Hasil untuk "Medical legislation"

Prabhat Kc, Rongping Zeng, Nirmal Soni et al.

Deep learning (DL) methods are currently being explored to restore images from sparse-view-, limited-data-, and undersampled-based acquisitions in medical applications. Although outputs from DL may appear visually appealing based on likability/subjective criteria (such as less noise, smooth features), they may also suffer from hallucinations. This issue is further exacerbated by a lack of easy-to-use techniques and robust metrics for the identification of hallucinations in DL outputs. In this work, we propose performing Fourier Ring Correlation (FRC) analysis over small patches and concomitantly (s)canning across DL outputs and their reference counterparts to detect hallucinations (termed as sFRC). We describe the rationale behind sFRC and provide its mathematical formulation. The parameters essential to sFRC may be set using predefined hallucinated features annotated by subject matter experts or using imaging theory-based hallucination maps. We use sFRC to detect hallucinations for three undersampled medical imaging problems: CT super-resolution, CT sparse view, and MRI subsampled restoration. In the testing phase, we demonstrate sFRC's effectiveness in detecting hallucinated features for the CT problem and sFRC's agreement with imaging theory-based outputs on hallucinated feature maps for the MR problem. Finally, we quantify the hallucination rates of DL methods on in-distribution versus out-of-distribution data and under increasing subsampling rates to characterize the robustness of DL methods. Beyond DL-based methods, sFRC's effectiveness in detecting hallucinations for a conventional regularization-based restoration method and a state-of-the-art unrolled method is also shown.

Juan Sosa, Brayan Riveros, Emma J. Camargo-Díaz

The legislative output of Colombia's House of Representatives between 2014 and 2025 is analyzed using 4,083 bills. Bipartite networks are constructed between parties and bills, and between representatives and bills, along with their projections, to characterize co-sponsorship patterns, centrality, and influence, and to assess whether political polarization is reflected in legislative collaboration. In parallel, the content of the initiatives is studied through semantic networks based on co-occurrences extracted from short descriptions, and topics by party and period are identified using a stochastic block model for weighted networks, with additional comparison using Latent Dirichlet Allocation. In addition, a Bayesian sociability model is applied to detect terms with robust connectivity and to summarize discursive cores. Overall, the approach integrates relational and semantic structure to describe thematic shifts across administrations, identify influential actors and collectives, and provide a reproducible synthesis that promotes transparency and citizen oversight of the legislative process.

Runshi Zhang, Hao Mo, Junchen Wang et al.

Complicated image registration is a key issue in medical image analysis, and deep learning-based methods have achieved better results than traditional methods. The methods include ConvNet-based and Transformer-based methods. Although ConvNets can effectively utilize local information to reduce redundancy via small neighborhood convolution, the limited receptive field results in the inability to capture global dependencies. Transformers can establish long-distance dependencies via a self-attention mechanism; however, the intense calculation of the relationships among all tokens leads to high redundancy. We propose a novel unsupervised image registration method named the unified Transformer and superresolution (UTSRMorph) network, which can enhance feature representation learning in the encoder and generate detailed displacement fields in the decoder to overcome these problems. We first propose a fusion attention block to integrate the advantages of ConvNets and Transformers, which inserts a ConvNet-based channel attention module into a multihead self-attention module. The overlapping attention block, a novel cross-attention method, uses overlapping windows to obtain abundant correlations with match information of a pair of images. Then, the blocks are flexibly stacked into a new powerful encoder. The decoder generation process of a high-resolution deformation displacement field from low-resolution features is considered as a superresolution process. Specifically, the superresolution module was employed to replace interpolation upsampling, which can overcome feature degradation. UTSRMorph was compared to state-of-the-art registration methods in the 3D brain MR (OASIS, IXI) and MR-CT datasets. The qualitative and quantitative results indicate that UTSRMorph achieves relatively better performance. The code and datasets are publicly available at https://github.com/Runshi-Zhang/UTSRMorph.

Harry Anthony, Konstantinos Kamnitsas

Reliable use of deep neural networks (DNNs) for medical image analysis requires methods to identify inputs that differ significantly from the training data, called out-of-distribution (OOD), to prevent erroneous predictions. OOD detection methods can be categorised as either confidence-based (using the model's output layer for OOD detection) or feature-based (not using the output layer). We created two new OOD benchmarks by dividing the D7P (dermatology) and BreastMNIST (ultrasound) datasets into subsets which either contain or don't contain an artefact (rulers or annotations respectively). Models were trained with artefact-free images, and images with the artefacts were used as OOD test sets. For each OOD image, we created a counterfactual by manually removing the artefact via image processing, to assess the artefact's impact on the model's predictions. We show that OOD artefacts can boost a model's softmax confidence in its predictions, due to correlations in training data among other factors. This contradicts the common assumption that OOD artefacts should lead to more uncertain outputs, an assumption on which most confidence-based methods rely. We use this to explain why feature-based methods (e.g. Mahalanobis score) typically have greater OOD detection performance than confidence-based methods (e.g. MCP). However, we also show that feature-based methods typically perform worse at distinguishing between inputs that lead to correct and incorrect predictions (for both OOD and ID data). Following from these insights, we argue that a combination of feature-based and confidence-based methods should be used within DNN pipelines to mitigate their respective weaknesses. These project's code and OOD benchmarks are available at: https://github.com/HarryAnthony/Evaluating_OOD_detection.

Antonino Capillo, Enrico De Santis, Fabio Massimo Frattale Mascioli et al.

Efforts in the fight against Climate Change are increasingly oriented towards new energy efficiency strategies in Smart Grids (SGs). In 2018, with proper legislation, the European Union (EU) defined the Renewable Energy Community (REC) as a local electrical grid whose participants share their self-produced renewable energy, aiming at reducing bill costs by taking advantage of proper incentives. That action aspires to accelerate the spread of local renewable energy exploitation, whose costs could not be within everyone's reach. Since a REC is technically an SG, the strategies above can be applied, and specifically, practical Energy Management Systems (EMSs) are required. Therefore, in this work, an online Hierarchical EMS (HEMS) is synthesized for REC cost minimization to evaluate its superiority over a local self-consumption approach. EU technical indications (as inherited from Italy) are diligently followed, aiming for results that are as realistic as possible. Power flows between REC nodes, or Microgrids (MGs) are optimized by taking Energy Storage Systems (ESSs) and PV plant costs, energy purchase costs, and REC incentives. A hybrid Fuzzy Inference System - Genetic Algorithm (FIS-GA) model is implemented with the GA encoding the FIS parameters. Power generation and consumption, which are the overall system input, are predicted by a LSTM trained on historical data. The proposed hierarchical model achieves good precision in short computation times and outperforms the self-consumption approach, leading to about 20% savings compared to the latter. In addition, the Explainable AI (XAI), which characterizes the model through the FIS, makes results more reliable thanks to an excellent human interpretation level. To finish, the HEMS is parametrized so that it is straightforward to switch to another Country's technical legislation framework.

M.M. Blikhar, I.О. Lychenko, Y.S. Oliinyk et al.

Background. The article analyses the interaction between the state’s mandatory regulation of the human right to reproductive health and its coordination with the patient’s autonomous will. The main emphasis is placed on the latest reproductive procedures and methods and possible problems in their implementation in relation to human autonomy are pointed out. Objective of the study: to find the optimal combination of state, public and private legal interests in the exercise of the right to reproductive health and to establish the legal nature of patient autonomy in this area. Materials and methods. A comprehensive methodological approach was used, including a combination of legal, medical knowledge and cultural and ethical norms of society. The humanistic method was used to establish the priority of human rights and will in the regulation of reproductive rights, and the method of gender analysis was used to understand the differences in the level of autonomy of women and men in reproductive medicine. The empirical method was used in the author’s survey of 402 women in the Republic of Poland and Ukraine on their personal and state legal attitudes to reproductive health. Results. Two approaches to state regulation of reproductive autonomy are envisaged: the first one limits legal regulation and state intervention, giving priority to individual autonomy, the second one indicates a legitimate broad, but legitimate possibility of interfering with the patient’s autonomous decision in the field of reproductive health. The author distinguishes legitimate groups of restrictive legal phenomena in the field of the right to exercise the human reproductive function: general legal restrictions and special restrictions relating exclusively to the human right to reproduction. Conclusions. It is need to update national legislation by specifying clearer medical protocols regarding the number of embryos during embryo transfer and their dependence on the patient’s age. The author identifies the newest possibilities of gene editing as an ethical and medical problem and proves the public fear in this area, which requires additional legal regulation.

Xiaofeng Liu, Jerry L. Prince, Fangxu Xing et al.

Self-training is an important class of unsupervised domain adaptation (UDA) approaches that are used to mitigate the problem of domain shift, when applying knowledge learned from a labeled source domain to unlabeled and heterogeneous target domains. While self-training-based UDA has shown considerable promise on discriminative tasks, including classification and segmentation, through reliable pseudo-label filtering based on the maximum softmax probability, there is a paucity of prior work on self-training-based UDA for generative tasks, including image modality translation. To fill this gap, in this work, we seek to develop a generative self-training (GST) framework for domain adaptive image translation with continuous value prediction and regression objectives. Specifically, we quantify both aleatoric and epistemic uncertainties within our GST using variational Bayes learning to measure the reliability of synthesized data. We also introduce a self-attention scheme that de-emphasizes the background region to prevent it from dominating the training process. The adaptation is then carried out by an alternating optimization scheme with target domain supervision that focuses attention on the regions with reliable pseudo-labels. We evaluated our framework on two cross-scanner/center, inter-subject translation tasks, including tagged-to-cine magnetic resonance (MR) image translation and T1-weighted MR-to-fractional anisotropy translation. Extensive validations with unpaired target domain data showed that our GST yielded superior synthesis performance in comparison to adversarial training UDA methods.

Holly Rosencranz, Japhia Ramkumar, Leslie Herzog et al.

Introduction Doctors are trusted voices for communities and can influence lawmakers on climate change. Effective climate policy advocacy requires awareness, knowledge, and skills not typically taught in medical schools. Such curriculum additions could help students describe reasons for physicians to engage in climate policy advocacy and compose advocacy presentations. Methods To empower engagement in climate policies and develop advocacy skills, we deployed three 90-minute workshops at three institutions for first-, second-, and fourth-year students. The workshops included background on various climate policies of concern to health care professionals, advocacy guidance, scripts and factsheets from physicians’ meetings illustrating advocacy opportunities for students and physicians, and active learning exercises. The exercises utilized advocacy templates and actual proposed actions on climate change. Students worked in small groups on advocacy presentations’ content and format. Each group shared its work, and facilitators provided feedback. Results Out of 102 participants, 29 completed a survey (28% response rate). Using a Likert scale and narratives, students reported significant improvements in readiness to advocate for legislation or policies to mitigate the health effects of climate change, awareness of advocacy opportunities, and capability to prepare advocacy documents. Discussion Workshops on climate policy advocacy can equip medical students with important perspectives on their responsibilities and opportunities, as well as skills to be effective. The physician's voice is critical to promoting policies related to the health impacts of climate change. Targeted workshops with actual examples and exercises on climate advocacy are feasible and important additions to the curriculum.

Teja Mannepalli, Aurobinda Routray

Source localization using EEG is important in diagnosing various physiological and psychiatric diseases related to the brain. The high temporal resolution of EEG helps medical professionals assess the internal physiology of the brain in a more informative way. The internal sources are obtained from EEG by an inversion process. The number of sources in the brain outnumbers the number of measurements. In this article, a comprehensive review of the state of the art sparse source localization methods in this field is presented. A recently developed method, certainty based reduced sparse solution (CARSS), is implemented and is examined. A vast comparative study is performed using a sixty four channel setup involving two source spaces. The first source space has 5004 sources and the other has 2004 sources. Four test cases with one, three, five, and seven simulated active sources are considered. Two noise levels are also being added to the noiseless data. The CARSS is also evaluated. The results are examined. A real EEG study is also attempted.

Yoon Lee, Anil Aswani

Though the sharing of medical data has the potential to lead to breakthroughs in health care, the sharing process itself exposes patients and health care providers to various risks. Patients face risks due to the possible loss in privacy or livelihood that can occur when medical data is stolen or used in non-permitted ways, whereas health care providers face risks due to the associated liability. For medical data, these risks persist even after anonymizing/deidentifying, according to the standards defined in existing legislation, the data sets prior to sharing, because shared medical data can often be deanonymized/reidentified using advanced artificial intelligence and machine learning methodologies. As a result, health care providers are hesitant to share medical data. One possible solution to encourage health care providers to responsibly share data is through the use of cybersecurity insurance contracts. This paper studies the problem of designing optimal cybersecurity insurance contracts, with the goal of encouraging the sharing of the medical data. We use a principal-agent model with moral hazard to model various scenarios, derive the optimal contract, discuss its implications, and perform numerical case studies. In particular, we consider two scenarios: the first scenario is where a health care provider is selling medical data to a technology firm who is developing an artificial intelligence algorithm using the shared data. The second scenario is where a group of health care providers share health data amongst themselves for the purpose of furthering medical research using the aggregated medical data.

Nóra Menich

Based on notions from the Capability Approach, this study investigates the service delivery process for assistive technology in Hungary. The research aimed to explore whether the service delivery is person-centered, with a specific focus on the users’ possible choices. In addition to a comprehensive analysis of legislative and policy documents, qualitative data were collected in semi-structured interviews with users and professionals (<i>n</i> = 31) to gain a deeper understanding of personal experiences. Our findings indicate that the service delivery system is product-centered and dominated by financial considerations. The policy and legislation framework does not provide an institutional guarantee for users to be able to have their voices heard; the extent to which their opinions and preferences prevail depends on the attitude, knowledge, and goodwill of the professionals involved in the process. The realization of a person-centered approach will be hindered as long as the users’ needs are viewed from a medical point of view.

Malcolm M. MacFarlane

This paper explores the marginalization experienced by International Medical Graduates (IMGs) in the Canadian Residency Matching Service (CaRMS) Match. This marginalization occurs despite all IMGs being Canadian citizens or permanent residents, and having objectively demonstrated competence equivalent to that expected of a graduate of a Canadian medical School through examinations such as the MCCQE1 and the National Assessment Collaboration OSCE. This paper explores how the current CaRMS Match works, evidence of marginalization, and ethnicity and human rights implications of the current CaRMS system. A brief history of post graduate medical education and the residency selection process is provided along with a brief legal analysis of authority for making CaRMS eligibility decisions. Current CaRMS practices are situated in the context of Provincial fairness legislation, and rationalizations and rationales for the current CaRMS system are explored. The paper examines objective indicators of IMG competence, as well as relevant legislation regarding international credential recognition and labour mobility. The issues are placed in the context of current immigration and education policies and best practices. An international perspective is provided through comparison with the United States National Residency Matching Program. Suggestions are offered for changes to the current CaRMS system to bring the process more in line with legislation and current Canadian value systems, such that “A Canadian is a Canadian.”

Andreas Maier, Harald Köstler, Marco Heisig et al.

In this article, we perform a review of the state-of-the-art of hybrid machine learning in medical imaging. We start with a short summary of the general developments of the past in machine learning and how general and specialized approaches have been in competition in the past decades. A particular focus will be the theoretical and experimental evidence pro and contra hybrid modelling. Next, we inspect several new developments regarding hybrid machine learning with a particular focus on so-called known operator learning and how hybrid approaches gain more and more momentum across essentially all applications in medical imaging and medical image analysis. As we will point out by numerous examples, hybrid models are taking over in image reconstruction and analysis. Even domains such as physical simulation and scanner and acquisition design are being addressed using machine learning grey box modelling approaches. Towards the end of the article, we will investigate a few future directions and point out relevant areas in which hybrid modelling, meta learning, and other domains will likely be able to drive the state-of-the-art ahead.

Kaicong Sun, Sven Simon

Image registration is a fundamental task for medical imaging. Resampling of the intensity values is required during registration and better spatial resolution with finer and sharper structures can improve the resampling performance and hence the registration accuracy. Super-resolution (SR) is an algorithmic technique targeting at spatial resolution enhancement which can achieve an image resolution beyond the hardware limitation. In this work, we consider SR as a preprocessing technique and present a CNN-based resolution enhancement module (REM) which can be easily plugged into the registration network in a cascaded manner. Different residual schemes and network configurations of REM are investigated to obtain an effective architecture design of REM. In fact, REM is not confined to image registration, it can also be straightforwardly integrated into other vision tasks for enhanced resolution. The proposed REM is thoroughly evaluated for deformable registration on medical images quantitatively and qualitatively at different upscaling factors. Experiments on LPBA40 brain MRI dataset demonstrate that REM not only improves the registration accuracy, especially when the input images suffer from degraded spatial resolution, but also generates resolution enhanced images which can be exploited for successive diagnosis.

Ilias Chalkidis, Manos Fergadiotis, Nikolaos Manginas et al.

Major scandals in corporate history have urged the need for regulatory compliance, where organizations need to ensure that their controls (processes) comply with relevant laws, regulations, and policies. However, keeping track of the constantly changing legislation is difficult, thus organizations are increasingly adopting Regulatory Technology (RegTech) to facilitate the process. To this end, we introduce regulatory information retrieval (REG-IR), an application of document-to-document information retrieval (DOC2DOC IR), where the query is an entire document making the task more challenging than traditional IR where the queries are short. Furthermore, we compile and release two datasets based on the relationships between EU directives and UK legislation. We experiment on these datasets using a typical two-step pipeline approach comprising a pre-fetcher and a neural re-ranker. Experimenting with various pre-fetchers from BM25 to k nearest neighbors over representations from several BERT models, we show that fine-tuning a BERT model on an in-domain classification task produces the best representations for IR. We also show that neural re-rankers under-perform due to contradicting supervision, i.e., similar query-document pairs with opposite labels. Thus, they are biased towards the pre-fetcher's score. Interestingly, applying a date filter further improves the performance, showcasing the importance of the time dimension.

Vincent Thériault, Diane Guay, Gina Bravo

Background: In Quebec, medical aid in dying (MAiD) is legal under certain conditions. Access is currently restricted to patients who are able to consent at the time of the act, which excludes most people with dementia at an advanced stage. However, recent legislative and political developments have opened the door to an extension of the legislation that could give them access to MAiD. Our study aimed to explore the attitudes of people with early-stage dementia toward MAiD should it become accessible to them. Methods: We used a qualitative descriptive design consisting of eight face-to-face semi-structured interviews with persons living with early-stage Alzheimer’s disease, followed by a thematic analysis of the contents of the interviews. Results and Interpretations: Analysis revealed three main themes: 1) favourable to MAiD; 2) avoiding advanced dementia; and 3) disposition to request MAiD. Most participants anticipated dementia to be a painful experience. The main reasons for supporting MAiD were to avoid cognitive loss, dependence on others for their basic needs, and suffering for both themselves and their loved ones. Every participant said that they would ask for MAiD at some point should it become available to incompetent patients and most wished that it would be legal to access it through a request written before losing capacity. Conclusion: The reasons for which persons with Alzheimer’s disease want MAiD are related to the particular trajectory of the disease. Any policy to extend MAiD to incompetent patients should take their perspective into account.

Jie Su, Yi Cao, Yuehui Chen et al.

Abstract Background Protection of privacy data published in the health care field is an important research field. The Health Insurance Portability and Accountability Act (HIPAA) in the USA is the current legislation for privacy protection. However, the Institute of Medicine Committee on Health Research and the Privacy of Health Information recently concluded that HIPAA cannot adequately safeguard the privacy, while at the same time researchers cannot use the medical data for effective researches. Therefore, more effective privacy protection methods are urgently needed to ensure the security of released medical data. Methods Privacy protection methods based on clustering are the methods and algorithms to ensure that the published data remains useful and protected. In this paper, we first analyzed the importance of the key attributes of medical data in the social network. According to the attribute function and the main objective of privacy protection, the attribute information was divided into three categories. We then proposed an algorithm based on greedy clustering to group the data points according to the attributes and the connective information of the nodes in the published social network. Finally, we analyzed the loss of information during the procedure of clustering, and evaluated the proposed approach with respect to classification accuracy and information loss rates on a medical dataset. Results The associated social network of a medical dataset was analyzed for privacy preservation. We evaluated the values of generalization loss and structure loss for different values of k and a, i.e. $$k$$ k  = {3, 6, 9, 12, 15, 18, 21, 24, 27, 30}, a = {0, 0.2, 0.4, 0.6, 0.8, 1}. The experimental results in our proposed approach showed that the generalization loss approached optimal when a = 1 and k = 21, and structure loss approached optimal when a = 0.4 and k = 3. Conclusion We showed the importance of the attributes and the structure of the released health data in privacy preservation. Our method achieved better results of privacy preservation in social network by optimizing generalization loss and structure loss. The proposed method to evaluate loss obtained a balance between the data availability and the risk of privacy leakage.

Rongchen Guo, Takanori Fujiwara, Yiran Li et al.

Machine learning for data-driven diagnosis has been actively studied in medicine to provide better healthcare. Supporting analysis of a patient cohort similar to a patient under treatment is a key task for clinicians to make decisions with high confidence. However, such analysis is not straightforward due to the characteristics of medical records: high dimensionality, irregularity in time, and sparsity. To address this challenge, we introduce a method for similarity calculation of medical records. Our method employs event and sequence embeddings. While we use an autoencoder for the event embedding, we apply its variant with the self-attention mechanism for the sequence embedding. Moreover, in order to better handle the irregularity of data, we enhance the self-attention mechanism with consideration of different time intervals. We have developed a visual analytics system to support comparative studies of patient records. To make a comparison of sequences with different lengths easier, our system incorporates a sequence alignment method. Through its interactive interface, the user can quickly identify patients of interest and conveniently review both the temporal and multivariate aspects of the patient records. We demonstrate the effectiveness of our design and system with case studies using a real-world dataset from the neonatal intensive care unit of UC Davis.

Tonghe Wang, Yang Lei, Yabo Fu et al.

This paper reviewed the deep learning-based studies for medical imaging synthesis and its clinical application. Specifically, we summarized the recent developments of deep learning-based methods in inter- and intra-modality image synthesis by listing and highlighting the proposed methods, study designs and reported performances with related clinical applications on representative studies. The challenges among the reviewed studies were summarized in the discussion part.

Lia Morra, Luca Piano, Fabrizio Lamberti et al.

Deep learning has thrived by training on large-scale datasets. However, in many applications, as for medical image diagnosis, getting massive amount of data is still prohibitive due to privacy, lack of acquisition homogeneity and annotation cost. In this scenario, transfer learning from natural image collections is a standard practice that attempts to tackle shape, texture and color discrepancies all at once through pretrained model fine-tuning. In this work, we propose to disentangle those challenges and design a dedicated network module that focuses on color adaptation. We combine learning from scratch of the color module with transfer learning of different classification backbones, obtaining an end-to-end, easy-to-train architecture for diagnostic image recognition on X-ray images. Extensive experiments showed how our approach is particularly efficient in case of data scarcity and provides a new path for further transferring the learned color information across multiple medical datasets.