Search Results for author:
Found 21 papers, 14 papers with code
Attention Networks for Augmenting Clinical Text with Support Sets for Diagnosis Prediction
Furthermore, we find that support sets drastically improve the performance for pregnancy- and gynecology-related diagnoses up to 32. 9% points compared to the baseline.
Comply: Learning Sentences with Complex Weights inspired by Fruit Fly Olfaction
Biologically inspired neural networks offer alternative avenues to model data distributions.
LongHealth: A Question Answering Benchmark with Long Clinical Documents
However, all models struggled significantly in tasks requiring the identification of missing information, highlighting a critical area for improvement in clinical data interpretation.
MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data
As large language models (LLMs) like OpenAI's GPT series continue to make strides, we witness the emergence of artificial intelligence applications in an ever-expanding range of fields.
MEDBERT.de: A Comprehensive German BERT Model for the Medical Domain
This paper presents medBERTde, a pre-trained German BERT model specifically designed for the German medical domain.
This Patient Looks Like That Patient: Prototypical Networks for Interpretable Diagnosis Prediction from Clinical Text
The use of deep neural models for diagnosis prediction from clinical text has shown promising results.
Cross-Lingual Knowledge Transfer for Clinical Phenotyping
Clinical phenotyping enables the automatic extraction of clinical conditions from patient records, which can be beneficial to doctors and clinics worldwide.
What Do You See in this Patient? Behavioral Testing of Clinical NLP Models
We thus introduce an extendable testing framework that evaluates the behavior of clinical outcome models regarding changes of the input.
KIMERA: Injecting Domain Knowledge into Vacant Transformer Heads
We apply KIMERA to BERT-base to evaluate the extent of the domain transfer and also improve on the already strong results of BioBERT in the clinical domain.
Self-supervised Answer Retrieval on Clinical Notes
Retrieving answer passages from long documents is a complex task requiring semantic understanding of both discourse and document context.
Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration
Outcome prediction from clinical text can prevent doctors from overlooking possible risks and help hospitals to plan capacities.
Ranked #1 on
Length-of-Stay prediction
on Clinical Admission Notes from MIMIC-III
(using extra training data)
VisBERT: Hidden-State Visualizations for Transformers
At the same time, they are difficult to incorporate into the large, black-box models that achieve state-of-the-art results in a multitude of NLP tasks.
Learning Contextualized Document Representations for Healthcare Answer Retrieval
Our model leverages a dual encoder architecture with hierarchical LSTM layers and multi-task training to encode the position of clinical entities and aspects alongside the document discourse.
From Symmetry to Asymmetry: Generalizing TSP Approximations by Parametrization
Our generalization of the tree doubling algorithm gives a parameterized 3-approximation, where the parameter is the number of asymmetric edges in a given minimum spanning arborescence.
Data Structures and Algorithms
How Does BERT Answer Questions? A Layer-Wise Analysis of Transformer Representations
In order to better understand BERT and other Transformer-based models, we present a layer-wise analysis of BERT's hidden states.
SECTOR: A Neural Model for Coherent Topic Segmentation and Classification
From our extensive evaluation of 20 architectures, we report a highest score of 71. 6% F1 for the segmentation and classification of 30 topics from the English city domain, scored by our SECTOR LSTM model with bloom filter embeddings and bidirectional segmentation.
Challenges for Toxic Comment Classification: An In-Depth Error Analysis
Toxic comment classification has become an active research field with many recently proposed approaches.
Crowd-Labeling Fashion Reviews with Quality Control
We present a new methodology for high-quality labeling in the fashion domain with crowd workers instead of experts.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
IDEL: In-Database Entity Linking with Neural Embeddings
We present a novel architecture, In-Database Entity Linking (IDEL), in which we integrate the analytics-optimized RDBMS MonetDB with neural text mining abilities.
Analysing Errors of Open Information Extraction Systems
We report results on benchmarking Open Information Extraction (OIE) systems using RelVis, a toolkit for benchmarking Open Information Extraction systems.
Robust Named Entity Recognition in Idiosyncratic Domains
We propose a generic and robust approach for high-recall named entity recognition.
