FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction

• URL: http://arxiv.org/abs/2205.15104v1
• Date: Mon, 30 May 2022 13:45:56 GMT
• Title: FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction
• Authors: Lena Mondrejevski, Ioanna Miliou, Annaclaudia Montanino, David Pitts, Jaakko Hollm\'en, Panagiotis Papapetrou
• Abstract summary: Healthcare data is sensitive, requiring strict privacy practices, and typically stored in data silos. Federated learning can counteract those limitations by training machine learning models over data silos. This study proposes a federated learning workflow for ICU mortality prediction.
• Score: 4.16749898760461
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although Machine Learning (ML) can be seen as a promising tool to improve clinical decision-making for supporting the improvement of medication plans, clinical procedures, diagnoses, or medication prescriptions, it remains limited by access to healthcare data. Healthcare data is sensitive, requiring strict privacy practices, and typically stored in data silos, making traditional machine learning challenging. Federated learning can counteract those limitations by training machine learning models over data silos while keeping the sensitive data localized. This study proposes a federated learning workflow for ICU mortality prediction. Hereby, the applicability of federated learning as an alternative to centralized machine learning and local machine learning is investigated by introducing federated learning to the binary classification problem of predicting ICU mortality. We extract multivariate time series data from the MIMIC-III database (lab values and vital signs), and benchmark the predictive performance of four deep sequential classifiers (FRNN, LSTM, GRU, and 1DCNN) varying the patient history window lengths (8h, 16h, 24h, 48h) and the number of FL clients (2, 4, 8). The experiments demonstrate that both centralized machine learning and federated learning are comparable in terms of AUPRC and F1-score. Furthermore, the federated approach shows superior performance over local machine learning. Thus, the federated approach can be seen as a valid and privacy-preserving alternative to centralized machine learning for classifying ICU mortality when sharing sensitive patient data between hospitals is not possible.

Related papers

• Evaluating the Fairness of the MIMIC-IV Dataset and a Baseline Algorithm: Application to the ICU Length of Stay Prediction [65.268245109828]
  This paper uses the MIMIC-IV dataset to examine the fairness and bias in an XGBoost binary classification model predicting the ICU length of stay. The research reveals class imbalances in the dataset across demographic attributes and employs data preprocessing and feature extraction. The paper concludes with recommendations for fairness-aware machine learning techniques for mitigating biases and the need for collaborative efforts among healthcare professionals and data scientists.
  arXiv  Detail & Related papers  (2023-12-31T16:01:48Z)
• Multimodal Pretraining of Medical Time Series and Notes [45.89025874396911]
  Deep learning models show promise in extracting meaningful patterns, but they require extensive labeled data. We propose a novel approach employing self-supervised pretraining, focusing on the alignment of clinical measurements and notes. In downstream tasks, including in-hospital mortality prediction and phenotyping, our model outperforms baselines in settings where only a fraction of the data is labeled.
  arXiv  Detail & Related papers  (2023-12-11T21:53:40Z)
• Early prediction of the risk of ICU mortality with Deep Federated Learning [0.0]
  We evaluate the ability of deep Federated Learning to predict the risk of Intensive Care Unit mortality at an early stage. We show that the prediction performance is higher when the patient history window is closer to discharge or death.
  arXiv  Detail & Related papers  (2022-12-01T15:01:27Z)
• Federated Learning in Multi-Center Critical Care Research: A Systematic Case Study using the eICU Database [24.31499341763427]
  Federated learning (FL) has been proposed as a method to train a model on different units without exchanging data. We investigate the effectiveness of FL on the publicly available eICU dataset for predicting the survival of each ICU stay.
  arXiv  Detail & Related papers  (2022-04-20T09:03:09Z)
• When Accuracy Meets Privacy: Two-Stage Federated Transfer Learning Framework in Classification of Medical Images on Limited Data: A COVID-19 Case Study [77.34726150561087]
  COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. CNN has been widely utilized and verified in analyzing medical images.
  arXiv  Detail & Related papers  (2022-03-24T02:09:41Z)
• Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases [57.90226879210227]
  FedCy is a semi-supervised learning (FSSL) method that combines FL and self-supervised learning to exploit a decentralized dataset of both labeled and unlabeled videos. We demonstrate significant performance gains over state-of-the-art FSSL methods on the task of automatic recognition of surgical phases.
  arXiv  Detail & Related papers  (2022-03-14T17:44:53Z)
• 2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets [89.84774119537087]
  We design two transfer learning challenges around diagnostics and Brain-Computer-Interfacing (BCI) Task 1 is centred on medical diagnostics, addressing automatic sleep stage annotation across subjects. Task 2 is centred on Brain-Computer Interfacing (BCI), addressing motor imagery decoding across both subjects and data sets.
  arXiv  Detail & Related papers  (2022-02-14T12:12:20Z)
• A Machine Learning Early Warning System: Multicenter Validation in Brazilian Hospitals [4.659599449441919]
  Early recognition of clinical deterioration is one of the main steps for reducing inpatient morbidity and mortality. Since hospital wards are given less attention compared to the Intensive Care Unit, ICU, we hypothesized that when a platform is connected to a stream of EHR, there would be a drastic improvement in dangerous situations awareness. With the application of machine learning, the system is capable to consider all patient's history and through the use of high-performing predictive models, an intelligent early warning system is enabled.
  arXiv  Detail & Related papers  (2020-06-09T21:21:38Z)
• Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification [80.00316465793702]
  We present a deep learning framework that enables robust modeling in challenging scenarios. Our results show that using 85% lesser labeled data, we can build predictive models that match the performance of classifiers trained in a large-scale data setting.
  arXiv  Detail & Related papers  (2020-05-03T02:36:00Z)

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.