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Found 5 papers, 2 papers with code
Spectral structure learning for clinical time series
We develop and evaluate a structure learning algorithm for clinical time series.
Facilitating phenotyping from clinical texts: the medkit library
In addition to the core of the library, we share the operations and pipelines we already developed and invite the phenotyping community for their reuse and enrichment.
Learning the grammar of drug prescription: recurrent neural network grammars for medication information extraction in clinical texts
Similarly, we evaluated seq-RNNG, a hybrid RNNG model that takes as extra-input the output of the BiLSTMs for entities and events.
Terminologies augmented recurrent neural network model for clinical named entity recognition
On APcNER corpus, the micro-average F-measure of the hybrid system on the 5 entities was 69. 5% in exact match, and 84. 1% in non-exact match.
Hybrid Approaches for our Participation to the n2c2 Challenge on Cohort Selection for Clinical Trials
Materials and Methods: The first method is a weakly supervised method using an unlabeled corpus (MIMIC) to build a silver standard, by producing semi-automatically a small and very precise set of rules to detect some samples of positive and negative patients.
