Summary of What Is Hiding in Medicine’s Dark Matter? Learning with Missing Data in Medical Practices, by Neslihan Suzen et al.
What is Hiding in Medicine’s Dark Matter? Learning with Missing Data in Medical Practices
by Neslihan Suzen, Evgeny M. Mirkes, Damian Roland, Jeremy Levesley, Alexander N. Gorban, Tim J. Coats
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Information Theory (cs.IT)
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 Machine learning educators can use this abstract to explore new approaches for understanding and handling missing data in electronic patient records (EPRs). The study focuses on statistical methods for interpreting missing data and machine learning-based clinical data imputation. Researchers used a single centre’s paediatric emergency data and the UK’s largest clinical audit for traumatic injury database (TARN) to demonstrate that missing data are non-random and linked to health care professional practice patterns. They applied Singular Value Decomposition (SVD) and k-Nearest Neighbour (kNN) based imputation methods, concluding that the 1NN imputer is the best approach. This highlights the importance of understanding and handling missing data in clinical analysis and decision-making. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning experts want to understand how to deal with missing data in electronic patient records. One way is by using special math techniques and machine learning algorithms. Researchers studied a big database of kids who went to the emergency room and another database about injuries. They found that missing data isn’t random, but connected to how doctors work. They tested different methods to fill in the gaps, and found one method was the best. This is important because we need accurate data to make good decisions. |
Keywords
* Artificial intelligence * Machine learning
