such apps. At the request of the European Commission, the international Standard Development Organizations (SDOs), European Committee for Standardization, International Organization for Standardization, and International Electrotechnical Commission have joined forces to develop a technical specification (TS) for assessing the quality and reliability of health and wellness apps. Objective This study aimed to create a useful, globally applicable, trustworthy, and usable framework to assess health app quality. Methods A 2-round Delphi technique with 83 experts from 6 continents (predominantly Europe) participating in one (n=42, 51%) or both (n=41, 49%) rounds was used to achieve consensus on a framework for assessing health app quality. Aims included identifying the maximum 100 requirement questions for the uptake of apps that do or do not qualify as medical devices. The draft assessment framework was built on 26 existing frameworks, the principles of stringent legislation, and input from 20 core experts. A follow-up survey with 28 respondents informed a scoring mechanism for the questions. After subsequent alignment with related standards, the quality assessment framework was tested and fine-tuned with manufacturers of 11 COVID-19 symptom apps. National mirror committees from the 52 countries that participated in the SDO technical committees were invited to comment on 4 working drafts and subsequently vote on the TS. Results The final quality assessment framework includes 81 questions, 67 (83%) of which impact the scores of 4 overarching quality aspects. After testing with people with low health literacy, these aspects were phrased as “Healthy and safe,” “Easy to use,” “Secure data,” and “Robust build.” The scoring mechanism enables communication of the quality assessment results in a health app quality score and label, alongside a detailed report. Unstructured interviews with stakeholders revealed that evidence and third-party assessment are needed for health app uptake. The manufacturers considered the time needed to complete the assessment and gather evidence (2-4 days) acceptable. Publication of CEN-ISO/TS 82304-2:2021 Health software – Part 2: Health and wellness apps – Quality and reliability was approved in May 2021 in a nearly unanimous vote by 34 national SDOs, including 6 of the 10 most populous countries worldwide. Conclusions A useful and usable international standard for health app quality assessment was developed. Its quality, approval rate, and early use provide proof of its potential to become the trusted, commonly used global framework. The framework will help manufacturers enhance and efficiently demonstrate the quality of health apps, consumers, and health care professionals to make informed decisions on health apps. It will also help insurers to make reimbursement decisions on health apps.
    Type of Medium: Online Resource
    ISSN: 2561-326X
    Language: English
    Publisher: JMIR Publications Inc.
    Publication Date: 2023
    detail.hit.zdb_id: 2941716-8
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  • 3
    In: Pediatrics, American Academy of Pediatrics (AAP), Vol. 150, No. 1 ( 2022-07-01)
    Abstract: Outcome prediction of preterm birth is important for neonatal care, yet prediction performance using conventional statistical models remains insufficient. Machine learning has a high potential for complex outcome prediction. In this scoping review, we provide an overview of the current applications of machine learning models in the prediction of neurodevelopmental outcomes in preterm infants, assess the quality of the developed models, and provide guidance for future application of machine learning models to predict neurodevelopmental outcomes of preterm infants. METHODS A systematic search was performed using PubMed. Studies were included if they reported on neurodevelopmental outcome prediction in preterm infants using predictors from the neonatal period and applying machine learning techniques. Data extraction and quality assessment were independently performed by 2 reviewers. RESULTS Fourteen studies were included, focusing mainly on very or extreme preterm infants, predicting neurodevelopmental outcome before age 3 years, and mostly assessing outcomes using the Bayley Scales of Infant Development. Predictors were most often based on MRI. The most prevalent machine learning techniques included linear regression and neural networks. None of the studies met all newly developed quality assessment criteria. Studies least prone to inflated performance showed promising results, with areas under the curve up to 0.86 for classification and R2 values up to 91% in continuous prediction. A limitation was that only 1 data source was used for the literature search. CONCLUSIONS Studies least prone to inflated prediction results are the most promising. The provided evaluation framework may contribute to improved quality of future machine learning models.
    Type of Medium: Online Resource
    ISSN: 0031-4005 , 1098-4275
    Language: English
    Publisher: American Academy of Pediatrics (AAP)
    Publication Date: 2022
    detail.hit.zdb_id: 1477004-0
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  • 4
    In: Clinical Biochemistry, Elsevier BV, Vol. 116 ( 2023-06), p. 7-10
    Type of Medium: Online Resource
    ISSN: 0009-9120
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 1496880-0
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  • 5
    In: Haematologica, Ferrata Storti Foundation (Haematologica), Vol. 107, No. 8 ( 2022-03-31), p. 1940-1943
    Type of Medium: Online Resource
    ISSN: 1592-8721 , 0390-6078
    Language: Unknown
    Publisher: Ferrata Storti Foundation (Haematologica)
    Publication Date: 2022
    detail.hit.zdb_id: 2186022-1
    detail.hit.zdb_id: 2030158-3
    detail.hit.zdb_id: 2805244-4
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  • 6
    Online Resource
    Online Resource
    BMJ ; 2020
    In:  Evidence Based Mental Health Vol. 23, No. 1 ( 2020-02), p. 27-33
    In: Evidence Based Mental Health, BMJ, Vol. 23, No. 1 ( 2020-02), p. 27-33
    Abstract: Background Self-reported client assessments during online treatments enable the development of statistical models for the prediction of client improvement and symptom development. Evaluation of these models is mandatory to ensure their validity. Methods For this purpose, we suggest besides a model evaluation based on study data the use of a simulation analysis. The simulation analysis provides insight into the model performance and enables to analyse reasons for a low predictive accuracy. In this study, we evaluate a temporal causal model (TCM) and show that it does not provide reliable predictions of clients’ future mood levels. Results Based on the simulation analysis we investigate the potential reasons for the low predictive performance, for example, noisy measurements and sampling frequency. We conclude that the analysed TCM in its current form is not sufficient to describe the underlying psychological processes. Conclusions The results demonstrate the importance of model evaluation and the benefit of a simulation analysis. The current manuscript provides practical guidance for conducting model evaluation including simulation analysis.
    Type of Medium: Online Resource
    ISSN: 1362-0347 , 1468-960X
    Language: English
    Publisher: BMJ
    Publication Date: 2020
    detail.hit.zdb_id: 3160283-6
    detail.hit.zdb_id: 2052843-7
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  • 7
    In: Open Heart, BMJ, Vol. 8, No. 1 ( 2021-02), p. e001554-
    Abstract: Early recognition of individuals with increased risk of sudden cardiac arrest (SCA) remains challenging. SCA research so far has used data from cardiologist care, but missed most SCA victims, since they were only in general practitioner (GP) care prior to SCA. Studying individuals with type 2 diabetes (T2D) in GP care may help solve this problem, as they have increased risk for SCA, and rich clinical datasets, since they regularly visit their GP for check-up measurements. This information can be further enriched with extensive genetic and metabolic information. Aim To describe the study protocol of the REcognition of Sudden Cardiac arrest vUlnErability in Diabetes (RESCUED) project, which aims at identifying clinical, genetic and metabolic factors contributing to SCA risk in individuals with T2D, and to develop a prognostic model for the risk of SCA. Methods The RESCUED project combines data from dedicated SCA and T2D cohorts, and GP data, from the same region in the Netherlands. Clinical data, genetic data (common and rare variant analysis) and metabolic data (metabolomics) will be analysed (using classical analysis techniques and machine learning methods) and combined into a prognostic model for risk of SCA. Conclusion The RESCUED project is designed to increase our ability at early recognition of elevated SCA risk through an innovative strategy of focusing on GP data and a multidimensional methodology including clinical, genetic and metabolic analyses.
    Type of Medium: Online Resource
    ISSN: 2053-3624
    Language: English
    Publisher: BMJ
    Publication Date: 2021
    detail.hit.zdb_id: 2747269-3
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  • 8
    In: Intensive Care Medicine Experimental, Springer Science and Business Media LLC, Vol. 9, No. 1 ( 2021-12)
    Abstract: The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, and resource allocation. Previous studies have determined risk factors on admission only, and included a limited number of predictors. Therefore, using data from the highly granular and multicenter Dutch Data Warehouse, we developed machine learning models to identify risk factors for ICU mortality, ventilator-free days and ICU-free days during the course of invasive mechanical ventilation (IMV) in COVID-19 patients. Methods The DDW is a growing electronic health record database of critically ill COVID-19 patients in the Netherlands. All adult ICU patients on IMV were eligible for inclusion. Transfers, patients admitted for less than 24 h, and patients still admitted at time of data extraction were excluded. Predictors were selected based on the literature, and included medication dosage and fluid balance. Multiple algorithms were trained and validated on up to three sets of observations per patient on day 1, 7, and 14 using fivefold nested cross-validation, keeping observations from an individual patient in the same split. Results A total of 1152 patients were included in the model. XGBoost models performed best for all outcomes and were used to calculate predictor importance. Using Shapley additive explanations (SHAP), age was the most important demographic risk factor for the outcomes upon start of IMV and throughout its course. The relative probability of death across age values is visualized in Partial Dependence Plots (PDPs), with an increase starting at 54 years. Besides age, acidaemia, low P / F -ratios and high driving pressures demonstrated a higher probability of death. The PDP for driving pressure showed a relative probability increase starting at 12 cmH 2 O. Conclusion Age is the most important demographic risk factor of ICU mortality, ICU-free days and ventilator-free days throughout the course of invasive mechanical ventilation in critically ill COVID-19 patients. pH, P / F ratio, and driving pressure should be monitored closely over the course of mechanical ventilation as risk factors predictive of these outcomes.
    Type of Medium: Online Resource
    ISSN: 2197-425X
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2740385-3
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  • 9
    In: Journal of Diabetes Science and Technology, SAGE Publications
    Abstract: The Glycemia Risk Index (GRI) was introduced as a single value derived from the ambulatory glucose profile that identifies patients who need attention. This study describes participants in each of the five GRI zones and examines the percentage of variation in GRI scores that is explained by sociodemographic and clinical variables among diverse adults with type 1 diabetes. Methods: A total of 159 participants provided blinded continuous glucose monitoring (CGM) data over 14 days (mean age [SD] = 41.4 [14.5] years; female = 54.1%, Hispanic = 41.5%). Glycemia Risk Index zones were compared on CGM, sociodemographic, and clinical variables. Shapley value analysis examined the percentage of variation in GRI scores explained by different variables. Receiver operating characteristic curves examined GRI cutoffs for those more likely to have experienced ketoacidosis or severe hypoglycemia. Results: Mean glucose and variability, time in range, and percentage of time in high, and very high, glucose ranges differed across the five GRI zones ( P values 〈 .001). Multiple sociodemographic indices also differed across zones, including education level, race/ethnicity, age, and insurance status. Sociodemographic and clinical variables collectively explained 62.2% of variance in GRI scores. A GRI score ≥84.5 reflected greater likelihood of ketoacidosis (area under the curve [AUC] = 0.848), and scores ≥58.2 reflected greater likelihood of severe hypoglycemia (AUC = 0.729) over the previous six months. Conclusions: Results support the use of the GRI, with GRI zones identifying those in need of clinical attention. Findings highlight the need to address health inequities. Treatment differences associated with the GRI also suggest behavioral and clinical interventions including starting individuals on CGM or automated insulin delivery systems.
    Type of Medium: Online Resource
    ISSN: 1932-2968 , 1932-2968
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2023
    detail.hit.zdb_id: 2467312-2
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  • 10
    In: Intensive and Critical Care Nursing, Elsevier BV, Vol. 61 ( 2020-12), p. 102925-
    Type of Medium: Online Resource
    ISSN: 0964-3397
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2020
    detail.hit.zdb_id: 2049072-0
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