medical

Predicting Patient Outcomes

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in predicting-patient-outcomes

Trend

Dataset

Best Model

Actions

eICU Collaborative Research Database

Libraries

i

Use these libraries to find predicting-patient-outcomes models and implementations

Datasets

Ward2ICU

Subtasks

No subtasks available.

Most implemented papers

Ward2ICU: A Vital Signs Dataset of Inpatients from the General Ward

Daniel de Souza Severo, Fl'avio Amaro, Estevam Hruschka, André Soares de Moura Costa•Tue Oct 01 2019

A proxy dataset of vital signs with class labels indicating patient transitions from the ward to intensive care units called Ward2ICU is presented, and a solution for a special case through class label balancing is proposed.

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Attentive State-Space Modeling of Disease Progression

A. Alaa, M. Schaar•Mon Dec 31 2018

The attentive state-space model is developed, a deep probabilistic model that learns accurate and interpretable structured representations for disease trajectories that demonstrates superior predictive accuracy and provides insights into the progression of chronic disease.

118 0

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Temporal pointwise convolutional networks for length of stay prediction in the intensive care unit

Stephanie L. Hyland, Emma Rocheteau, Pietro Liò•Fri Jul 17 2020

A new deep learning model based on the combination of temporal convolution and pointwise (1x1) convolution, to solve the length of stay prediction task on the eICU and MIMIC-IV critical care datasets is proposed.

73 0

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DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network

Y. Kluger, Jared Katzman, Uri Shaham, A. Cloninger, Jonathan Bates, Tingting Jiang•Wed Jun 01 2016

The predictive and modeling capabilities of DeepSurv will enable medical researchers to use deep neural networks as a tool in their exploration, understanding, and prediction of the effects of a patient’s characteristics on their risk of failure.

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Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples

Drew F. K. Williamson, Faisal Mahmood, Bowen Chen, Guillaume Jaume, Andrew H. Song, Mane Williams, Andrew Zhang, R. Serafin, Jonathan T. C. Liu, Alexander S. Baras, A. Parwani•Wed Jul 26 2023

Modality-Agnostic Multiple instance learning for volumetric Block Analysis (MAMBA) is presented, a deep-learning-based platform for processing 3D tissue images from diverse imaging modalities and predicting patient outcomes and can help to reveal novel 3D morphological biomarkers for prognosis and therapeutic response.

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Predicting Patient Outcomes with Graph Representation Learning

Petar Velickovic, N. Lane, Emma Rocheteau, Pietro Liò, Catherine Tong•Sun Jan 10 2021

This work proposes LSTM-GNN for patient outcome prediction tasks: a hybrid model combining Long Short-Term Memory networks (LSTMs) for extracting temporal features and Graph Neural Networks (GNNs) for extracts the patient neighbourhood information.

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Paper Graph

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