Predicting Patient Outcomes
6 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Predicting Patient Outcomes
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Ward2ICU
Latest papers with no code
Interpreting Differentiable Latent States for Healthcare Time-series Data
Understanding the meaning of latent states is crucial for interpreting machine learning models, assuming they capture underlying patterns.
Mining Themes in Clinical Notes to Identify Phenotypes and to Predict Length of Stay in Patients admitted with Heart Failure
These clinical phenotypes usually have a probabilistic latent structure and hence, as there has been no previous work on identifying phenotypes in clinical notes of heart failure patients using a probabilistic framework and to predict length of stay of these patients using data-driven artificial intelligence-based methods, we apply natural language processing technique, topic modeling, to identify the themes present in diagnostic codes and in procedure reports of 1, 200 patients admitted for heart failure at the University of Illinois Hospital and Health Sciences System (UI Health).
Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review
Dozens of studies over the last few years have demonstrated that a variety of molecular biomarkers can be predicted from H&E alone using the advancements of deep learning: molecular alterations, genomic subtypes, protein biomarkers, and even the presence of viruses.
Prediction of Oral Food Challenge Outcomes via Ensemble Learning
The results of this analysis suggest that ensemble learning has the potential to predict OFC outcomes and reveal relevant clinical factors for further study.
Computer-aided diagnosis and prediction in brain disorders
Regarding prediction, i. e. estimation of the future 'condition' of the patient, we will zoom in on use cases such as predicting the disease course in multiple sclerosis and predicting patient outcomes after treatment in brain cancer.
Conformal Prediction with Temporal Quantile Adjustments
TQA adjusts the quantile to query in CP at each time $t$, accounting for both cross-sectional and longitudinal coverage in a theoretically-grounded manner.
Detecting Invasive Ductal Carcinoma with Semi-Supervised Conditional GANs
Invasive ductal carcinoma (IDC) comprises nearly 80% of all breast cancers.
Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression
Simulating images representative of neurodegenerative diseases is important for predicting patient outcomes and for validation of computational models of disease progression.
Learning Representations of Missing Data for Predicting Patient Outcomes
Extracting actionable insight from Electronic Health Records (EHRs) poses several challenges for traditional machine learning approaches.
Bridging Medical Data Inference to Achilles Tendon Rupture Rehabilitation
Imputing incomplete medical tests and predicting patient outcomes are crucial for guiding the decision making for therapy, such as after an Achilles Tendon Rupture (ATR).
