Summary of Mapping Patient Trajectories: Understanding and Visualizing Sepsis Prognostic Pathways From Patients Clinical Narratives, by Sudeshna Jana et al.
Mapping Patient Trajectories: Understanding and Visualizing Sepsis Prognostic Pathways from Patients Clinical Narratives
by Sudeshna Jana, Tirthankar Dasgupta, Lipika Dey
First submitted to arxiv on: 20 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes a systematic methodology for developing sepsis prognostic pathways from clinical notes in Electronic Health Records (EHRs). The approach leverages Natural Language Processing models, specifically SHAP, to explore comorbidities associated with sepsis and generate explanations of diverse patient subgroups. The extracted pathways provide valuable insights into the dynamic trajectories of sepsis severity over time, revealing patterns and pivotal factors influencing disease progression. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps doctors create personalized care plans for patients by analyzing their medical records. It uses special computer models to identify different patient groups based on their health conditions, and then shows how these conditions change over time. This information can help doctors make better decisions about treatment and improve patient outcomes. |
Keywords
* Artificial intelligence * Natural language processing
