Search Results for author:
Found 7 papers, 2 papers with code
An Emotion Recognition Embedded System using a Lightweight Deep Learning Model
The emotional states are recognized for every three‑second epoch of received signals on the embedded system, which can be applied in real‑time usage in practice.
Supervised Learning in the Presence of Noise: Application in ICD-10 Code Classification
We first identify ICD-10 codes that human coders tend to misuse or confuse, based on the codes' locations in the ICD-10 hierarchy, the types of the codes, and baseline classifier's prediction behaviors; we then develop a novel training strategy that accounts for such noise.
From Extreme Multi-label to Multi-class: A Hierarchical Approach for Automated ICD-10 Coding Using Phrase-level Attention
With our proposed approach, interpretability is achieved not through implicitly learned attention scores but by attributing each prediction to a particular sentence and words selected by human coders.
An Ensemble Approach for Automatic Structuring of Radiology Reports
We present an ensemble method that consolidates the predictions of three models, capturing various attributes of textual information for automatic labeling of sentences with section labels.
Neural Token Representations and Negation and Speculation Scope Detection in Biomedical and General Domain Text
Since the introduction of context-aware token representation techniques such as Embeddings from Language Models (ELMo) and Bidirectional Encoder Representations from Transformers (BERT), there has been numerous reports on improved performance on a variety of natural language tasks.
Clinical Concept Extraction with Contextual Word Embedding
Next, a bidirectional LSTM-CRF model is trained for clinical concept extraction using the contextual word embedding model.
Towards radiologist-level cancer risk assessment in CT lung screening using deep learning
Importance: Lung cancer is the leading cause of cancer mortality in the US, responsible for more deaths than breast, prostate, colon and pancreas cancer combined and it has been recently demonstrated that low-dose computed tomography (CT) screening of the chest can significantly reduce this death rate.
