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Found 7 papers, 1 papers with code
Multi-task Learning via Adaptation to Similar Tasks for Mortality Prediction of Diverse Rare Diseases
Mortality prediction of diverse rare diseases using electronic health record (EHR) data is a crucial task for intelligent healthcare.
Predictive Multi-level Patient Representations from Electronic Health Records
Most related studies transform EHR data of a patient into a sequence of clinical events in temporal order and then use sequential models to learn patient representations for outcome prediction.
Early Prediction of Sepsis From Clinical Datavia Heterogeneous Event Aggregation
Rather than directly applying the LSTM model to the event sequences, our proposed model firstly aggregates heterogeneous clinical events in a short period and then captures temporal interactions of the aggregated representations with LSTM.
Learning Hierarchical Representations of Electronic Health Records for Clinical Outcome Prediction
Our model learns hierarchical representationsof event sequences, to adaptively distinguish between short-range and long-range events, and accurately capture coretemporal dependencies.
Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction
One important application is clinical endpoint prediction, which aims to predict whether a disease, a symptom or an abnormal lab test will happen in the future according to patients' history records.
Syntax Aware LSTM model for Semantic Role Labeling
In Semantic Role Labeling (SRL) task, the tree structured dependency relation is rich in syntax information, but it is not well handled by existing models.
Syntax Aware LSTM Model for Chinese Semantic Role Labeling
As for semantic role labeling (SRL) task, when it comes to utilizing parsing information, both traditional methods and recent recurrent neural network (RNN) based methods use the feature engineering way.
