| 1. |
- Cao, Yang, Associate Professor, 1972-, et al.
(författare)
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Deep Learning Neural Networks to Predict Serious Complications After Bariatric Surgery : Analysis of Scandinavian Obesity Surgery Registry Data
- 2020
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Ingår i: JMIR Medical Informatics. - : JMIR Publications. - 2291-9694. ; 8:5
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Obesity is one of today's most visible public health problems worldwide. Although modern bariatric surgery is ostensibly considered safe, serious complications and mortality still occur in some patients.OBJECTIVE: This study aimed to explore whether serious postoperative complications of bariatric surgery recorded in a national quality registry can be predicted preoperatively using deep learning methods.METHODS: Patients who were registered in the Scandinavian Obesity Surgery Registry (SOReg) between 2010 and 2015 were included in this study. The patients who underwent a bariatric procedure between 2010 and 2014 were used as training data, and those who underwent a bariatric procedure in 2015 were used as test data. Postoperative complications were graded according to the Clavien-Dindo classification, and complications requiring intervention under general anesthesia or resulting in organ failure or death were considered serious. Three supervised deep learning neural networks were applied and compared in our study: multilayer perceptron (MLP), convolutional neural network (CNN), and recurrent neural network (RNN). The synthetic minority oversampling technique (SMOTE) was used to artificially augment the patients with serious complications. The performances of the neural networks were evaluated using accuracy, sensitivity, specificity, Matthews correlation coefficient, and area under the receiver operating characteristic curve.RESULTS: In total, 37,811 and 6250 patients were used as the training data and test data, with incidence rates of serious complication of 3.2% (1220/37,811) and 3.0% (188/6250), respectively. When trained using the SMOTE data, the MLP appeared to have a desirable performance, with an area under curve (AUC) of 0.84 (95% CI 0.83-0.85). However, its performance was low for the test data, with an AUC of 0.54 (95% CI 0.53-0.55). The performance of CNN was similar to that of MLP. It generated AUCs of 0.79 (95% CI 0.78-0.80) and 0.57 (95% CI 0.59-0.61) for the SMOTE data and test data, respectively. Compared with the MLP and CNN, the RNN showed worse performance, with AUCs of 0.65 (95% CI 0.64-0.66) and 0.55 (95% CI 0.53-0.57) for the SMOTE data and test data, respectively.CONCLUSIONS: MLP and CNN showed improved, but limited, ability for predicting the postoperative serious complications after bariatric surgery in the Scandinavian Obesity Surgery Registry data. However, the overfitting issue is still apparent and needs to be overcome by incorporating intra- and perioperative information.
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| 2. |
- Cao, Yang, Associate Professor, 1972-, et al.
(författare)
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Using a Convolutional Neural Network to Predict Remission of Diabetes After Gastric Bypass Surgery : Machine Learning Study From the Scandinavian Obesity Surgery Register
- 2021
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Ingår i: JMIR Medical Informatics. - : JMIR Publications. - 2291-9694. ; 9:8
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Prediction of diabetes remission is an important topic in the evaluation of patients with type 2 diabetes (T2D) before bariatric surgery. Several high-quality predictive indices are available, but artificial intelligence algorithms offer the potential for higher predictive capability.OBJECTIVE: This study aimed to construct and validate an artificial intelligence prediction model for diabetes remission after Roux-en-Y gastric bypass surgery.METHODS: Patients who underwent surgery from 2007 to 2017 were included in the study, with collection of individual data from the Scandinavian Obesity Surgery Registry (SOReg), the Swedish National Patients Register, the Swedish Prescribed Drugs Register, and Statistics Sweden. A 7-layer convolution neural network (CNN) model was developed using 80% (6446/8057) of patients randomly selected from SOReg and 20% (1611/8057) of patients for external testing. The predictive capability of the CNN model and currently used scores (DiaRem, Ad-DiaRem, DiaBetter, and individualized metabolic surgery) were compared.RESULTS: In total, 8057 patients with T2D were included in the study. At 2 years after surgery, 77.09% achieved pharmacological remission (n=6211), while 63.07% (4004/6348) achieved complete remission. The CNN model showed high accuracy for cessation of antidiabetic drugs and complete remission of T2D after gastric bypass surgery. The area under the receiver operating characteristic curve (AUC) for the CNN model for pharmacological remission was 0.85 (95% CI 0.83-0.86) during validation and 0.83 for the final test, which was 9%-12% better than the traditional predictive indices. The AUC for complete remission was 0.83 (95% CI 0.81-0.85) during validation and 0.82 for the final test, which was 9%-11% better than the traditional predictive indices.CONCLUSIONS: The CNN method had better predictive capability compared to traditional indices for diabetes remission. However, further validation is needed in other countries to evaluate its external generalizability.
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| 3. |
- Dallora Moraes, Ana Luiza, et al.
(författare)
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Age assessment of youth and young adults using magnetic resonance imaging of the knee : A deep learning approach
- 2019
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Ingår i: JMIR Medical Informatics. - : JMIR PUBLICATIONS. - 2291-9694. ; 7:4, s. 419-436
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Tidskriftsartikel (refereegranskat)abstract
- Background: Bone age assessment (BAA) is an important tool for diagnosis and in determining the time of treatment in a number of pediatric clinical scenarios, as well as in legal settings where it is used to estimate the chronological age of an individual where valid documents are lacking. Traditional methods for BAA suffer from drawbacks, such as exposing juveniles to radiation, intra- and interrater variability, and the time spent on the assessment. The employment of automated methods such as deep learning and the use of magnetic resonance imaging (MRI) can address these drawbacks and improve the assessment of age. Objective: The aim of this paper is to propose an automated approach for age assessment of youth and young adults in the age range when the length growth ceases and growth zones are closed (14-21 years of age) by employing deep learning using MRI of the knee. Methods: This study carried out MRI examinations of the knee of 402 volunteer subjects-221 males (55.0%) and 181 (45.0%) females-aged 14-21 years. The method comprised two convolutional neural network (CNN) models: the first one selected the most informative images of an MRI sequence, concerning age-assessment purposes; these were then used in the second module, which was responsible for the age estimation. Different CNN architectures were tested, both training from scratch and employing transfer learning. Results: The CNN architecture that provided the best results was GoogLeNet pretrained on the ImageNet database. The proposed method was able to assess the age of male subjects in the range of 14-20.5 years, with a mean absolute error (MAE) of 0.793 years, and of female subjects in the range of 14-19.5 years, with an MAE of 0.988 years. Regarding the classification of minors-with the threshold of 18 years of age-an accuracy of 98.1% for male subjects and 95.0% for female subjects was achieved. Conclusions: The proposed method was able to assess the age of youth and young adults from 14 to 20.5 years of age for male subjects and 14 to 19.5 years of age for female subjects in a fully automated manner, without the use of ionizing radiation, addressing the drawbacks of traditional methods. © 2019 Journal of Medical Internet Research. All rights reserved.
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| 4. |
- Eivazzadeh, Shahryar, et al.
(författare)
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Evaluating Health Information Systems Using Ontologies
- 2016
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Ingår i: JMIR Medical Informatics. - : JMIR Publications. - 2291-9694. ; 4:2
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Tidskriftsartikel (refereegranskat)abstract
- Background: There are several frameworks that attempt to address the challenges of evaluation of health information systems by offering models, methods, and guidelines about what to evaluate, how to evaluate, and how to report the evaluation results. Model-based evaluation frameworks usually suggest universally applicable evaluation aspects but do not consider case-specific aspects. On the other hand, evaluation frameworks that are case specific, by eliciting user requirements, limit their output to the evaluation aspects suggested by the users in the early phases of system development. In addition, these case-specific approaches extract different sets of evaluation aspects from each case, making it challenging to collectively compare, unify, or aggregate the evaluation of a set of heterogeneous health information systems.Objectives: The aim of this paper is to find a method capable of suggesting evaluation aspects for a set of one or more health information systems—whether similar or heterogeneous—by organizing, unifying, and aggregating the quality attributes extracted from those systems and from an external evaluation framework.Methods: On the basis of the available literature in semantic networks and ontologies, a method (called Unified eValuation using Ontology; UVON) was developed that can organize, unify, and aggregate the quality attributes of several health information systems into a tree-style ontology structure. The method was extended to integrate its generated ontology with the evaluation aspects suggested by model-based evaluation frameworks. An approach was developed to extract evaluation aspects from the ontology that also considers evaluation case practicalities such as the maximum number of evaluation aspects to be measured or their required degree of specificity. The method was applied and tested in Future Internet Social and Technological Alignment Research (FI-STAR), a project of 7 cloud-based eHealth applications that were developed and deployed across European Union countries.Results: The relevance of the evaluation aspects created by the UVON method for the FI-STAR project was validated by the corresponding stakeholders of each case. These evaluation aspects were extracted from a UVON-generated ontology structure that reflects both the internally declared required quality attributes in the 7 eHealth applications of the FI-STAR project and the evaluation aspects recommended by the Model for ASsessment of Telemedicine applications (MAST) evaluation framework. The extracted evaluation aspects were used to create questionnaires (for the corresponding patients and health professionals) to evaluate each individual case and the whole of the FI-STAR project.Conclusions: The UVON method can provide a relevant set of evaluation aspects for a heterogeneous set of health information systems by organizing, unifying, and aggregating the quality attributes through ontological structures. Those quality attributes can be either suggested by evaluation models or elicited from the stakeholders of those systems in the form of system requirements. The method continues to be systematic, context sensitive, and relevant across a heterogeneous set of health information systems.
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| 5. |
- Eivazzadeh, Shahryar, 1975-, et al.
(författare)
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Most Influential Qualities in Creating Satisfaction Among the Users of Health Information Systems : Study in Seven European Union Countries
- 2018
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Ingår i: JMIR Medical Informatics. - : JMIR Publications. - 2291-9694. ; 6:4
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Tidskriftsartikel (refereegranskat)abstract
- Background:Several models suggest how the qualities of a product or service influence user satisfaction. Models, such as the Customer Satisfaction Index (CSI), Technology Acceptance Model (TAM), and Delone and McLean Information Systems Success (D&M IS), demonstrate those relations and have been used in the context of health information systems.Objective:We want to investigate which qualities foster greater satisfaction among patient and professional users. In addition, we are interested in knowing to what extent improvement in those qualities can explain user satisfaction and if this makes user satisfaction a proxy indicator of those qualities.Methods:The Unified eValuation using ONtology (UVON) method was utilised to construct an ontology of the required qualities for seven e-health applications being developed in the FI-STAR project, a European Union (EU) project in e-health. The e-health applications were deployed across seven EU countries. The ontology included and unified the required qualities of those systems together with the aspects suggested by the Model for ASsessment of Telemedicine applications (MAST) evaluation framework. Two similar questionnaires, for 87 patient users and 31 health professional users, were elicited from the ontology. In the questionnaires, user was asked if the system has improved the specified qualities and if the user was satisfied with the system. The results were analysed using Kendall correlation coefficients matrices, incorporating the quality and satisfaction aspects. For the next step, two Partial Least Squares Structural Equation Modelling (PLS-SEM) path models were developed using the quality and satisfaction measure variables and the latent construct variables that were suggested by the UVON method.Results:Most of the quality aspects grouped by the UVON method are highly correlated. Strong correlations in each group suggest that the grouped qualities can be measures which reflect a latent quality construct. The PLS-SEM path analysis for the patients reveals that the effectiveness, safety, and efficiency of treatment provided by the system are the most influential qualities in achieving and predicting user satisfaction. For the professional users, effectiveness and affordability are the most influential. The parameters of the PLS-SEM that are calculated allow for the measurement of a user satisfaction index similar to CSI for similar health information systems.Conclusions:For both patients and professionals, the effectiveness of systems highly contributes to their satisfaction. Patients care about improvements in safety and efficiency, while professionals care about improvements in the affordability of treatments with health information systems. User satisfaction is reflected more in the users' evaluation of system output and fulfilment of expectations, but slightly less in how far the system is from ideal. Investigating satisfaction scores can be a simple, fast way to infer if the system has improved the abovementioned qualities in treatment and care.
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| 6. |
- Entezarjou, Artin, et al.
(författare)
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Antibiotic Prescription Rates After eVisits Versus Office Visits in Primary Care : Observational Study
- 2021
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Ingår i: JMIR Medical Informatics. - : JMIR Publications Inc.. - 2291-9694. ; 9:3
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Direct-to-consumer telemedicine is an increasingly used modality to access primary care. Previous research on assessment using synchronous virtual visits showed mixed results regarding antibiotic prescription rates, and research on assessment using asynchronous chat-based eVisits is lacking.OBJECTIVE: The goal of the research was to investigate if eVisit management of sore throat, other respiratory symptoms, or dysuria leads to higher rates of antibiotic prescription compared with usual management using physical office visits.METHODS: Data from 3847 eVisits and 759 office visits for sore throat, dysuria, or respiratory symptoms were acquired from a large private health care provider in Sweden. Data were analyzed to compare antibiotic prescription rates within 3 days, antibiotic type, and diagnoses made. For a subset of sore throat visits (n=160 eVisits, n=125 office visits), Centor criteria data were manually extracted and validated.RESULTS: Antibiotic prescription rates were lower following eVisits compared with office visits for sore throat (169/798, 21.2%, vs 124/312, 39.7%; P<.001) and respiratory symptoms (27/1724, 1.6%, vs 50/251, 19.9%; P<.001), while no significant differences were noted comparing eVisits to office visits for dysuria (1016/1325, 76.7%, vs 143/196, 73.0%; P=.25). Guideline-recommended antibiotics were prescribed similarly following sore throat eVisits and office visits (163/169, 96.4%, vs 117/124, 94.4%; P=.39). eVisits for respiratory symptoms and dysuria were more often prescribed guideline-recommended antibiotics (26/27, 96.3%, vs 37/50, 74.0%; P=.02 and 1009/1016, 99.3%, vs 135/143, 94.4%; P<.001, respectively). Odds ratios of antibiotic prescription following office visits compared with eVisits after adjusting for age and differences in set diagnoses were 2.94 (95% CI 1.99-4.33), 11.57 (95% CI 5.50-24.32), 1.01 (95% CI 0.66-1.53), for sore throat, respiratory symptoms, and dysuria, respectively.CONCLUSIONS: The use of asynchronous eVisits for the management of sore throat, dysuria, and respiratory symptoms is not associated with an inherent overprescription of antibiotics compared with office visits.TRIAL REGISTRATION: ClinicalTrials.gov NCT03474887; https://clinicaltrials.gov/ct2/show/NCT03474887.
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| 7. |
- Entezarjou, Artin, et al.
(författare)
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Human- Versus Machine Learning-Based Triage Using Digitalized Patient Histories in Primary Care : Comparative Study
- 2020
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Ingår i: JMIR Medical Informatics. - : JMIR Publications Inc.. - 2291-9694. ; 8:9
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Tidskriftsartikel (refereegranskat)abstract
- Background: Smartphones have made it possible for patients to digitally report symptoms before physical primary care visits. Using machine learning (ML), these data offer an opportunity to support decisions about the appropriate level of care (triage). Objective: The purpose of this study was to explore the interrater reliability between human physicians and an automated ML-based triage method. Methods: After testing several models, a naive Bayes triage model was created using data from digital medical histories, capable of classifying digital medical history reports as either in need of urgent physical examination or not in need of urgent physical examination The model was tested on 300 digital medical history reports and classification was compared with the majority vote of an expert panel of 5 primary care physicians (PCPs). Reliability between raters was measured using both Cohen kappa (adjusted for chance agreement) and percentage agreement (not adjusted for chance agreement). Results: Interrater reliability as measured by Cohen kappa was 0.17 when comparing the majority vote of the reference group with the model. Agreement was 74% (138/186) for cases judged not in need of urgent physical examination and 42% (38/90) for cases judged to be in need of urgent physical examination No specific features linked to the model's triage decision could be identified. Between physicians within the panel, Cohen kappa was 0.2. Intrarater reliability when 1 physician retriaged 50 reports resulted in Cohen kappa of 0.55. Conclusions: Low interrater and intrarater agreement in triage decisions among PCPs limits the possibility to use human decisions as a reference for ML to automate triage in primary care.
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| 8. |
- Garell, Cecilia, 1970-, et al.
(författare)
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A Legal Framework to Support Development and Assessment of Digital Health Services
- 2016
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Ingår i: JMIR Medical Informatics. - Toronto : JMIR Publications, Inc.. - 2291-9694. ; 4:2, s. e17-e17
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Digital health services empower people to track, manage, and improve their own health and quality of life while delivering a more personalized and precise health care, at a lower cost and with higher efficiency and availability. Essential for the use of digital health services is that the treatment of any personal data is compatible with the Patient Data Act, Personal Data Act, and other applicable privacy laws.OBJECTIVE: The aim of this study was to develop a framework for legal challenges to support designers in development and assessment of digital health services.METHODS: A purposive sampling, together with snowball recruitment, was used to identify stakeholders and information sources for organizing, extending, and prioritizing the different concepts, actors, and regulations in relation to digital health and health-promoting digital systems. The data were collected through structured interviewing and iteration, and 3 different cases were used for face validation of the framework.RESULTS: A framework for assessing the legal challenges in developing digital health services (Legal Challenges in Digital Health [LCDH] Framework) was created and consists of 6 key questions to be used to evaluate a digital health service according to current legislation.CONCLUSIONS: Structured discussion about legal challenges in relation to health-promoting digital services can be enabled by a constructive framework to investigate, assess, and verify the digital service according to current legislation. The LCDH Framework developed in this study proposes such a framework and can be used in prospective evaluation of the relationship of a potential health-promoting digital service with the existing laws and regulations.
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| 9. |
- Jonnergård, Karin, et al.
(författare)
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Communicating the Implementation of Open Notes to Health Care Professionals : Mixed Methods Study
- 2021
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Ingår i: JMIR Medical Informatics. - Toronto : JMIR Publications Inc.. - 2291-9694. ; 9:8
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Tidskriftsartikel (refereegranskat)abstract
- Background: The literature on how to communicate reform in organizations has mainly focused on levels of hierarchy and has largely ignored the variety of professions that may be found within an organization. In this study, we focus on the relationship between media type and professional responses.Objective: The objective of this study was to investigate whether and how belonging to a profession influences the choice of communication media and the perception of information when a technical innovation is implemented in a health care setting.Methods: This study followed a mixed methods design based on observations and participant studies, as well as a survey of professionals in psychiatric health care in Sweden. The χ2 test was used to detect differences in perceptions between professional groups.Results: The use of available communication media differed among professions. These differences seem to be related to the status attached to each profession. The sense-making of the information appears to be similar among the professions, but is based on their traditional professional norms rather than on reflection on the reform at hand.Conclusions: When communicating about the implementation of a new technology, the choice of media and the message need to be attuned to the employees in both hierarchical and professional terms. This also applies to situations where professional employees are only indirectly affected by the implementation. A differentiated communication strategy is preferred over a downward cascade of information. © Karin Jonnergård, Lena Petersson, Gudbjörg Erlingsdóttir.
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| 10. |
- Moraes, Ana Luiza Dallora, et al.
(författare)
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Chronological Age Assessment in Young Individuals Using Bone Age Assessment Staging and Nonradiological Aspects : Machine Learning Multifactorial Approach
- 2020
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Ingår i: JMIR Medical Informatics. - : JMIR Publications. - 2291-9694. ; 8:9
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Tidskriftsartikel (refereegranskat)abstract
- Background: Bone age assessment (BAA) is used in numerous pediatric clinical settings, as well as in legal settings when entities need an estimate of chronological age (CA) when valid documents are lacking. The latter case presents itself as critical since the law is harsher for adults and granted rights along with imputability changes drastically if the individual is a minor. Traditional BAA methods suffer from drawbacks such as exposure of minors to radiation, do not consider factors that might affect the bone age and they mostly focus on a single region. Given the critical scenarios in which BAA can affect the lives of young individuals it is important to focus on the drawbacks of the traditional methods and investigate the potential of estimating CA through BAA.Objective: This paper aims to investigate CA estimation through BAA in young individuals of 14 to 21 years with machine learning methods, addressing the drawbacks in the research using magnetic resonance imaging (MRI), assessment of multiple ROIs and other factors that may affect the bone age.Methods: MRI examinations of the radius, distal tibia, proximal tibia, distal femur and calcaneus were carried out on 465 males and 473 females subjects (14-21 years). Measures of weight and height were taken from the subjects and a questionnaire was given for additional information (self-assessed Tanner Scale, physical activity level, parents' origin, type of residence during upbringing). Two pediatric radiologists assessed, independently, the MRI images as to their stage of bone development (blinded to age, gender and each other). All the gathered information was used in training machine learning models for chronological age estimation and minor versus adults classification (threshold of 18 years). Different machine learning methods were investigated.Results: The minor versus adults classification produced accuracies of 90% and 84%, for male and female subjects, respectively, with high recalls for the classification of minors. The chronological age estimation for the eight age groups (14-21 years) achieved mean absolute errors of 0.95 years and 1.24 years for male and female subjects, respectively. However, for the latter lower error occurred only for the ages of 14 and 15.Conclusions: This paper proposed to investigate the CA estimation through BAA using machine learning methods in two ways: minor versus adults classification and CA estimation in eight age groups (14-21 years), while addressing the drawbacks in the research on BAA. The first achieved good results, however, for the second case BAA showed not precise enough for the classification.
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| 11. |
- Reps, J. M., et al.
(författare)
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Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study
- 2021
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Ingår i: JMIR Medical Informatics. - : JMIR Publications Inc.. - 2291-9694. ; 9:4
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Tidskriftsartikel (refereegranskat)abstract
- Background: SARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who require hospitalization from those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision-making during the pandemic. However, the model is at high risk of bias according to the "prediction model risk of bias assessment" criteria, and it has not been externally validated. Objective: The aim of this study was to externally validate the C-19 index across a range of health care settings to determine how well it broadly predicts hospitalization due to pneumonia in COVID-19 cases. Methods: We followed the Observational Health Data Sciences and Informatics (OHDSI) framework for external validation to assess the reliability of the C-19 index. We evaluated the model on two different target populations, 41,381 patients who presented with SARS-CoV-2 at an outpatient or emergency department visit and 9,429,285 patients who presented with influenza or related symptoms during an outpatient or emergency department visit, to predict their risk of hospitalization with pneumonia during the following 0-30 days. In total, we validated the model across a network of 14 databases spanning the United States, Europe, Australia, and Asia. Results: The internal validation performance of the C-19 index had a C statistic of 0.73, and the calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data, the model obtained C statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US, and South Korean data sets, respectively. The calibration was poor, with the model underestimating risk. When validated on 12 data sets containing influenza patients across the OHDSI network, the C statistics ranged between 0.40 and 0.68. Conclusions: Our results show that the discriminative performance of the C-19 index model is low for influenza cohorts and even worse among patients with COVID-19 in the United States, Spain, and South Korea. These results suggest that C-19 should not be used to aid decision-making during the COVID-19 pandemic. Our findings highlight the importance of performing external validation across a range of settings, especially when a prediction model is being extrapolated to a different population. In the field of prediction, extensive validation is required to create appropriate trust in a model.
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| 12. |
- Rossander, Anna, 1980, et al.
(författare)
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Structure of Health Information With Different Information Models: Evaluation Study With Competency Questions.
- 2023
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Ingår i: JMIR medical informatics. - 2291-9694. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- There is a flora of health care information models but no consensus on which to use. This leads to poor information sharing and duplicate modelling work. The amount and type of differences between models has, to our knowledge, not been evaluated.This work aims to explore how information structured with various information models differ in practice. Our hypothesis is that differences between information models are overestimated. This work will also assess the usability of competency questions as a method for evaluation of information models within health care.In this study, 4 information standards, 2 standards for secondary use, and 2 electronic health record systems were included as material. Competency questions were developed for a random selection of recommendations from a clinical guideline. The information needed to answer the competency questions was modelled according to each included information model, and the results were analyzed. Differences in structure and terminology were quantified for each combination of standards.In this study, 36 competency questions were developed and answered. In general, similarities between the included information models were larger than the differences. The demarcation between information model and terminology was overall similar; on average, 45% of the included structures were identical between models. Choices of terminology differed within and between models; on average, 11% was usable in interaction with each other. The information models included in this study were able to represent most information required for answering the competency questions.Different but same same; in practice, different information models structure much information in a similar fashion. To increase interoperability within and between systems, it is more important to move toward structuring information with any information model rather than finding or developing a perfect information model. Competency questions are a feasible way of evaluating how information models perform in practice.
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| 13. |
- Staffini, Alessio, et al.
(författare)
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An agent-based model of the local spread of SARS-CoV-2 : Modeling study
- 2021
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Ingår i: JMIR Medical Informatics. - : JMIR Publications Inc.. - 2291-9694. ; 9:4
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Tidskriftsartikel (refereegranskat)abstract
- Background: The spread of SARS-CoV-2, originating in Wuhan, China, was classified as a pandemic by the World Health Organization on March 11, 2020. The governments of affected countries have implemented various measures to limit the spread of the virus. The starting point of this paper is the different government approaches, in terms of promulgating new legislative regulations to limit the virus diffusion and to contain negative effects on the populations. Objective: This paper aims to study how the spread of SARS-CoV-2 is linked to government policies and to analyze how different policies have produced different results on public health. Methods: Considering the official data provided by 4 countries (Italy, Germany, Sweden, and Brazil) and from the measures implemented by each government, we built an agent-based model to study the effects that these measures will have over time on different variables such as the total number of COVID-19 cases, intensive care unit (ICU) bed occupancy rates, and recovery and case-fatality rates. The model we implemented provides the possibility of modifying some starting variables, and it was thus possible to study the effects that some policies (eg, keeping the national borders closed or increasing the ICU beds) would have had on the spread of the infection. Results: The 4 considered countries have adopted different containment measures for COVID-19, and the forecasts provided by the model for the considered variables have given different results. Italy and Germany seem to be able to limit the spread of the infection and any eventual second wave, while Sweden and Brazil do not seem to have the situation under control. This situation is also reflected in the forecasts of pressure on the National Health Services, which see Sweden and Brazil with a high occupancy rate of ICU beds in the coming months, with a consequent high number of deaths. Conclusions: In line with what we expected, the obtained results showed that the countries that have taken restrictive measures in terms of limiting the population mobility have managed more successfully than others to contain the spread of COVID-19. Moreover, the model demonstrated that herd immunity cannot be reached even in countries that have relied on a strategy without strict containment measures.
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| 14. |
- Viberg Johansson, Jennifer, et al.
(författare)
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Preferences of the Public for Sharing Health Data : Discrete Choice Experiment
- 2021
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Ingår i: JMIR Medical Informatics. - : JMIR Publications. - 2291-9694. ; 9:7
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Tidskriftsartikel (refereegranskat)abstract
- Background: Digital technological development in the last 20 years has led to significant growth in digital collection, use, and sharing of health data. To maintain public trust in the digital society and to enable acceptable policy-making in the future, it is important to investigate people’s preferences for sharing digital health data.Objective: The aim of this study is to elicit the preferences of the public in different Northern European countries (the United Kingdom, Norway, Iceland, and Sweden) for sharing health information in different contexts.Methods: Respondents in this discrete choice experiment completed several choice tasks, in which they were asked if data sharing in the described hypothetical situation was acceptable to them. Latent class logistic regression models were used to determine attribute-level estimates and heterogeneity in preferences. We calculated the relative importance of the attributes and the predicted acceptability for different contexts in which the data were shared from the estimates.Results: In the final analysis, we used 37.83% (1967/5199) questionnaires. All attributes influenced the respondents’ willingness to share health information (P<.001). The most important attribute was whether the respondents were informed about their data being shared. The possibility of opting out from sharing data was preferred over the opportunity to consent (opt-in). Four classes were identified in the latent class model, and the average probabilities of belonging were 27% for class 1, 32% for class 2, 23% for class 3, and 18% for class 4. The uptake probability varied between 14% and 85%, depending on the least to most preferred combination of levels.Conclusions: Respondents from different countries have different preferences for sharing their health data regarding the value of a review process and the reason for their new use. Offering respondents information about the use of their data and the possibility to opt out is the most preferred governance mechanism.
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| 15. |
- Guillet, B., et al.
(författare)
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Properties of ultra-thin NbN films for membrane-type THz HEB
- 2007
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Ingår i: Journal of Low Temperature Physics. - : Springer Science and Business Media LLC. - 0022-2291 .- 1573-7357. ; 151:1-2, s. 570-574
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Konferensbidrag (refereegranskat)abstract
- Various buffer layers have been investigated in order to improve the crystalline quality of NbN ultra-thin films. The structural properties, the thickness, the surface morphology of 5-10 nm NbN films have been studied by different techniques. Uncertainty on thickness measurements in this range and the relation between NbN film quality and gain bandwidth are discussed in the framework of their use in Hot Electron Bolometers (HEB).
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