[HTML][HTML] Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: a review
DB Fogel - Contemporary clinical trials communications, 2018 - Elsevier
Clinical trials are time consuming, expensive, and often burdensome on patients. Clinical
trials can fail for many reasons. This survey reviews many of these reasons and offers …
trials can fail for many reasons. This survey reviews many of these reasons and offers …
Natural language processing in radiology: a systematic review
Radiological reporting has generated large quantities of digital content within the electronic
health record, which is potentially a valuable source of information for improving clinical care …
health record, which is potentially a valuable source of information for improving clinical care …
A roadmap for foundational research on artificial intelligence in medical imaging: from the 2018 NIH/RSNA/ACR/The Academy Workshop
Imaging research laboratories are rapidly creating machine learning systems that achieve
expert human performance using open-source methods and tools. These artificial …
expert human performance using open-source methods and tools. These artificial …
Optimizing the factual correctness of a summary: A study of summarizing radiology reports
Neural abstractive summarization models are able to generate summaries which have high
overlap with human references. However, existing models are not optimized for factual …
overlap with human references. However, existing models are not optimized for factual …
A review on the role of machine learning in enabling IoT based healthcare applications
HK Bharadwaj, A Agarwal, V Chamola… - IEEE …, 2021 - ieeexplore.ieee.org
The Internet of Things (IoT) is playing a vital role in the rapid automation of the healthcare
sector. The branch of IoT dedicated towards medical science is at times termed as …
sector. The branch of IoT dedicated towards medical science is at times termed as …
Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification
This paper explores cutting-edge deep learning methods for information extraction from
medical imaging free text reports at a multi-institutional scale and compares them to the state …
medical imaging free text reports at a multi-institutional scale and compares them to the state …
Next-generation phenotyping of electronic health records
G Hripcsak, DJ Albers - Journal of the American Medical …, 2013 - academic.oup.com
The national adoption of electronic health records (EHR) promises to make an
unprecedented amount of data available for clinical research, but the data are complex …
unprecedented amount of data available for clinical research, but the data are complex …
[HTML][HTML] Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives
In secondary analysis of electronic health records, a crucial task consists in correctly
identifying the patient cohort under investigation. In many cases, the most valuable and …
identifying the patient cohort under investigation. In many cases, the most valuable and …
Deep learning to classify radiology free-text reports
Purpose To evaluate the performance of a deep learning convolutional neural network
(CNN) model compared with a traditional natural language processing (NLP) model in …
(CNN) model compared with a traditional natural language processing (NLP) model in …
MedEx: a medication information extraction system for clinical narratives
Medication information is one of the most important types of clinical data in electronic
medical records. It is critical for healthcare safety and quality, as well as for clinical research …
medical records. It is critical for healthcare safety and quality, as well as for clinical research …