Summary of Equitable Length Of Stay Prediction For Patients with Learning Disabilities and Multiple Long-term Conditions Using Machine Learning, by Emeka Abakasanga et al.
Equitable Length of Stay Prediction for Patients with Learning Disabilities and Multiple Long-term Conditions Using Machine Learning
by Emeka Abakasanga, Rania Kousovista, Georgina Cosma, Ashley Akbari, Francesco Zaccardi, Navjot Kaur, Danielle Fitt, Gyuchan Thomas Jun, Reza Kiani, Satheesh Gangadharan
First submitted to arxiv on: 3 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Applications (stat.AP)
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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 A new study analyzing electronic health records (EHRs) in Wales aims to improve predictive modeling for patients with learning disabilities and long-term conditions. By applying machine learning models, including random forest (RF), researchers can better estimate hospital stay lengths, which is crucial for healthcare resource allocation and patient care. The RF model achieved high accuracy rates, but performance varied across ethnic groups, highlighting the need for bias mitigation techniques. Two algorithms were applied to minimize discrepancies: threshold optimization and the reductions algorithm using an exponentiated gradient. Threshold optimization outperformed the other method, demonstrating its potential in addressing data imbalances. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary People with learning disabilities have a higher risk of premature deaths than others. Researchers looked at hospital records from Wales to see how machine learning models could help predict how long patients would stay in the hospital. They found that a type of model called random forest worked well, but it was not perfect and performed differently for different groups of people. The researchers then tried two ways to make the model work better for everyone: threshold optimization and another method. The first one worked better, which is important because hospitals need to plan ahead to take good care of patients. |
Keywords
» Artificial intelligence » Machine learning » Optimization » Random forest
