mtslearn: Machine Learning in Python for Medical Time Series

• URL: http://arxiv.org/abs/2603.29432v1
• Date: Tue, 31 Mar 2026 08:38:04 GMT
• Title: mtslearn: Machine Learning in Python for Medical Time Series
• Abstract summary: mtslearn is an end-to-end integrated toolkit specifically designed for medical time-series data.<n>It automates the parsing and alignment of wide, long, and flat data formats.<n>It offers flexible interfaces for custom algorithms.
• Score: 13.551658135095446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical time-series data captures the dynamic progression of patient conditions, playing a vital role in modern clinical decision support systems. However, real-world clinical data is highly heterogeneous and inconsistently formatted. Furthermore, existing machine learning tools often have steep learning curves and fragmented workflows. Consequently, a significant gap remains between cutting-edge AI technologies and clinical application. To address this, we introduce mtslearn, an end-to-end integrated toolkit specifically designed for medical time-series data. First, the framework provides a unified data interface that automates the parsing and alignment of wide, long, and flat data formats. This design significantly reduces data cleaning overhead. Building on this, mtslearn provides a complete pipeline from data reading and feature engineering to model training and result visualization. Furthermore, it offers flexible interfaces for custom algorithms. Through a modular design, mtslearn simplifies complex data engineering tasks into a few lines of code. This significantly lowers the barrier to entry for clinicians with limited programming experience, empowering them to focus more on exploring medical hypotheses and accelerating the translation of advanced algorithms into real-world clinical practice. mtslearn is publicly available at https://github.com/PKUDigitalHealth/mtslearn.

Related papers

• An Efficient Contrastive Unimodal Pretraining Method for EHR Time Series Data [35.943089444017666]
  We propose an efficient method of contrastive pretraining tailored for long clinical timeseries data. Our model demonstrates the ability to impute missing measurements, providing clinicians with deeper insights into patient conditions.
  arXiv  Detail & Related papers  (2024-10-11T19:05:25Z)
• EndToEndML: An Open-Source End-to-End Pipeline for Machine Learning Applications [0.2826977330147589]
  We propose a web-based end-to-end pipeline that is capable of preprocessing, training, evaluating, and visualizing machine learning models. Our library assists in recognizing, classifying, clustering, and predicting a wide range of multi-modal, multi-sensor datasets.
  arXiv  Detail & Related papers  (2024-03-27T02:24:38Z)
• Clairvoyance: A Pipeline Toolkit for Medical Time Series [95.22483029602921]
  Time-series learning is the bread and butter of data-driven *clinical decision support* Clairvoyance proposes a unified, end-to-end, autoML-friendly pipeline that serves as a software toolkit. Clairvoyance is the first to demonstrate viability of a comprehensive and automatable pipeline for clinical time-series ML.
  arXiv  Detail & Related papers  (2023-10-28T12:08:03Z)
• 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)
• TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for Medicine [91.3755431537592]
  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.
  arXiv  Detail & Related papers  (2023-01-28T17:57:53Z)
• Unsupervised pre-training of graph transformers on patient population graphs [48.02011627390706]
  We propose a graph-transformer-based network to handle heterogeneous clinical data. We show the benefit of our pre-training method in a self-supervised and a transfer learning setting.
  arXiv  Detail & Related papers  (2022-07-21T16:59:09Z)
• BERT WEAVER: Using WEight AVERaging to enable lifelong learning for transformer-based models in biomedical semantic search engines [49.75878234192369]
  We present WEAVER, a simple, yet efficient post-processing method that infuses old knowledge into the new model. We show that applying WEAVER in a sequential manner results in similar word embedding distributions as doing a combined training on all data at once.
  arXiv  Detail & Related papers  (2022-02-21T10:34:41Z)
• 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)
• A Data and Compute Efficient Design for Limited-Resources Deep Learning [68.55415606184]
  equivariant neural networks have gained increased interest in the deep learning community. They have been successfully applied in the medical domain where symmetries in the data can be effectively exploited to build more accurate and robust models. Mobile, on-device implementations of deep learning solutions have been developed for medical applications. However, equivariant models are commonly implemented using large and computationally expensive architectures, not suitable to run on mobile devices. In this work, we design and test an equivariant version of MobileNetV2 and further optimize it with model quantization to enable more efficient inference.
  arXiv  Detail & Related papers  (2020-04-21T00:49:11Z)
• 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.