Yoxas/statistical_literacy huggingface dataset:
Accurate estimation of an expected discharge date (EDD) early during hospitalization impacts clinical operations and discharge planning. We conducted a retrospective study of patients discharged from six general medicine units at an academic medical center in Boston, MA from January 2017 to June 2018. We retrieved all EDD entries and patient, encounter, unit, and provider data from the electronic health record (EHR), and public weather data. We excluded patients who expired, discharged against medical advice, or lacked an EDD within the first 24 h of hospitalization. We used generalized estimating equations in a multivariable logistic regression analysis to model early EDD accuracy (an accurate EDD entered within 24 h of admission), adjusting for all covariates and clustering by patient. We similarly constructed a secondary multivariable model using covariates present upon admission alone. Of 3917 eligible hospitalizations, 890 (22.7%) had at least one accurate early EDD entry. Factors significantly positively associated (OR > 1) with an accurate early EDD included clinician-entered EDD, admit day and discharge day during the work week, and teaching clinical units. Factors significantly negatively associated (OR < 1) with an accurate early EDD included Elixhauser Comorbidity Index ≥ 11 and length of stay of two or more days. C-statistics for the primary and secondary multivariable models were 0.75 and 0.60, respectively. EDDs entered within the first 24 h of admission were often inaccurate. While several variables from the EHR were associated with accurate early EDD entries, few would be useful for prospective prediction.
Accurate identification of the failure modes of Reinforced Concrete (RC) columns based on the design parameters of the structural members is critical for earthquake-resistant design and safety evaluation of existing structures. Existing identification methods have some problems, such as high cost, incomplete consideration of influencing factors, and low precision or recall in identifying shear or flexural-shear failure. In this paper, the main factors for the failure modes of RC columns are first analyzed and studied. Then, the problem of class imbalance in data samples is investigated. To identify the failure modes of RC columns, oversampling of data (BSB-FMC), model ensembling (RFB-FMC), cost-sensitive learning (CSB-FMC) and a fusion model of three strategies (BSFCB-FMC) are proposed. And finally, the SHapley Additive exPlanations (SHAP) method is used to provide a better interpretation of the designed model. The results show that the developed strategies can improve the accuracy of identifying the failure modes of RC columns compared to the models using a single Artificial Neural Network (ANN), a Support Vector Machine (SVM), a Random Forest (RF), and Adaptive Boosting (AdaBoost). The overall accuracy of the developed BSFCB-FMC model reaches 97%, and the precision and recall for the three failure modes are both above 90%. The designed model provides a solution for fast, accurate and cost-effective identification of the failure modes of RC columns.
Accurate information on age is an essential prerequisite for demographic and epidemiological studies. This study analyzed the age data quality among the literate and illiterate (combined, rural, urban) population and examined the association between data quality and literacy. Secondary data on age statistics and literacy were obtained from census 2011. We measured age data quality for literates and illiterates (combined, rural, urban) by transforming Whipple's index known as ABCC, abbreviated based on surnames of the developers A'Hearn, Baten, and Crayen (2009). Correlation tests between literacy and ABCC were performed. RStudio (Version 1.3.1073) was used. Computed ABCC indices in majority states (union territories) for literates (data quality rough) were higher than illiterates (data quality very rough). Urban data among literates and rural data among illiterates were comparatively superior. Correlation between ABCC and literacy rates for (i) literate combined (R = 0.84, P = 3.5e-10), (ii) literate rural (R = 0.8, P = 1.1e-08), (iii) literate urban (R = 0.8, P = 1e-08), (iv) illiterate combined (R = 0.54, P = 9e-04), (v) illiterate rural (R = 0.48, P = 0.0034), and (vi) illiterate urban (R = 0.73, P = 6.4e-07) was significant. Age data quality for both literates and illiterates was poor. There was heaping at terminal digits "0" and "5" even among literates, which contradicts the theoretical expectation of quality data among literates. Correlations between data quality and literacy were significant, with comparatively lower magnitude among illiterates, which indicates the role of literacy in yielding quality data. Awareness, training, ADHAAR-based enumeration, and digitization may be suggested for better age data.
Accurate knowledge and identification of anatomical landmarks on radiographic imaging are essential when preparing for any invasive procedure. The mental foramen has been the subject of many publications because of its significance as the origin point of the mental nerve and its proximity to the lower premolar area. The aim of this study was to assess the horizontal location of the mental foramen (MF) in the samples collected from the Faculty of Dental Medicine at Umm Al-Qura University, Mecca, Saudi Arabia. This included comparing genders, ages, and bilateral symmetry. In addition, the study aimed to assess inter-rater reliability in locating the mental foramen on a digital panoramic radiograph (OPG). A total of 334 digital panoramic radiographs were chosen for retrospective analysis from a total of 2,199 images taken from the Umm Al-Qura University, Faculty of Dental Medicine, teaching hospital database. The locations were scored independently by four examiners. The area was divided into six zones, determined by drawing straight lines along the long axis of the premolars and contact areas. A scoring index of 1-6 was utilized to describe the location in relation to the premolars. Analysis was carried out using chi-square and descriptive statistics. The inter-rater reliability was calculated using Fleiss' Kappa to determine observer agreement. The ages of the patients ranged from 13 to 76 years, with a mean of 29.66. There was no significant difference related to gender, but there was a significant difference related to age. The most frequent location was zone 4, 47.6% on the left side and 51.5% on the right, followed by zone 5, 18.6% on the left side and 16.2% on the right, and zone 3, 15.3% on both the right and the left side. The location was symmetrical in 64.7% of the cases and asymmetrical in 35.3%. The inter-rater reliability among the examiners was fair. The findings of this study indicate that the MF's location was more closely related to the mandibular second premolar than the first premolar. Furthermore, bilateral symmetry was found in 65% of the sample. There was no statistical significance observed in differences between the genders. Both newly graduated and experienced dentists could identify the location of the MF from the radiograph using the MF's location in relation to the six zones.
Accurate self-assessment is crucial for the professional development of physicians. There has been sparse data on the accuracy of self-assessments on Anesthesiology Milestones. The aim of this study was to investigate the differences between resident self-assessments and faculty-assessments on Anesthesiology Milestones and the associated factors. This was a cross-sectional study conducted in a general tertiary university-affiliated hospital. We included anesthesia residents who were enrolled in the standardized residency training program in postgraduate year two and three at the time of the Milestone evaluation. We requested evaluations of competencies from both the Clinical Competency Committee faculty and the anesthesia residents themselves, utilizing the Chinese version of Anesthesiology Milestones in January 2023 and January 2024. The primary outcome was the differences between self- and faculty-assessments, calculated by subtracting the faculty-rated score from the self-rated score on each subcompetency. A total of 46 and 42 residents were evaluated in year 2023 and 2024, respectively. The self-rated sum score was significantly higher than that rated by faculty [mean (standardized deviation): 120.39 (32.41) vs. 114.44 (23.71), P = 0.008 in paired t test] with an intraclass correlation coefficient of 0.55 [95% confidence interval (CI): 0.31 to 0.70]. The Bland-Altman plots revealed significant overestimation in patient care (bias 0.32, 95% CI: 0.05 to 0.60), practice-based learning and improvement (bias 0.45, 95% CI: 0.07 to 0.84), and professionalism (bias 0.37, 95% CI: 0.02 to 0.72). Ratings from residents with master's degrees (mean difference: -1.06, 95% CI: -1.80 to -0.32, P = 0.005) and doctorate degrees (mean difference: -1.14, 95% CI: -1.91 to -0.38, P = 0.003) were closer to the faculty-assessments than residents with bachelor's degrees. Compared with patient care, the differences between self- and faculty- rated scores were smaller in medical knowledge (mean difference: -0.18, 95% CI: -0.35 to -0.02, P = 0.031) and interpersonal and communication skills (mean difference: -0.41, 95% CI: -0.64 to -0.19, P < 0.001) in the generalized estimating equation logistic regression model. This study revealed that residents tended to overestimate themselves, emphasizing the need to improve the accuracy of Milestones self-assessment. The differences between self- and faculty-assessments were associated with residents' degrees and domains of competency.
Accurate volumetric segmentation of brain tumors and tissues is beneficial for quantitative brain analysis and brain disease identification in multi-modal Magnetic Resonance (MR) images. Nevertheless, due to the complex relationship between modalities, 3D Fully Convolutional Networks (3D FCNs) using simple multi-modal fusion strategies hardly learn the complex and nonlinear complementary information between modalities. Meanwhile, the indiscriminative feature aggregation between low-level and high-level features easily causes volumetric feature misalignment in 3D FCNs. On the other hand, the 3D convolution operations of 3D FCNs are excellent at modeling local relations but typically inefficient at capturing global relations between distant regions in volumetric images. To tackle these issues, we propose an Aligned Cross-Modality Interaction Network (ACMINet) for segmenting the regions of brain tumors and tissues from MR images. In this network, the cross-modality feature interaction module is first designed to adaptively and efficiently fuse and refine multi-modal features. Secondly, the volumetric feature alignment module is developed for dynamically aligning low-level and high-level features by the learnable volumetric feature deformation field. Thirdly, we propose the volumetric dual interaction graph reasoning module for graph-based global context modeling in spatial and channel dimensions. Our proposed method is applied to brain glioma, vestibular schwannoma, and brain tissue segmentation tasks, and we performed extensive experiments on BraTS2018, BraTS2020, Vestibular Schwannoma, and iSeg-2017 datasets. Experimental results show that ACMINet achieves state-of-the-art segmentation performance on all four benchmark datasets and obtains the highest DSC score of hard-segmented enhanced tumor region on the validation leaderboard of the BraTS2020 challenge.
Accurate, reliable, non-invasive assessment of patients diagnosed with prostate cancer is essential for proper disease management. Quantitative assessment of multi-parametric MRI, such as through artificial intelligence or spectral/statistical approaches, can provide a non-invasive objective determination of the prostate tumor aggressiveness without side effects or potential poor sampling from needle biopsy or overdiagnosis from prostate serum antigen measurements. To simplify and expedite prostate tumor evaluation, this study examined the efficacy of autonomously extracting tumor spectral signatures for spectral/statistical algorithms for spatially registered bi-parametric MRI. Spatially registered hypercubes were digitally constructed by resizing, translating, and cropping from the image sequences (Apparent Diffusion Coefficient (ADC), High B-value, T2) from 42 consecutive patients in the bi-parametric MRI PI-CAI dataset. Prostate cancer blobs exceeded a threshold applied to the registered set from normalizing the registered set into an image that maximizes High B-value, but minimizes the ADC and T2 images, appearing "green" in the color composite. Clinically significant blobs were selected based on size, average normalized green value, sliding window statistics within a blob, and position within the hypercube. The center of mass and maximized sliding window statistics within the blobs identified voxels associated with tumor signatures. We used correlation coefficients (R) and p-values, to evaluate the linear regression fits of the z-score and SCR (with processed covariance matrix) to tumor aggressiveness, as well as Area Under the Curves (AUC) for Receiver Operator Curves (ROC) from logistic probability fits to clinically significant prostate cancer. The highest R (R > 0.45), AUC (>0.90), and lowest p-values (<0.01) were achieved using z-score and modified registration applied to the covariance matrix and tumor signatures selected from the "greenest" parts from the selected blob. The first autonomous tumor signature applied to spatially registered bi-parametric MRI shows promise for determining prostate tumor aggressiveness.
accurately classifying the malignancy of lesions detected in a screening scan is critical for reducing false positives. Radiomics holds great potential to differentiate malignant from benign tumors by extracting and analyzing a large number of quantitative image features. Since not all radiomic features contribute to an effective classifying model, selecting an optimal feature subset is critical. this work proposes a new multi-objective based feature selection (MO-FS) algorithm that considers sensitivity and specificity simultaneously as the objective functions during feature selection. For MO-FS, we developed a modified entropy-based termination criterion that stops the algorithm automatically rather than relying on a preset number of generations. We also designed a solution selection methodology for multi-objective learning that uses the evidential reasoning approach (SMOLER) to automatically select the optimal solution from the Pareto-optimal set. Furthermore, we developed an adaptive mutation operation to generate the mutation probability in MO-FS automatically. we evaluated the MO-FS for classifying lung nodule malignancy in low-dose CT and breast lesion malignancy in digital breast tomosynthesis. the experimental results demonstrated that the feature set selected by MO-FS achieved better classification performance than features selected by other commonly used methods. the proposed method is general and more effective radiomic feature selection strategy.
Accurately measuring and recording the length or area of lesions affects the judgement of the forensic report, surgical wound management and, in some countries, the billing of health care services. The aim of this study was to determine whether the length and area of lesions described by physicians by estimation are accurate. This study was designed as a cross-sectional descriptive study and was conducted with 494 participants consisting of internists and physicians at Ondokuz Mayıs University, Faculty of Medicine. The participants were asked to estimate the lengths or areas of 6 different shapes in the questionnaire form without using a measuring instrument. Of the participants, 216 (43.7%) were interned physicians and 278 (56.3%) were physicians. Most 122 people (24.7%) answered the curved line shape with a length of 4 cm as "exact value". The average of the values given by the internists to each shape was higher than the average of the values given by the physicians to each shape and was found to be further away from the true value. It was determined that more than half of the participants gave values above the actual length and area values of the shapes. It was observed that the rate of correct estimation of line shapes was higher than the rate of correct estimation of area shapes both as exact values and with ±10% margin of error. For line shapes, it was observed that the number of those who gave exact values decreased as the line length increased. When preparing forensic reports, determining surgical wound management and billing, estimated data should not be used in lesion description. It would also be useful to develop tools that will enable physicians to make measurements in terms of easy use.
Accurately measuring the evolution of Multiple Sclerosis (MS) with magnetic resonance imaging (MRI) critically informs understanding of disease progression and helps to direct therapeutic strategy. Deep learning models have shown promise for automatically segmenting MS lesions, but the scarcity of accurately annotated data hinders progress in this area. Obtaining sufficient data from a single clinical site is challenging and does not address the heterogeneous need for model robustness. Conversely, the collection of data from multiple sites introduces data privacy concerns and potential label noise due to varying annotation standards. To address this dilemma, we explore the use of the federated learning framework while considering label noise. Our approach enables collaboration among multiple clinical sites without compromising data privacy under a federated learning paradigm that incorporates a noise-robust training strategy based on label correction. Specifically, we introduce a Decoupled Hard Label Correction (DHLC) strategy that considers the imbalanced distribution and fuzzy boundaries of MS lesions, enabling the correction of false annotations based on prediction confidence. We also introduce a Centrally Enhanced Label Correction (CELC) strategy, which leverages the aggregated central model as a correction teacher for all sites, enhancing the reliability of the correction process. Extensive experiments conducted on two multi-site datasets demonstrate the effectiveness and robustness of our proposed methods, indicating their potential for clinical applications in multi-site collaborations to train better deep learning models with lower cost in data collection and annotation.
Accurately predicting distant metastasis in head & neck cancer has the potential to improve patient survival by allowing early treatment intensification with systemic therapy for high-risk patients. By extracting large amounts of quantitative features and mining them, radiomics has achieved success in predicting treatment outcomes for various diseases. However, there are several challenges associated with conventional radiomic approaches, including: (1) how to optimally combine information extracted from multiple modalities; (2) how to construct models emphasizing different objectives for different clinical applications; and (3) how to utilize and fuse output obtained by multiple classifiers. To overcome these challenges, we propose a unified model termed as multifaceted radiomics (M-radiomics). In M-radiomics, a deep learning with stacked sparse autoencoder is first utilized to fuse features extracted from different modalities into one representation feature set. A multi-objective optimization model is then introduced into M-radiomics where probability-based objective functions are designed to maximize the similarity between the probability output and the true label vector. Finally, M-radiomics employs multiple base classifiers to get a diverse Pareto-optimal model set and then fuses the output probabilities of all the Pareto-optimal models through an evidential reasoning rule fusion (ERRF) strategy in the testing stage to obtain the final output probability. Experimental results show that M-radiomics with the stacked autoencoder outperforms the model without the autoencoder. M-radiomics obtained more accurate results with a better balance between sensitivity and specificity than other single-objective or single-classifier-based models.
Accurately predicting kidney outcomes in IgA nephropathy is crucial for clinical decision making. Insufficient use of longitudinal data in previous studies has limited the accuracy and interpretability of prediction models for failing to reflect the chronic nature of IgA nephropathy. This study aimed at establishing a multivariable dynamic deep learning model using comprehensive longitudinal data for the prediction of kidney outcomes in IgA nephropathy. In this retrospective cohort study of 2,056 IgA nephropathy patients at 18 kidney centers, a total of 28,317 data points were collected by the sliding window method. Among them, 15,462 windows in a single center were randomly assigned to training (80%) and validation (20%) sets while 8797 windows in 18 kidney centers were assigned to an independently test set. Interpretable Multi-Variable Long Short-Term Memory (IMV-LSTM), a deep learning model, was implemented to predict kidney outcomes (kidney failure or 50% decline in kidney function) based on time-invariant variables measured at biopsy and time-variant variables measured during follow-up. Risk performance was evaluated using Kaplan-Meier analysis and the C statistic. Trajectory analysis was performed to assess the various trends of clinical variables during follow-up. The model achieved a higher C statistic (0.93; 95% CI, 0.92-0.95) on the test set than the XGBoost prediction model that we developed in a previous study using only baseline information (C statistic, 0.84; 95% CI, 0.80-0.88). Kaplan-Meier analysis showed that groups with lower predicted risks from the full model survived longer than groups with higher risks. Time-variant variables demonstrated higher importance scores than time-invariant variables. Within time-variant variables, more recent measurements showed higher importance scores. Further interpretation showed that certain trajectory groups of time-variant variables such as serum creatinine and urine protein were associated with elevated risks of adverse outcomes. In IgA nephropathy, a deep learning model can be used to accurately and dynamically predict kidney prognosis based on longitudinal data, and time-variant variables show strong ability to predict kidney outcome.