mlsanityData to 14 Oct 2026

Clairvoyance: A Pipeline Toolkit for Medical Time Series

arXiv 2310.18688 ↗First posted 28 Oct 2023cs.LGcited by 12

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Abstract (arXiv)

Time-series learning is the bread and butter of data-driven *clinical decision support*, and the recent explosion in ML research has demonstrated great potential in various healthcare settings. At the same time, medical time-series problems in the wild are challenging due to their highly *composite* nature: They entail design choices and interactions among components that preprocess data, impute missing values, select features, issue predictions, estimate uncertainty, and interpret models. Despite exponential growth in electronic patient data, there is a remarkable gap between the potential and realized utilization of ML for clinical research and decision support. In particular, orchestrating a real-world project lifecycle poses challenges in engineering (i.e. hard to build), evaluation (i.e. hard to assess), and efficiency (i.e. hard to optimize). Designed to address these issues simultaneously, Clairvoyance proposes a unified, end-to-end, autoML-friendly pipeline that serves as a (i) software toolkit, (ii) empirical standard, and (iii) interface for optimization. Our ultimate goal lies in facilitating transparent and reproducible experimentation with complex inference workflows, providing integrated pathways for (1) personalized prediction, (2) treatment-effect estimation, and (3) information acquisition. Through illustrative examples on real-world data in outpatient, general wards, and intensive-care settings, we illustrate the applicability of the pipeline paradigm on core tasks in the healthcare journey. To the best of our knowledge, Clairvoyance is the first to demonstrate viability of a comprehensive and automatable pipeline for clinical time-series ML.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 34 of its 132 references that are in the map. First posted on arXiv 28 Oct 2023.

Builds on

34 of 132 references.

References · 34

  1. 2016Recurrent Neural Networks for Multivariate Time Series with Missing Valuescited by 554
  2. 2015Deep Kernel Learningcited by 445
  3. 2018GAIN: Missing Data Imputation using Generative Adversarial Netscited by 352
  4. 2016RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanismcited by 349
  5. 2017Multitask learning and benchmarking with clinical time series datacited by 290
  6. 2018BRITS: Bidirectional Recurrent Imputation for Time Seriescited by 290
  7. 2018Scalable and accurate deep learning for electronic health recordscited by 280
  8. 2015Doctor AI: Predicting Clinical Events via Recurrent Neural Networkscited by 208
  9. 2020Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representationscited by 155
  10. 2017Attend and Diagnose: Clinical Time Series Analysis using Attention Modelscited by 105
  11. 2019sktime: A Unified Interface for Machine Learning with Time Seriescited by 102
  12. 2015Learning to Diagnose with LSTM Recurrent Neural Networkscited by 99
22 more
  1. 2017Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networkscited by 86
  2. 2019Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounderscited by 75
  3. 2017Batched High-dimensional Bayesian Optimization via Structural Kernel Learningcited by 51
  4. 2017Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifiercited by 47
  5. 2019Can I Trust the Explainer? Verifying Post-hoc Explanatory Methodscited by 38
  6. 2017Classification with Costly Features using Deep Reinforcement Learningcited by 34
  7. 2018AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learningcited by 22
  8. 2016Personalized Risk Scoring for Critical Care Prognosis using Mixtures of Gaussian Processescited by 19
  9. 2017Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes Processes for Risk Prognosiscited by 18
  10. 2016A Bayesian Nonparametric Approach for Estimating Individualized Treatment-Response Curvescited by 15
  11. 2019ASAC: Active Sensing using Actor-Critic modelscited by 13
  12. 2018Imputation of Clinical Covariates in Time Seriescited by 10
  13. 2018Disease-Atlas: Navigating Disease Trajectories with Deep Learningcited by 8
  14. 2016A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inferencecited by 6
  15. 2020What went wrong and when? Instance-wise Feature Importance for Time-series Modelscited by 6
  16. 2018Seglearn: A Python Package for Learning Sequences and Time Seriescited by 5
  17. 2018Progressive Sampling-Based Bayesian Optimization for Efficient and Automatic Machine Learning Model Selectioncited by 5
  18. 2020Target-Embedding Autoencoders for Supervised Representation Learningcited by 5
  19. 2016FLASH: Fast Bayesian Optimization for Data Analytic Pipelinescited by 4
  20. 2017Sparse Multi-Output Gaussian Processes for Medical Time Series Predictioncited by 3
  21. 2020Stepwise Model Selection for Sequence Prediction via Deep Kernel Learningcited by 3
  22. 2019UQ-CHI: An Uncertainty Quantification-Based Contemporaneous Health Index for Degenerative Disease Monitoringcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 5; 2026: 2.

12 papers.

  1. 2024Benchmarking with MIMIC-IV, an irregular, spare clinical time series datasetcited by 5
  2. 2024MEDS-Tab: Automated tabularization and baseline methods for MEDS datasetscited by 4
  3. 2026Harnessing Generalist Agents for Contextualized Time Seriescited by 3
  4. 2024Large Language Model as a Universal Clinical Multi-task Decodercited by 2
  5. 2023auto-sktime: Automated Time Series Forecastingcited by 1
  6. 2024On the Regularization of Learnable Embeddings for Time Series Forecastingcited by 1
  7. 2025Tyee: A Unified, Modular, and Fully-Integrated Configurable Toolkit for Intelligent Physiological Health Carecited by 1
  8. 2025Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guidecited by 0
  9. 2025EMR-AGENT: Automating Cohort and Feature Extraction from EMR Databasescited by 0
  10. 2025Closing Gaps: An Imputation Analysis of ICU Vital Signscited by 0
  11. 2025VitalBench: A Rigorous Multi-Center Benchmark for Long-Term Vital Sign Prediction in Intraoperative Carecited by 0
  12. 2026When Does Multimodal Learning Help in Healthcare? A Benchmark on EHR and Chest X-Ray Fusioncited by 0