mlsanityData to 14 Oct 2026

TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for Medicine

arXiv 2301.12260 ↗First posted 28 Jan 2023cs.LG cs.AIcited by 5

Not on the map. Why?

Abstract (arXiv)

TemporAI is an open source Python software library for machine learning (ML) tasks involving data with a time component, focused on medicine and healthcare use cases. It supports data in time series, static, and eventmodalities and provides an interface for prediction, causal inference, and time-to-event analysis, as well as common preprocessing utilities and model interpretability methods. The library aims to facilitate innovation in the medical ML space by offering a standardized temporal setting toolkit for model development, prototyping and benchmarking, bridging the gaps in the ML research, healthcare professional, medical/pharmacological industry, and data science communities. TemporAI is available on GitHub (https://github.com/vanderschaarlab/temporai) and we welcome community engagement through use, feedback, and code contributions.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 11 of its 31 references that are in the map. First posted on arXiv 28 Jan 2023.

Builds on

11 of 31 references.

References · 11

  1. 2020Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representationscited by 155
  2. 2017Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysiscited by 148
  3. 2019sktime: A Unified Interface for Machine Learning with Time Seriescited by 102
  4. 2021Darts: User-Friendly Modern Machine Learning for Time Seriescited by 91
  5. 2020TimeSHAP: Explaining Recurrent Models through Sequence Perturbationscited by 74
  6. 2021Explaining Time Series Predictions with Dynamic Maskscited by 73
  7. 2021SurvTRACE: Transformers for Survival Analysis with Competing Eventscited by 41
  8. 2020Temporal Phenotyping using Deep Predictive Clustering of Disease Progressioncited by 26
  9. 2022Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimationcited by 25
  10. 2016A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inferencecited by 6
  11. 2018Seglearn: A Python Package for Learning Sequences and Time Seriescited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 0; 2026: 1.

5 papers.

  1. 2023Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Eventscited by 46
  2. 2024HoTPP Benchmark: Are We Good at the Long Horizon Events Forecasting?cited by 13
  3. 2024ACES: Automatic Cohort Extraction System for Event-Stream Datasetscited by 9
  4. 2024MEDS-Tab: Automated tabularization and baseline methods for MEDS datasetscited by 4
  5. 2026mtslearn: Machine Learning in Python for Medical Time Seriescited by 0