Deep learning in clinical natural language processing: a methodical review
Objective This article methodically reviews the literature on deep learning (DL) for natural
language processing (NLP) in the clinical domain, providing quantitative analysis to answer …
language processing (NLP) in the clinical domain, providing quantitative analysis to answer …
[HTML][HTML] A frame semantic overview of NLP-based information extraction for cancer-related EHR notes
Objective There is a lot of information about cancer in Electronic Health Record (EHR) notes
that can be useful for biomedical research provided natural language processing (NLP) …
that can be useful for biomedical research provided natural language processing (NLP) …
[HTML][HTML] Prediction of breast cancer using machine learning approaches
Background: Breast cancer is considered one of the most common cancers in women
caused by various clinical, lifestyle, social, and economic factors. Machine learning has the …
caused by various clinical, lifestyle, social, and economic factors. Machine learning has the …
An imageomics and multi-network based deep learning model for risk assessment of liver transplantation for hepatocellular cancer
Introduction Liver transplantation (LT) is an effective treatment for hepatocellular carcinoma
(HCC), the most common type of primary liver cancer. Patients with small HCC (< 5 cm) are …
(HCC), the most common type of primary liver cancer. Patients with small HCC (< 5 cm) are …
Two-stage CNNs for computerized BI-RADS categorization in breast ultrasound images
Abstract Background Quantizing the Breast Imaging Reporting and Data System (BI-RADS)
criteria into different categories with the single ultrasound modality has always been a …
criteria into different categories with the single ultrasound modality has always been a …
The upside of being a digital pharma player
A Schuhmacher, A Gatto, M Hinder, M Kuss… - Drug discovery today, 2020 - Elsevier
Highlights•We have investigated the state of artificial intelligence in pharmaceutical
R&D.•We have outlined here a risk and reward perspective regarding digital R&D.•The …
R&D.•We have outlined here a risk and reward perspective regarding digital R&D.•The …
Recent advancement of machine learning and deep learning in the field of healthcare system
The healthcare sector has long been adapted primarily and significantly from scientific
advances. Nowadays, machine learning (ML, a subset of artificial intelligence) plays a vital …
advances. Nowadays, machine learning (ML, a subset of artificial intelligence) plays a vital …
Using BI-RADS stratifications as auxiliary information for breast masses classification in ultrasound images
J Xing, C Chen, Q Lu, X Cai, A Yu, Y Xu… - IEEE Journal of …, 2020 - ieeexplore.ieee.org
Breast Ultrasound (BUS) imaging has been recognized as an essential imaging modality for
breast masses classification in China. Current deep learning (DL) based solutions for BUS …
breast masses classification in China. Current deep learning (DL) based solutions for BUS …
A deep learning–based decision support tool for precision risk assessment of breast cancer
PURPOSE The Breast Imaging Reporting and Data System (BI-RADS) lexicon was
developed to standardize mammographic reporting to assess cancer risk and facilitate the …
developed to standardize mammographic reporting to assess cancer risk and facilitate the …
A scoping review of natural language processing of radiology reports in breast cancer
Various natural language processing (NLP) algorithms have been applied in the literature to
analyze radiology reports pertaining to the diagnosis and subsequent care of cancer …
analyze radiology reports pertaining to the diagnosis and subsequent care of cancer …