Model predicts blood culture positivity, aiding ICU doctors.
problem Early detection of bacteria or fungi in blood cultures.
method BiLSTM network trained on ICU clinical time series data.
result 71.95% AUC under precision-recall curve.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,291 papers · 148 categories
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48 results for “blood culture”
The paper uses WeChat data to map urban cultural resource needs.
problem Optimizing cultural resource allocation in cities.
method Data-driven framework using WeChat user check-ins and temporal LDA model.
result Identifies urban regions lacking cultural resources.
Deep learning identifies 30 common bacterial pathogens from Raman spectra.
problem Rapid and accurate identification of pathogenic bacteria.
method State-of-the-art deep learning applied to large Raman spectroscopy dataset.
result 99.0% accuracy in identifying 30 common bacterial pathogens.
Enhances content moderation with culturally-aware models.
problem Global content moderation policies miss local cultural nuances.
method Fine-tuning encoder-decoder models on media-diet data.
result Improved accuracy in local violation detection and cultural alignment.
Breiman's two cultures reconciled through blending statistical thinking.
problem Tension between parametric statistical and machine learning approaches.
method Establishing a link between parametric statistical and machine learning frameworks.
result Integrated statistical thinking can bridge the gap between two cultures.
Examines algorithmic modeling across three cultures.
problem Tackles algorithmic modeling in different cultural contexts.
method Uses parametric regressions, interpretable algorithms, and complex algorithms.
result Extension of Leo Breiman's thesis to include cultural differences.
Study shows how national culture influences investors' tendency to hold or sell stocks.
problem Investors' tendency to hold or sell stocks varies internationally.
method Examined brokerage data from 83 countries, analyzed cultural dimensions and age/gender.
result National culture, specifically long-term orientation and indulgence, influences the disposition effect.
A Cultural Market Modelq-fin.GN
Social interactions and personal tastes shape our consumption behavior of cultural products. In this study, we present a computational model of a cultural market and we aim to analyze the behavior of the consumer population as an emergent phenomena. Our results suggest that the final market shares of cultural products …
Breiman discusses two statistical cultures, advocating for more research on 'before' and 'after' the black box.
problem Statistical modeling lacks exploration of processes before and after the 'black box'.
method Analyzes Breiman's visual metaphor of two statistical cultures.
result Promotes the importance of studying the 'before' and 'after' of data transformations.
Breiman (2001) proposed to statisticians awareness of two cultures: 1. Parametric modeling culture, pioneered by R.A.Fisher and Jerzy Neyman; 2. Algorithmic predictive culture, pioneered by machine learning research. Parzen (2001), as a part of discussing Breiman (2001), proposed that researchers be aware of many cultu…
Gaussian Process model improves blood glucose prediction using contextual data.
problem Improving blood glucose prediction with contextual information.
method Gaussian Process model combining blood glucose and contextual data.
result Gaussian Process model outperforms common methods and blood glucose values alone.
New method shows cultural transmission improves language compositionality.
problem How cultural transmission affects language emergence and compositionality.
method Introduced cultural evolutionary dynamics into language emergence by periodically replacing agents in a population.
result Languages emerge with better compositional generalization due to implicit cultural transmission.
Study investigates impact of labeler socio-cultural background on affect detection model performance.
problem Impact of labeler socio-cultural background on affect detection model performance.
method Investigates the impact of labeler socio-cultural background on affect detection model performance.
result Differences in labeler background impact the performance of affect detection models.
Study finds conformity bias drives music sampling traditions.
problem How frequency-based bias drives cultural diversity in music sampling.
method Agent-based simulations in approximate Bayesian computation framework.
result Sampling patterns at population-level consistent with conformity bias.
Commentary on Rashomon Effect complicating model selection.
problem Many models equally predict data; hard to draw conclusions.
method Connections to recent ML literature exploring implications.
result Grasping Rashomon Effect can foster collaboration.
Multi-Class Classification of Blood Cells -- End to End Computer Vision based diagnosis case studycs.CV
This study automates blood cell classification using computer vision.
problem Automated detection and classification of white blood cells.
method Color-based segmentation, morphological processing, feature extraction, PCA, Unsupervised and Supervised learning algorithms, Deep Convolutional Neural Networks.
result Identification of robust algorithms for automated blood cell classification.
Deep neural network predicts blood glucose levels for diabetes patients.
problem Diabetes patients need to anticipate hyperglycaemia and hypoglycaemia.
method Used a sequential model with LSTM and Bi-LSTM layers to predict blood glucose levels.
result The proposed network outperforms baseline methods in predicting blood glucose levels.
AVEC 2019 challenges AI in detecting depression and cross-cultural emotions.
problem Detecting depression and cross-cultural emotions from audiovisual data.
method Comparison of machine learning methods under standardized conditions.
result Baseline system performance on state-of-mind, depression, and cross-cultural tasks.
Study of red blood cells using elastic surface theory.
problem Understanding the shape of red blood cells.
method Used Helfrich-Canham functional to model red blood cells as elastic surfaces.
result Cassinian ovals, except for the round sphere, do not solve the shape equation.
CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved YOLOv7 and CNN-Swin Transformercs.CV
CST-YOLO improves blood cell detection with YOLOv7 and CNN-Swin Transformer.
problem Small-scale object detection in blood cells.
method YOLOv7 architecture enhanced with CNN-Swin Transformer, W-ELAN, MCS, CatConv.
result CST-YOLO achieves 92.7%, 95.6%, and 91.1% mAP@0.5 on three blood cell datasets.
Machine learning model diagnoses COVID-19 from routine blood tests.
problem Difficulty in diagnosing COVID-19 due to inconsistent blood parameter changes.
method Constructed a machine learning model using 5,333 patients with various infections and 160 COVID-19-positive patients.
result Cross-validated AUC of 0.97, sensitivity of 81.9%, specificity of 97.9%.
Novel framework for whole-slide blood cell segmentation.
problem Semantic segmentation of whole-slide blood cell images.
method Convolutional encoder-decoder framework with VGG-16 feature extraction.
result Outstanding accuracy (97.18% global accuracy) in classwise segmentation.
Paper tackles curve pattern identification from fragmented cultural heritage objects.
problem Identify full design of curve patterns from fragmented cultural heritage objects.
method Two-stage matching algorithm combining template matching and CNN re-ranking.
result Proposed algorithm outperforms traditional methods in identifying curve patterns from fragmented objects.
Predicting blood lactate levels helps manage ICU patients without invasive tests.
problem Predict blood lactate levels accurately in ICU patients without invasive tests.
method Defined a benchmark problem, evaluated different prediction algorithms, and investigated missing value imputation methods.
result Promising prediction results show the potential of machine learning in ICU care.
Study predicts blood glucose levels from infrequent measurements.
problem Predicting blood glucose levels from infrequent measurements.
method Proposed post-prediction methods to improve accuracy of machine learning models.
result Post-prediction methods marginally improve blood glucose prediction accuracy.
Town hall discusses AI's impact on statistics, culture, and training.
problem Adapting statistics to AI advancements and infrastructure.
method Open panel discussion and audience Q&A.
result Candid perspectives on evolving statistical practices.
Turing test relevance questioned in age of AI.
problem Relevance of Turing test in AI age.
method Examining cultural and technological context.
result Turing test may not fully capture AI capabilities.
Develops a tool to identify abnormal blood smear results based on CBC tests.
problem Manual review of blood smears by technologists is time-consuming and inconsistent.
method Cost-sensitive Lasso-penalized additive logistic regression combined with stability selection.
result The tool correctly identifies true cutoff values for abnormal smear results.
Study predicts blood pressure response to fluid bolus therapy with high accuracy.
problem Predicting successful response to fluid bolus therapy in hypotensive ICU patients.
method Used attention-based LSTM and GRU neural networks on a large ICU database.
result Stacked LSTM with attention mechanism achieved highest accuracy of 0.852.
Mathematical model describes how red blood cells return to equilibrium.
problem How red blood cells regain equilibrium after deformation.
method Gradient flow of the Canham-Helfrich functional, proving global existence and convergence for spheres and axisymmetric tori.
result Global existence and convergence of smooth solutions for spheres and axisymmetric tori under specific energy conditions.
Weak supervision challenges black-box models, suggesting fusion of modeling cultures.
problem Challenges of strong supervision in achieving accurate predictions.
method Integrating data modeling into algorithmic modeling for weak supervision.
result Integration of data modeling culture improves model stability and accuracy.
New method classifies reticulocytes from red blood cells without labels.
problem Classifying reticulocytes from mature red blood cells without fluorescent labels.
method Unsupervised machine learning on morpho-rheological markers.
result Promising results in classifying reticulocytes from mature red blood cells.
Deep multi-output models predict blood glucose trajectories more accurately.
problem Accurately predicting blood glucose levels over multiple time steps.
method Proposed multi-output deep architectures for multi-step forecasting.
result Improved performance compared to existing methods (4.87 vs. 5.31 APE).
New framework learns blood sample MTS representations with missing data.
problem Missing data in clinical time series.
method Combines autoencoder with TCK kernel for missing data.
result Improved classification of blood samples with missing data.
Study introduces KorFinMTEB for Korean financial texts, revealing model limitations.
problem Limited evaluation benchmarks for low-resource domains, especially Korean.
method Developed KorFinMTEB, a tailored benchmark for Korean financial texts.
result Models perform better on translated benchmarks than on domain-specific ones.
Detecting aggressive cancer tumors using ctDNA dynamics from few blood samples.
problem Early multi-cancer detection using circulating tumor DNA (ctDNA) levels.
method Combines continuous time Markov modelling and Signature theory for efficient testing procedures.
result Correctly addresses the challenge of data scarcity in cancer monitoring.
Nonparametric modeling of the composite effect of multiple nutrients on blood glucose dynamicsstat.ML
New method estimates effects of multiple nutrients on blood glucose.
problem Estimating physiological response to multiple nutrient treatments.
method Convolution-based multi-output Gaussian process model.
result Improved prediction accuracy and better interpretation of individual nutrient effects.
Improving Malaria Parasite Detection from Red Blood Cell using Deep Convolutional Neural Networkseess.IV
Deep neural network improves malaria detection from red blood cells.
problem Improving malaria detection from red blood cell images.
method End-to-end deep learning approach using convolutional neural networks.
result Best model achieves 97.77% accuracy.
Unsupervised method detects surgical site infections from blood samples.
problem Detecting surgical site infections from blood samples without labeled data.
method Powerful kernels for multivariate time series with missing data handling.
result Framework shows superior performance compared to baselines.
Machine learning models predict hematologic diseases with high accuracy.
problem Accurate medical diagnosis of hematologic diseases.
method Built two machine learning models using blood test results.
result Models achieved high prediction accuracy (0.88 and 0.86) for five diseases, and 0.59 and 0.57 for the most probable disease.
Novel neural network improves retinal blood vessel segmentation accuracy.
problem Lack of effective low-level and high-level features in existing approaches.
method Proposes a convolutional neural network using atrous convolution for multi-scale features.
result Significantly outperforms existing approaches in accuracy and speed.
TIMELY improves consistency in labeling blood cell images.
problem Inconsistent labeling of blood cells in microscopy images leads to unreliable diagnoses.
method TIMELY combines pseudotime inference and hidden Markov trees to correct labeling mistakes.
result TIMELY outperforms baseline methods in identifying and correcting inconsistent labels.
Deep RL improves blood glucose control for T1D patients.
problem Managing blood glucose levels for people with type 1 diabetes.
method Developed deep reinforcement learning techniques for automated blood glucose control.
result Deep RL approach outperforms baseline control algorithms, reducing glycemic risk and hypoglycemia.
Computational Drug Repositioning (CDR) is the task of discovering potential new indications for existing drugs by mining large-scale heterogeneous drug-related data sources. Leveraging the patient-level temporal ordering information between numeric physiological measurements and various drug prescriptions provided in E…
AI tool automates blood segmentation from head CT scans after SAH.
problem Accurate volumetric assessment of SAH patients for clinical and prognostic implications.
method Transformer-based Swin UNETR architecture for noncontrast CT scans.
result High accuracy and robust performance across internal and external validation cohorts.
Machine learning models detect COVID-19 from routine blood tests.
problem Separating COVID-19 from other viral pneumonias using blood tests.
method Employed random forests and support vector machines on blood data.
result SVM-based classifier achieves 84% accuracy in detecting COVID-19.
Deep learning predicts hypoxemia from SpO2 data better than anesthesiologists.
problem Improving hypoxemia prediction for better patient outcomes.
method Trained deep learning model on SpO2 data to predict hypoxemia.
result Deep learning model outperforms anesthesiologists in predicting hypoxemia.
Study analyzes how blood pressure impacts cardiac health.
problem Impact of blood pressure on cardiac function.
method Combines deep learning and variational autoencoder for interpretable biomarkers.
result Identifies key factors and patterns of cardiac adaptation.