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Automated Multi-Task Learning for Joint Disease Prediction on Electronic Health Records (2403.04086v3)

Published 6 Mar 2024 in cs.LG

Abstract: In the realm of big data and digital healthcare, Electronic Health Records (EHR) have become a rich source of information with the potential to improve patient care and medical research. In recent years, machine learning models have proliferated for analyzing EHR data to predict patients future health conditions. Among them, some studies advocate for multi-task learning (MTL) to jointly predict multiple target diseases for improving the prediction performance over single task learning. Nevertheless, current MTL frameworks for EHR data have significant limitations due to their heavy reliance on human experts to identify task groups for joint training and design model architectures. To reduce human intervention and improve the framework design, we propose an automated approach named AutoDP, which can search for the optimal configuration of task grouping and architectures simultaneously. To tackle the vast joint search space encompassing task combinations and architectures, we employ surrogate model-based optimization, enabling us to efficiently discover the optimal solution. Experimental results on real-world EHR data demonstrate the efficacy of the proposed AutoDP framework. It achieves significant performance improvements over both hand-crafted and automated state-of-the-art methods, also maintains a feasible search cost at the same time. Source code can be found via the link: \url{https://github.com/SH-Src/AutoDP}.

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References (35)
  1. Deep elastic networks with model selection for multi-task learning. In Proceedings of the IEEE/CVF international conference on computer vision. 6529–6538.
  2. Stochastic filter groups for multi-task cnns: Learning specialist and generalist convolution kernels. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 1385–1394.
  3. Doctor ai: Predicting clinical events via recurrent neural networks. In Machine learning for healthcare conference. PMLR, 301–318.
  4. Retain: An interpretable predictive model for healthcare using reverse time attention mechanism. In Advances in Neural Information Processing Systems. 3504–3512.
  5. Neural architecture search: A survey. The Journal of Machine Learning Research 20, 1 (2019), 1997–2017.
  6. Efficiently identifying task groupings for multi-task learning. Advances in Neural Information Processing Systems 34 (2021), 27503–27516.
  7. Mtl-nas: Task-agnostic neural architecture search towards general-purpose multi-task learning. In Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition. 11543–11552.
  8. Learning to branch for multi-task learning. In International conference on machine learning. PMLR, 3854–3863.
  9. Multitask learning and benchmarking with clinical time series data. Scientific data 6, 1 (2019), 96.
  10. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770–778.
  11. AutoML: A survey of the state-of-the-art. Knowledge-Based Systems 212 (2021), 106622.
  12. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735–1780.
  13. Clinicalbert: Modeling clinical notes and predicting hospital readmission. arXiv preprint arXiv:1904.05342 (2019).
  14. Mimic-iv. PhysioNet. Available online at: https://physionet. org/content/mimiciv/1.0/(accessed August 23, 2021) (2020).
  15. MIMIC-III, a freely accessible critical care database. Scientific data 3, 1 (2016), 1–9.
  16. DARTS: Differentiable Architecture Search. In International Conference on Learning Representations.
  17. A survey on surrogate-assisted efficient neural architecture search. arXiv preprint arXiv:2206.01520 (2022).
  18. Dipole: Diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. 1903–1911.
  19. Advances in Mining Heterogeneous Healthcare Data. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 4050–4051.
  20. AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration. In AAAI.
  21. Multi-task prediction of disease onsets from longitudinal laboratory tests. In Machine learning for healthcare conference. PMLR, 73–100.
  22. Regularized evolution for image classifier architecture search. In Proceedings of the aaai conference on artificial intelligence, Vol. 33. 4780–4789.
  23. Efficient and effective multi-task grouping via meta learning on task combinations. Advances in Neural Information Processing Systems 35 (2022), 37647–37659.
  24. Which tasks should be learned together in multi-task learning?. In International Conference on Machine Learning. PMLR, 9120–9132.
  25. Adashare: Learning what to share for efficient deep multi-task learning. Advances in Neural Information Processing Systems 33 (2020), 8728–8740.
  26. A multi-task framework for monitoring health conditions via attention-based recurrent neural networks. In AMIA annual symposium proceedings, Vol. 2017. American Medical Informatics Association, 1665.
  27. Attention is all you need. In Advances in neural information processing systems. 5998–6008.
  28. Exploring joint disease risk prediction. In AMIA annual symposium proceedings, Vol. 2014. American Medical Informatics Association, 1180.
  29. Automated machine learning: Review of the state-of-the-art and opportunities for healthcare. Artificial intelligence in medicine 104 (2020), 101822.
  30. Multiple MACE risk prediction using multi-task recurrent neural network with attention. In 2019 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 1–2.
  31. Automtl: A programming framework for automating efficient multi-task learning. Advances in Neural Information Processing Systems 35 (2022), 34216–34228.
  32. D-vae: A variational autoencoder for directed acyclic graphs. Advances in Neural Information Processing Systems 32 (2019).
  33. UniMed: Multimodal Multitask Learning for Medical Predictions. In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 1399–1404.
  34. Barret Zoph and Quoc V Le. 2016. Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578 (2016).
  35. Learning transferable architectures for scalable image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 8697–8710.

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