Predicting translational progress in biomedical research | AISC

Channel:
Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
Subscribers:
22,300
Published on ● Video Link: https://www.youtube.com/watch?v=FWNgUpi3GK0


Duration: 1:10:37
246 views
9

For slides and more information on the paper, visit https://aisc.ai.science/events/2019-12-11

Discussion lead: George Santangelo
Discussion facilitator(s): Ehsan Amjadian

Motivation:
Fundamental scientific advances can take decades to translate into improvements in human health. Shortening this interval would increase the rate at which scientific discoveries lead to successful treatment of human disease. One way to accomplish this would be to identify which advances in knowledge are most likely to translate into clinical research. Toward that end, we built a machine learning system that detects whether a paper is likely to be cited by a future clinical trial or guideline. Despite the noisiness of citation dynamics, as little as 2 years of postpublication data yield accurate predictions about a paper’s eventual citation by a clinical article (accuracy = 84%, F1 score = 0.56; compared to 19% accuracy by chance). We found that distinct knowledge flow trajectories are linked to papers that either succeed or fail to influence clinical research. Translational progress in biomedicine can therefore be assessed and predicted in real time based on information conveyed by the scientific community’s early reaction to a paper.



Other Videos By LLMs Explained - Aggregate Intellect - AI.SCIENCE


2020-02-12Visualizing and measuring the geometry of BERT | AISC
2020-02-11Attention is not not explanation + Character Eyes: Seeing Language through Character-Level Taggers |
2020-02-10Single Headed Attention RNN: Stop Thinking With Your Head | AISC
2020-01-27Identifying Big ML product opportunities inside Big organizations | AISC
2020-01-23Machine Learning in Cyber Security, Overview | AISC
2020-01-22BottleSum: Unsupervised & Self-supervised Sentence Summarization w/ Information Bottleneck Principle
2020-01-20A Hybrid GA-PSO Method for Evolving Architecture and Short Connections of Deep Convolutional Neural
2020-01-13Plug and Play Language Models: A Simple Approach to Controlled Text Generation | AISC
2020-01-08Overview of Modern Anomaly and Novelty Detection | AISC
2020-01-06Annotating Object Instances With a Polygon RNN | AISC
2019-12-11Predicting translational progress in biomedical research | AISC
2019-12-09AlphaStar explained: Grandmaster level in StarCraft II with multi-agent RL
2019-12-04How Can We Be So Dense? The Benefits of Using Highly Sparse Representations | AISC
2019-12-02[RoBERT & ToBERT] Hierarchical Transformers for Long Document Classification | AISC
2019-11-25[OpenAI] Solving Rubik's Cube with a Robot Hand | AISC
2019-11-18Top-K Off-Policy Correction for a REINFORCE Recommender System | AISC
2019-11-13Overview of Unsupervised & Semi-supervised learning | AISC
2019-11-11Building products for Continous Delivery in Machine Learning | AISC
2019-11-06[WeightWatcher] Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory
2019-11-04Defending Against Fake Neural News | AISC
2019-10-28[RecSys Challenge 2019 2nd Place] Robust Contextual Models for In-Session Personalization | AISC