Papers Predicting Patient Outcomes
“Predicting Patient Outcomes” 태그가 달린 논문 27편 · 필터 해제
JASPR: Joint Spatial Representation learning of histology and spatial genomics for improved virtual genomic screening and clinical prognostication
Recent studies have shown that spatial properties of tumors are critical for understanding disease biology and predicting patient outcomes. These spatial properties are increasingly uncovered through complementary modali…
Predicting Patient OutcomesRepresentation LearningEnhancing WSI-Based Survival Analysis with Report-Auxiliary Self-Distillation
Survival analysis based on Whole Slide Images (WSIs) is crucial for evaluating cancer prognosis, as they offer detailed microscopic information essential for predicting patient outcomes. However, traditional WSI-based su…
Predicting Patient OutcomesExplainable AI for Infection Prevention and Control: Modeling CPE Acquisition and Patient Outcomes in an Irish Hospital with Transformers
Carbapenemase-Producing Enterobacteriace poses a critical concern for infection prevention and control in hospitals. However, predictive modeling of previously highlighted CPE-associated risks such as readmission, mortal…
Predicting Patient OutcomesTime-Aware Attention for Enhanced Electronic Health Records Modeling
Electronic Health Records (EHR) contain valuable clinical information for predicting patient outcomes and guiding healthcare decisions. However, effectively modeling Electronic Health Records (EHRs) requires addressing d…
Predicting Patient OutcomesUncovering Neuroimaging Biomarkers of Brain Tumor Surgery with AI-Driven Methods
Brain tumor resection is a highly complex procedure with profound implications for survival and quality of life. Predicting patient outcomes is crucial to guide clinicians in balancing oncological control with preservati…
Predicting Patient OutcomesFeature EngineeringIntelligent Histology for Tumor Neurosurgery
The importance of rapid and accurate histologic analysis of surgical tissue in the operating room has been recognized for over a century. Our standard-of-care intraoperative pathology workflow is based on light microscop…
Predicting Patient OutcomesDynamical Label Augmentation and Calibration for Noisy Electronic Health Records
Medical research, particularly in predicting patient outcomes, heavily relies on medical time series data extracted from Electronic Health Records (EHR), which provide extensive information on patient histories. Despite …
Predicting Patient OutcomesPerformance of Large Language Models in Supporting Medical Diagnosis and Treatment
The integration of Large Language Models (LLMs) into healthcare holds significant potential to enhance diagnostic accuracy and support medical treatment planning. These AI-driven systems can analyze vast datasets, assist…
Decision MakingDiagnosticMedical DiagnosisPredicting Patient OutcomesContinually Evolved Multimodal Foundation Models for Cancer Prognosis
Cancer prognosis is a critical task that involves predicting patient outcomes and survival rates. To enhance prediction accuracy, previous studies have integrated diverse data modalities, such as clinical notes, medical …
Predicting Patient OutcomesPrognosisTADM: Temporally-Aware Diffusion Model for Neurodegenerative Progression on Brain MRI
Generating realistic images to accurately predict changes in the structure of brain MRI is a crucial tool for clinicians. Such applications help assess patients' outcomes and analyze how diseases progress at the individu…
Predicting Patient OutcomesSelf-Normalizing Foundation Model for Enhanced Multi-Omics Data Analysis in Oncology
Multi-omics research has enhanced our understanding of cancer heterogeneity and progression. Investigating molecular data through multi-omics approaches is crucial for unraveling the complex biological mechanisms underly…
Predicting Patient OutcomesInterpreting Differentiable Latent States for Healthcare Time-series Data
Machine learning enables extracting clinical insights from large temporal datasets. The applications of such machine learning models include identifying disease patterns and predicting patient outcomes. However, limited …
Predicting Patient OutcomesTime SeriesWeakly Supervised AI for Efficient Analysis of 3D Pathology Samples
Human tissue and its constituent cells form a microenvironment that is fundamentally three-dimensional (3D). However, the standard-of-care in pathologic diagnosis involves selecting a few two-dimensional (2D) sections fo…
MambaMultiple Instance LearningPredicting Patient OutcomesPrognosis+1Mining Themes in Clinical Notes to Identify Phenotypes and to Predict Length of Stay in Patients admitted with Heart Failure
Heart failure is a syndrome which occurs when the heart is not able to pump blood and oxygen to support other organs in the body. Identifying the underlying themes in the diagnostic codes and procedure reports of patient…
DiagnosticPredicting Patient OutcomesDeep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review
Molecular and genomic properties are critical in selecting cancer treatments to target individual tumors, particularly for immunotherapy. However, the methods to assess such properties are expensive, time-consuming, and …
Predicting Patient Outcomeswhole slide imagesPrediction of Oral Food Challenge Outcomes via Ensemble Learning
Oral Food Challenges (OFCs) are essential to accurately diagnosing food allergy due to the limitations of existing clinical testing. However, some patients are hesitant to undergo OFCs, while those willing suffer from li…
Ensemble LearningPredicting Patient OutcomesSpecificityComputer-aided diagnosis and prediction in brain disorders
Computer-aided methods have shown added value for diagnosing and predicting brain disorders and can thus support decision making in clinical care and treatment planning. This chapter will provide insight into the type of…
BenchmarkingDecision MakingPredicting Patient OutcomesPredictionConformal Prediction with Temporal Quantile Adjustments
We develop Temporal Quantile Adjustment (TQA), a general method to construct efficient and valid prediction intervals (PIs) for regression on cross-sectional time series data. Such data is common in many domains, includi…
Conformal PredictionEconometricsPredicting Patient OutcomesPrediction+6Predicting Patient Outcomes with Graph Representation Learning
Recent work on predicting patient outcomes in the Intensive Care Unit (ICU) has focused heavily on the physiological time series data, largely ignoring sparse data such as diagnoses and medications. When they are include…
Graph Representation LearningLength-of-Stay predictionPredicting Patient OutcomesRepresentation Learning+2Temporal Pointwise Convolutional Networks for Length of Stay Prediction in the Intensive Care Unit
The pressure of ever-increasing patient demand and budget restrictions make hospital bed management a daily challenge for clinical staff. Most critical is the efficient allocation of resource-heavy Intensive Care Unit (I…
Length-of-Stay predictionManagementMortality PredictionPredicting Patient Outcomes+1