A deep learning approach for medication disposition and corresponding attributes extraction.

Objective: This article summarizes our approach to extracting medication and corresponding attributes from clinical notes, which is the focus of track 1 of the 2022 National Natural Language Processing (NLP) Clinical Challenges(n2c2) shared task.

Methods: The dataset was prepared using Contextualized Medication Event Dataset (CMED), including 500 notes from 296 patients. Our system consisted of three components: medication named entity recognition (NER), event classification (EC), and context classification (CC).

Serial Convalescent Plasma Infusions for the Initial COVID-19 Infections in the Appalachian Region of West Virginia.

Purpose: The rapid spread of SARS-CoV-2, the virus that is responsible for causing COVID-19, has presented the medical community with another example of when convalescent plasma (CP) is still used today. The ability to standardize CP at the onset of a pandemic is unlikely to exist in a reliable and uniformly reproducible way. We hypothesized that CP of unknown strength given in a serial manner will promote health and reduce mortality in those inflicted with COVID-19.

Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization.

The use of data science tools to provide the emergence of non-trivial chemical features for catalyst design is an important goal in catalysis science. Additionally, there is currently no general strategy for computational homogeneous, molecular catalyst design. Here, we report the unique combination of an experimentally verified DFT-transition-state model with a random forest machine learning model in a campaign to design new molecular Cr phosphine imine (Cr(P,N)) catalysts for selective ethylene oligomerization, specifically to increase 1-octene selectivity.