Summary of Handling Missing Values in Clinical Machine Learning: Insights From An Expert Study, by Lena Stempfle et al.
Handling missing values in clinical machine learning: Insights from an expert study
by Lena Stempfle, Arthur James, Julie Josse, Tobias Gauss, Fredrik D. Johansson
First submitted to arxiv on: 14 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a study on inherently interpretable machine learning (IML) models in clinical decision-making, focusing on scenarios where features contain missing values. The authors surveyed 55 clinicians from French trauma centers and tested three IML models for predicting hemorrhagic shock with missing values. They found that clinicians prefer methods that natively handle missing values, rather than traditional imputation techniques. This highlights the need to integrate clinical reasoning into future IML models to improve human-computer interaction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study explores how machine learning models can be used in hospitals to make decisions about patient care. The problem is that sometimes data might be missing or incomplete, which makes it hard for doctors to use these models. The researchers asked 55 doctors from French hospitals what they thought of three different machine learning models and how they liked using them to predict whether a patient was bleeding badly. What they found out was surprising – doctors don’t like traditional methods that fill in the missing data. Instead, they prefer using methods that can handle missing data naturally. This shows that we need to make sure our machine learning models work with doctors’ instincts and experience. |
Keywords
* Artificial intelligence * Machine learning
