Remove 2017 Remove Natural Language Processing Remove Support Vector Machines
article thumbnail

NLP-Powered Data Extraction for SLRs and Meta-Analyses

Towards AI

Natural Language Processing Getting desirable data out of published reports and clinical trials and into systematic literature reviews (SLRs) — a process known as data extraction — is just one of a series of incredibly time-consuming, repetitive, and potentially error-prone steps involved in creating SLRs and meta-analyses.

article thumbnail

From Rulesets to Transformers: A Journey Through the Evolution of SOTA in NLP

Mlearning.ai

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. Evolution of NLP Models To understand the full impact of the above evolutionary process.

article thumbnail

Computer Vision and Deep Learning for Healthcare

PyImageSearch

In the future, using large datasets and machine learning may predict optimal locations to edit DNA to alleviate suboptimal gene editing outcomes, enabling researchers to focus efforts on genes that are less likely to be at risk to patients. AI may also improve gene editing accuracy (a method of altering DNA at the cellular or organism level).