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Found 11 papers, 4 papers with code
John_Snow_Labs@SMM4H’22: Social Media Mining for Health (#SMM4H) with Spark NLP
Social media has become a major source of information for healthcare professionals but due to the growing volume of data in unstructured format, analyzing these resources accurately has become a challenge.
Beyond Accuracy: Automated De-Identification of Large Real-World Clinical Text Datasets
Recent research advances achieve human-level accuracy for de-identifying free-text clinical notes on research datasets, but gaps remain in reproducing this in large real-world settings.
Saliency Can Be All You Need In Contrastive Self-Supervised Learning
We propose an augmentation policy for Contrastive Self-Supervised Learning (SSL) in the form of an already established Salient Image Segmentation technique entitled Global Contrast based Salient Region Detection.
Understanding COVID-19 News Coverage using Medical NLP
Being a global pandemic, the COVID-19 outbreak received global media attention.
Mining Adverse Drug Reactions from Unstructured Mediums at Scale
Adverse drug reactions / events (ADR/ADE) have a major impact on patient health and health care costs.
Deeper Clinical Document Understanding Using Relation Extraction
The surging amount of biomedical literature & digital clinical records presents a growing need for text mining techniques that can not only identify but also semantically relate entities in unstructured data.
Ranked #1 on
Relation Extraction
on 2018 n2c2 posology
The Unreasonable Effectiveness of the Final Batch Normalization Layer
Early-stage disease indications are rarely recorded in real-world domains, such as Agriculture and Healthcare, and yet, their accurate identification is critical in that point of time.
Spark NLP: Natural Language Understanding at Scale
Spark NLP is a Natural Language Processing (NLP) library built on top of Apache Spark ML.
Improving Clinical Document Understanding on COVID-19 Research with Spark NLP
Second, the text processing pipeline includes assertion status detection, to distinguish between clinical facts that are present, absent, conditional, or about someone other than the patient.
Ranked #1 on
Clinical Assertion Status Detection
on 2010 i2b2/VA
Improving Model Accuracy for Imbalanced Image Classification Tasks by Adding a Final Batch Normalization Layer: An Empirical Study
We empirically observe that the initial F1 test score jumps from 0. 29 to 0. 95 for the minority class upon adding a final Batch Normalization (BN) layer just before the output layer in VGG19.
Biomedical Named Entity Recognition at Scale
Named entity recognition (NER) is a widely applicable natural language processing task and building block of question answering, topic modeling, information retrieval, etc.
Ranked #1 on
Named Entity Recognition (NER)
on Species800
