Summary of The Oxmat Dataset: a Multimodal Resource For the Development Of Ai-driven Technologies in Maternal and Newborn Child Health, by M. Jaleed Khan et al.
The OxMat dataset: a multimodal resource for the development of AI-driven technologies in maternal and newborn child health
by M. Jaleed Khan, Ioana Duta, Beth Albert, William Cooke, Manu Vatish, Gabriel Davis Jones
First submitted to arxiv on: 11 Apr 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 Oxford Maternity (OxMat) dataset is introduced as the world’s largest curated dataset of cardiotocography (CTG) recordings, featuring raw time series CTG data and extensive clinical data for both mothers and babies. The dataset addresses the critical gap in women’s health data by providing over 177,211 unique CTG recordings from 51,036 pregnancies, carefully curated and reviewed since 1991. It also comprises over 200 antepartum, intrapartum, and postpartum clinical variables, ensuring near-complete data for crucial outcomes such as stillbirth and acidaemia. The dataset’s focus on the antepartum period allows for early detection of at-risk fetuses, which can significantly improve health outcomes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new dataset that can help with fetal monitoring in obstetric care. It’s like a big book of information about women’s pregnancies and babies’ heartbeats. The book has lots of data from over 50,000 pregnancies, which is really helpful for machines learning how to predict when something might go wrong. This data is special because it looks at what happens before the baby is born, which is an important time for checking if everything is okay. |
Keywords
* Artificial intelligence * Time series
