Yoxas/statistical_literacy huggingface dataset:
Deep brain stimulation of the bilateral subthalamic nucleus (STN-DBS) improves motor fluctuation and severe dyskinesia in advanced Parkinson's disease (PD). Effects on non-motor symptoms, such as neurocognitive side effects, can also influence the quality of life of both patients with PD and caregivers. Predictive quantitative factors associated with postoperative neurocognitive deterioration therefore warrant further attention. Here, we evaluated preoperative electroencephalogram (EEG) as a predictive marker for changes in neurocognitive functions after surgery. Scalp EEG was recorded preoperatively from 17 patients with PD who underwent bilateral STN-DBS. Global relative power in the theta, alpha, and beta bands was calculated. Cognitive function was assessed with neuropsychological batteries preoperatively and 1 year after STN-DBS. Performance on the Symbol Search subtest of the WAIS III declined 1 year after DBS. The theta band was chosen for analysis with a 40% cutoff point for increased (≥ 40%) and decreased (< 40%) power. No significant differences between the two groups in baseline performance on most neuropsychological batteries were found, except for the Digit Symbol Coding subtest of the WAIS III. Changes in visual spatial functions were significantly different between groups. The increased theta band power group demonstrated a significant deterioration in performance on the WAIS III Matrix Reasoning subtest and the copy and immediate recall tasks of the Rey-Osterrieth complex figure test. These findings suggest that preoperative increases in theta power are related to postoperative deterioration of visuospatial function, which indicates the predictive potential of preoperative quantitative EEG for neurocognitive changes after STN-DBS.
Deep learning (DL) methods where interpretability is intrinsically considered as part of the model are required to better understand the relationship of clinical and imaging-based attributes with DL outcomes, thus facilitating their use in the reasoning behind the medical decisions. Latent space representations built with variational autoencoders (VAE) do not ensure individual control of data attributes. Attribute-based methods enforcing attribute disentanglement have been proposed in the literature for classical computer vision tasks in benchmark data. In this paper, we propose a VAE approach, the Attri-VAE, that includes an attribute regularization term to associate clinical and medical imaging attributes with different regularized dimensions in the generated latent space, enabling a better-disentangled interpretation of the attributes. Furthermore, the generated attention maps explained the attribute encoding in the regularized latent space dimensions. Using the Attri-VAE approach we analyzed healthy and myocardial infarction patients with clinical, cardiac morphology, and radiomics attributes. The proposed model provided an excellent trade-off between reconstruction fidelity, disentanglement, and interpretability, outperforming state-of-the-art VAE approaches according to several quantitative metrics. The resulting latent space allowed the generation of realistic synthetic data in the trajectory between two distinct input samples or along a specific attribute dimension to better interpret changes between different cardiac conditions.
Deep learning-based methods can promote and save critical time for the diagnosis of pneumonia from computed tomography (CT) images of the chest, where the methods usually rely on large amounts of labeled data to learn good visual representations. However, medical images are difficult to obtain and need to be labeled by professional radiologists. To address this issue, a novel contrastive learning model with token projection, namely CoTP, is proposed for improving the diagnostic quality of few-shot chest CT images. Specifically, (1) we utilize solely unlabeled data for fitting CoTP, along with a small number of labeled samples for fine-tuning, (2) we present a new Omicron dataset and modify the data augmentation strategy, i.e., random Poisson noise perturbation for the CT interpretation task, and (3) token projection is utilized to further improve the quality of the global visual representations. The ResNet50 pre-trained by CoTP attained accuracy (ACC) of 92.35%, sensitivity (SEN) of 92.96%, precision (PRE) of 91.54%, and the area under the receiver-operating characteristics curve (AUC) of 98.90% on the presented Omicron dataset. On the contrary, the ResNet50 without pre-training achieved ACC, SEN, PRE, and AUC of 77.61, 77.90, 76.69, and 85.66%, respectively. Extensive experiments reveal that a model pre-trained by CoTP greatly outperforms that without pre-training. The CoTP can improve the efficacy of diagnosis and reduce the heavy workload of radiologists for screening of Omicron pneumonia.
Deep models are commonly treated as black-boxes and lack interpretability. Here, we propose a novel approach to interpret deep image classifiers by generating discrete masks. Our method follows the generative adversarial network formalism. The deep model to be interpreted is the discriminator while we train a generator to explain it. The generator is trained to capture discriminative image regions that should convey the same or similar meaning as the original image from the model's perspective. It produces a probability map from which a discrete mask can be sampled. Then the discriminator is used to measure the quality of the sampled mask and provide feedbacks for updating. Due to the sampling operations, the generator cannot be trained directly by back-propagation. We propose to update it using policy gradient. Furthermore, we propose to incorporate gradients as auxiliary information to reduce the search space and facilitate training. We conduct both quantitative and qualitative experiments on the ILSVRC dataset. Experimental results indicate that our method can provide reasonable explanations for predictions and outperform existing approaches. In addition, our method can pass the model randomization test, indicating that it is reasoning the attribution of network predictions.
Deep neural networks (DNNs) with a complex structure and multiple nonlinear processing units have achieved great successes for feature learning in image and visualization analysis. Due to interpretability of the "black box" problem in DNNs, however, there are still many obstacles to applications of DNNs in various real-world cases. This paper proposes a new DNN model, knowledge-based deep stacked denoising auto-encoders (KBSDAE), which inserts the knowledge (i.e., confidence and classification rules) into the deep network structure. This model not only can offer a good understanding of the representations learned by the deep network but also can produce an improvement in the learning performance of stacked denoising auto-encoder (SDAE). The knowledge discovery algorithm is proposed to extract confidence rules to interpret the layerwise network (i.e., denoising auto-encoder (DAE)). The symbolic language is developed to describe the deep network and shows that it is suitable for the representation of quantitative reasoning in a deep network. The confidence rule insertion to the deep network is able to produce an improvement in feature learning of DAEs. The classification rules extracted from the data offer a novel method for knowledge insertion to the classification layer of SDAE. The testing results of KBSDAE on various benchmark data indicate that the proposed method not only effectively extracts knowledge from the deep network, but also shows better feature learning performance than that of those typical DNNs (e.g., SDAE).
Defence Medical Services personnel regularly deploy overseas to deliver training activities as part of defence engagement (DE) to positively influence partners and others. There remains scope for medical planners to enhance our understanding of how to optimally use medical staff and assets for DE. We aimed to develop a tool to improve planning for DE activities delivering first aid training. We used nominal group technique to conduct a focus group with UK experts in planning first aid training DE activities to identify and prioritise important planning considerations within a conceptual framework based on the Defence Lines of Development. We validated and refined this framework with international experts from partner nation militaries to help strengthen the final planning tool. We developed a detailed tool covering training curriculum and logistical and infrastructure requirements to deliver safe and effective DE training activities. First aid training engagement priorities include being tailored to the training audience and in harmony with the national or military healthcare services of that country. Messaging around the women, peace and security agenda should be integrated into training packages at conception to be effective. We propose a planning tool to aid in designing first aid training that considers the necessary components to support meaningful education and effective engagement in support of UK's strategic goals. We welcome the use of and feedback on this tool and its impact to those planning first aid training activities as part of DE operations.
Deficits in knowledge of parameters surrounding anticoagulant therapy administration carry substantial risks for patient harm. Patients prescribed anticoagulants may overestimate their knowledge of anticoagulant use principles. The purpose of this study was to analyze independent pharmacy patients' perceived versus actual knowledge of their anticoagulation therapies. A 15-item survey across a dual-site pharmacy location evaluated patients' confidence in their understanding of anticoagulant prescribing parameters and their awareness of measures to take to safeguard anticoagulant therapy routines. A pharmacist reviewed patient responses to survey items referencing actual understanding of anticoagulant therapies at the conclusion of the survey. Data analyses occurred by gender, age, ethnicity, location, living situation, self-management of anticoagulants, patient perception of anticoagulant safety, and reported receipt of anticoagulant education. Linear regressions were used for statistical analyses, with significance set at p<.05. During the span of three months, 45 patients, 20 females and 25 males, completed the survey. Perceived anticoagulant knowledge did not differ significantly by demographics. Actual anticoagulant knowledge declined by over 20% between age groups 60-69 and 80-89 years old. African Americans displayed 10% lower actual anticoagulant knowledge than Caucasian Americans. This study revealed gaps in knowledge of anticoagulants among patients at an independent pharmacy. An appeal remains for more strategies to assist with knowledge of anticoagulant regimens. Healthcare institutions should continue to incorporate programs geared towards ongoing anticoagulant education, such as question and answer sessions, along with peer support mediums that foster optimal outcomes.
Deficits in neurocognitive function have been demonstrated in individuals with schizophrenia and in the unaffected family members of these individuals. Genetic studies of such complementary traits, along with traditional analyses of diagnosis, may help to elucidate the biological pathways underlying familial liability to schizophrenia and related disorders. The authors conducted a multiplex, multigenerational family study using a genome-wide screen for schizophrenia and related neurocognitive phenotypes. Participants were 1) 676 European American individuals from 43 families, ascertained through an individual with schizophrenia, and 2) 236 healthy comparison subjects. Participants were evaluated clinically and examined through the use of a computerized neurocognitive test battery that provided measures of accuracy and speed on the cognitive domains of abstraction and mental flexibility; attention; verbal, face, and spatial memory; language and reasoning; spatial and emotion processing; and sensorimotor dexterity. A genome-wide linkage screen was also performed. Healthy comparison subjects were included in order to obtain normative phenotypic data but were not genotyped. Significant evidence for linkage of schizophrenia to chromosome 19q was observed. Analysis of cognitive traits revealed significant linkage to chromosome 5q for the domains of abstraction and mental flexibility. A variety of other neurocognitive traits also showed nominal evidence of linkage to the 5q region. Joint analyses with diagnosis suggested that this quantitative trait locus may also influence schizophrenia. Although chromosome 5 has been implicated in previous linkage studies of schizophrenia, the identification of the chromosome 19 quantitative trait locus is a novel finding. The identification of the chromosome 5 quantitative trait locus through linkage to neurocognitive phenotypes in the present study may inform functional hypotheses pertaining to how genotypes are connected to disease.
Defining profiles of patients that could benefit from relevant anti-cancer treatments is essential. An increasing number of specific criteria are necessary to be eligible to specific anti-cancer therapies. This study aimed to develop an automated algorithm able to detect patient and tumor characteristics to reduce the time-consuming prescreening for trial inclusions without delay. Hence, 640 anonymized multidisciplinary team meetings (MTM) reports concerning lung cancers from one French teaching hospital data warehouse between 2018 and 2020 were annotated. To automate the extraction of eight major eligibility criteria, corresponding to 52 classes, regular expressions were implemented. The RegEx's evaluation gave a F1-score of 93% in average, a positive predictive value (precision) of 98% and sensitivity (recall) of 92%. However, in MTM, fill rates variabilities among patient and tumor information remained important (from 31% to 100%). Genetic mutations and rearrangement test results were the least reported characteristics and also the hardest to automatically extract. To ease prescreening in clinical trials, the PreScIOUs study demonstrated the additional value of rule based and machine learning based methods applied on lung cancer MTM reports.
Deinstitutionalization of persons living with mental illness has led to many patients residing in communities with family members and shifting the burden of care and caregiving from hospitals to homes. The aim of the study was to determine the burden on caregivers of patients with schizophrenia at Edward Francis Small Teaching Hospital (EFSTH). This was a descriptive cross-sectional study design with a sample consisting of 161 randomly selected caregivers of patients with schizophrenia. The GHQ-12 questionnaire was used to determine the general health status of the caregivers. The caregivers' burden was assessed using the Zarit Burden Scale. The data were collected using the researcher-administered method. The collected data were analyzed with IBM SPSS Version 20 using descriptive statistics, mean differences, and the general linear model (GLM). The main findings of this study were that caregivers experienced a significantly high level of burden. Most of them experienced high levels of physical (70%), psychological (93.2%), social (78.3%) and financial (55.3%) burdens. Employment status, specifically unemployment status and belonging to the Wolof ethnic group, was a significant predictor of the level of financial burden on the caregivers. Similarly, the total score for social burden was also significantly greater among unemployed caregivers. Educational level was a significant predictor of the total score on the psychological and physical burden scale. The caregivers of patients with schizophrenia at EFSTH are experiencing a high level of burden as a result of their caregiving role, which affects their health, and this calls for urgent intervention.
Delayed engagement with health services is a key contributor to poorer health outcomes experienced by men. Patterns of health service usage which reduce the opportunity for disease prevention and health promotion appear to be especially prominent amongst young men. To identify the multiple and intersecting determinants of young men's help-seeking practices and health services usage, this review uses the social ecological model (SEM) to guide a critical synthesis of the literature on barriers and facilitators experienced by young men in accessing health services. A systematic review was conducted across five databases (MEDLINE, Embase, PsychINFO, CINAHL and Scopus). Included studies presented primary data regarding young men's (12-24 years) barriers and/or facilitators to seeking and accessing health care. Thirty-one studies (24 qualitative and 7 quantitative) underwent data extraction, quality appraisal and thematic analysis under the guiding framework of the SEM. Seven key themes were constructed, encapsulating the perceived barriers and facilitators to help-seeking and accessing health care experienced by young men, including masculine attitudes, health literacy, social pressure, service accessibility, economic factors, service characteristics and cultural attitudes. These findings highlight the complex interplay between the individual, interpersonal, organizational and societal factors impacting young men's healthcare engagement. They also illuminate avenues for multifaceted, context-specific interventions to enhance healthcare accessibility for this group, including addressing health literacy gaps, providing culturally sensitive care and reducing cost barriers.
Delays in breast cancer care, one important attributable factor for breast cancer being diagnosed at advanced stages, are not systematically studied in many countries. This study assessed the magnitude and factors associated with delays in breast cancer care in Sri Lanka, from symptom detection to treatment initiation. We interviewed 800 consecutively sampled female breast cancer patients, diagnosed within the last 12 months, using context-specific questionnaires and medical records. We defined the cut-off times for delays using international guidelines and features of the national health system and care-seeking patterns in the country. Delays were estimated as proportions with 95% confidence intervals and presented for: (i) presentation delay; (ii) diagnosis delay and (iii) treatment delay. We looked at how sociodemographic and healthcare availability and accessibility at the individual level were associated with delays using multivariate logistic regression, with a P value of 0.05 to define statistical significance. Nearly two-thirds of patients reported a presentation delay (63.3%, 95% confidence interval 59.9-66.6%). A diagnosis delay (36.7%, 95% confidence interval 33.4-40.0%) was also seen among one-third, whereas treatment delays (13.2%, 95% confidence interval 10.8-15.5%) were less common. Low family monthly income (odds ratio 6.3; 95% confidence interval 4.2-9.3) and poor knowledge on breast cancer (odds ratio 2.7; 95% confidence interval 1.8-3.8) were associated with presentation delay. Poor health literacy (odds ratio 1.7; 95% confidence interval 1.1-2.7) and the need to make more than two visits to the first contact health provider prior to diagnosis (odds ratio 7.2; 95% confidence interval 4.6-11.1) were associated with diagnosis delays, whereas directly contacting an appropriate specialised health provider once the lump was detected reduced diagnosis delay (odds ratio 0.3; 95% confidence interval 0.2-0.4). Having undergone a core biopsy (odds ratio 0.5; 95% confidence interval 0.3-0.8) and having a mammogram (odds ratio 0.6; 95% confidence interval 4.7-32.7) reduced the likelihood of treatment delays. Our study findings show that delays in breast cancer care in Sri Lanka are much lower than those in other lower-middle income countries. However, there is significant room for improvement, especially in relation to the excellence in quality of care, such as improving access to mammography services. Periodical estimation of breast cancer delays enabling temporal comparisons will probably provide useful information to policy makers in improving care delivery for breast cancer patients and, hence, is recommended. Such future assessments designed for comparisons between different treatment modalities would provide more information to assist policy decisions in care improvement.
Delays in seeking care in health facilities during pregnancy and childbirth can potentially lead to adverse outcomes for women with obstetric complications. These complications lead to maternal mortality and morbidity in developing countries such as Ethiopia. The magnitude and underlying causes of maternal first delay in Ethiopia, particularly in rural areas of the country, are not well documented. This study aims to assess the magnitude of delay in the decision to seek care and associated factors among mothers who gave birth in rural health centers in Wolaita Zone, Southern Ethiopia. A facility-based cross-sectional study was undertaken among mothers who gave birth in rural health centers of Wolaita Zone, Southern Ethiopia, from 30 June 2020 to 30 July 2022. A sample size of 410 study participants was selected from each public health center using the systematic random sampling method. Data were collected from a pretested and structured questionnaire using an Open Data Kit; analysis was carried out using SPSS version 25. The determining factors for the first delay were identified using binary logistic regression. Variables with a p-value of less than 0.25 in binary analysis were selected for a multivariable analysis. Variables with a p-value of less than 0.05 were considered statistically significant. The magnitude of delay in the decision to seek care in health facilities was 42.1% among mothers who gave birth in rural health centers in the Wolaita Zone, Southern Ethiopia. Unemployed mothers (Adjusted Odd Ratio, 2,529; 95% CI, 1.546, 4.136), husbands with no formal education (Adjusted Odd Ratio, 1.290; 95% CI, 1.029, 1.616), mothers who had negative attitudes towards seeking care in health facilities, and (Adjusted Odd Ratio; 1.695; 95% CI, 1.061, 2.709) were significantly associated with a delay in the decision to seek care at a health facility. The magnitude of the first maternal decision to seek care at health facilities among mothers was high in the study area. Efforts should be made to strengthen the literacy level of the husbands of mothers and increase household income through various income-generating approaches. In addition, the dissemination of health information could effectively raise community awareness of the importance of institutional delivery.