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

• URL: http://arxiv.org/abs/2301.12260v1
• Date: Sat, 28 Jan 2023 17:57:53 GMT
• Title: TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for Medicine
• Authors: Evgeny S. Saveliev and Mihaela van der Schaar
• Abstract summary: TemporAI is an open source Python software library for machine learning (ML) tasks involving data with a time component. It supports data in time series, static, and eventmodalities and provides an interface for prediction, causal inference, and time-to-event analysis.
• Score: 91.3755431537592
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

Related papers

• Automated Extraction and Maturity Analysis of Open Source Clinical Informatics Repositories from Scientific Literature [0.0]
  This study introduces an automated methodology to bridge the gap by systematically extracting GitHub repository URLs from academic papers indexed in arXiv. Our approach encompasses querying the arXiv API for relevant papers, cleaning extracted GitHub URLs, fetching comprehensive repository information via the GitHub API, and analyzing repository maturity based on defined metrics such as stars, forks, open issues, and contributors.
  arXiv  Detail & Related papers  (2024-03-20T17:06:51Z)
• Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical ML [0.7982607013768545]
  Yet Another ICU Benchmark (YAIB) is a modular framework that allows researchers to define reproducible and comparable clinical ML experiments. YAIB supports most open-access ICU datasets (MIMIC III/IV, eICU, HiRID, AUMCdb) and is easily adaptable to future ICU datasets. We demonstrate that the choice of dataset, cohort definition, and preprocessing have a major impact on the prediction performance.
  arXiv  Detail & Related papers  (2023-06-08T11:16:20Z)
• TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series [61.436361263605114]
  Time series data are often scarce or highly sensitive, which precludes the sharing of data between researchers and industrial organizations. We introduce Time Series Generative Modeling (TSGM), an open-source framework for the generative modeling of synthetic time series.
  arXiv  Detail & Related papers  (2023-05-19T10:11:21Z)
• Time Associated Meta Learning for Clinical Prediction [78.99422473394029]
  We propose a novel time associated meta learning (TAML) method to make effective predictions at multiple future time points. To address the sparsity problem after task splitting, TAML employs a temporal information sharing strategy to augment the number of positive samples. We demonstrate the effectiveness of TAML on multiple clinical datasets, where it consistently outperforms a range of strong baselines.
  arXiv  Detail & Related papers  (2023-03-05T03:54:54Z)
• MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images [49.664220687980006]
  The lack of annotated datasets is a major bottleneck for training new task-specific supervised machine learning models. We present MONAI Label, a free and open-source framework that facilitates the development of applications based on artificial intelligence (AI) models.
  arXiv  Detail & Related papers  (2022-03-23T12:33:11Z)
• Automated Machine Learning Techniques for Data Streams [91.3755431537592]
  This paper surveys the state-of-the-art open-source AutoML tools, applies them to data collected from streams, and measures how their performance changes over time. The results show that off-the-shelf AutoML tools can provide satisfactory results but in the presence of concept drift, detection or adaptation techniques have to be applied to maintain the predictive accuracy over time.
  arXiv  Detail & Related papers  (2021-06-14T11:42:46Z)
• The FeatureCloud AI Store for Federated Learning in Biomedicine and Beyond [0.7517525791460022]
  Privacy-preserving methods, such as Federated Learning (FL), allow for training ML models without sharing sensitive data. We present the FeatureCloud AI Store for FL as an all-in-one platform for biomedical research and other applications.
  arXiv  Detail & Related papers  (2021-05-12T15:31:46Z)
• PyHealth: A Python Library for Health Predictive Models [53.848478115284195]
  PyHealth is an open-source Python toolbox for developing various predictive models on healthcare data. The data preprocessing module enables the transformation of complex healthcare datasets into machine learning friendly formats. The predictive modeling module provides more than 30 machine learning models, including established ensemble trees and deep neural network-based approaches.
  arXiv  Detail & Related papers  (2021-01-11T22:02:08Z)
• PHOTONAI -- A Python API for Rapid Machine Learning Model Development [2.414341608751139]
  PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development. It functions as a unifying framework allowing the user to easily access and combine algorithms from different toolboxes into custom algorithm sequences.
  arXiv  Detail & Related papers  (2020-02-13T10:33:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.