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). These three components were built using transformer models with slightly different architecture and input text engineering. A zero-shot learning solution for CC was also explored. RESULTS: Our best performance systems achieved micro-average F1 scores of 0.973, 0.911, and 0.909 for the NER, EC, and CC, respectively. CONCLUSION: In this study, we implemented a deep learning-based NLP system and demonstrated that our approach of (1) utilizing special tokens helps our model to distinguish multiple medications mentions in the same context; (2) aggregating multiple events of a single medication into multiple labels improves our model's performance.

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. Methods: Participants were given up to 8 CP-units depending on their condition upon entry into the study and their response. Results: 102 out of 117 participants were given CP. The earlier a participant received CP corelated with survival (p = 0.0004). The number of CP-units given, throughout all the clinical severities, was not significant with outcomes, p = 0.3947. A higher number of CP-units given to the severe/critical participants (without biological immunosuppressants or restrictive lung disease) did correlate with survival p = 0.0116 (2.8 vs. 2 units). Lower platelets on admission corelated with mortality. Platelet levels increase correlated with CP infusions p < 0.0001. Conclusion: This study supports the serial use of CP of unknown strength based on clinical response for those infected with COVID-19. The use of 3-4 units of CP was found to be statistically significant for survival for severe and critical participants without restrictive lung disease and chronic biological immunosuppression. Increased platelet levels after CP infusions supports that CP is promoting overall health regardless of outcomes.

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. This involved the calculation of 1-hexene : 1-octene transition-state selectivity for 105 (P,N) ligands and the harvesting of 14 descriptors, which were then used to build a random forest regression model. This model showed the emergence of several key design features, such as Cr-N distance, Cr-α distance, and Cr distance out of pocket, which were then used to rapidly design a new generation of Cr(P,N) catalyst ligands that are predicted to give >95% selectivity for 1-octene.