DeepJoint: Robust Survival Modelling Under Clinical Presence Shift

• URL: http://arxiv.org/abs/2205.13481v1
• Date: Thu, 26 May 2022 16:42:38 GMT
• Title: DeepJoint: Robust Survival Modelling Under Clinical Presence Shift
• Authors: Vincent Jeanselme, Glen Martin, Niels Peek, Matthew Sperrin, Brian Tom and Jessica Barrett
• Abstract summary: We propose a recurrent neural network which models three clinical presence dimensions in parallel to the survival outcome. On a prediction task, explicit modelling of these three processes showed improved performance in comparison to state-of-the-art predictive models.
• Score: 2.9745607433320926
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Observational data in medicine arise as a result of the complex interaction between patients and the healthcare system. The sampling process is often highly irregular and itself constitutes an informative process. When using such data to develop prediction models, this phenomenon is often ignored, leading to sub-optimal performance and generalisability of models when practices evolve. We propose a multi-task recurrent neural network which models three clinical presence dimensions -- namely the longitudinal, the inter-observation and the missingness processes -- in parallel to the survival outcome. On a prediction task using MIMIC III laboratory tests, explicit modelling of these three processes showed improved performance in comparison to state-of-the-art predictive models (C-index at 1 day horizon: 0.878). More importantly, the proposed approach was more robust to change in the clinical presence setting, demonstrated by performance comparison between patients admitted on weekdays and weekends. This analysis demonstrates the importance of studying and leveraging clinical presence to improve performance and create more transportable clinical models.

Related papers

• Deep State-Space Generative Model For Correlated Time-to-Event Predictions [54.3637600983898]
  We propose a deep latent state-space generative model to capture the interactions among different types of correlated clinical events. Our method also uncovers meaningful insights about the latent correlations among mortality and different types of organ failures.
  arXiv  Detail & Related papers  (2024-07-28T02:42:36Z)
• Enhancing Uncertain Demand Prediction in Hospitals Using Simple and Advanced Machine Learning [3.9054437595657534]
  Using patient care demand data from Rambam Medical Center in Israel, our results show that both proposed models effectively capture hourly variations of patient demand. It is possible to predict patient care demand with good accuracy (around 4 patients) three days or a week in advance using machine learning.
  arXiv  Detail & Related papers  (2024-04-29T13:05:59Z)
• Personalized Event Prediction for Electronic Health Records [7.224184629864593]
  Clinical event sequences consist of hundreds of clinical events that represent records of patient care in time. One important challenge of learning predictive models of clinical sequences is their patient-specific variability.
  arXiv  Detail & Related papers  (2023-08-21T20:03:16Z)
• Towards Outcome-Driven Patient Subgroups: A Machine Learning Analysis Across Six Depression Treatment Studies [41.34047608276278]
  We analyzed data from six clinical trials of pharmacological treatment for depression using a neural network model. A model classifying remission and outputting individual remission probabilities for five first-line monotherapies and three combination treatments was trained. Post-hoc analyses yielded clusters (subgroups) based on patient prototypes learned during training.
  arXiv  Detail & Related papers  (2023-03-24T14:34:09Z)
• Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability [82.29775890542967]
  Estimating personalized effects of treatments is a complex, yet pervasive problem. Recent developments in the machine learning literature on heterogeneous treatment effect estimation gave rise to many sophisticated, but opaque, tools. We use post-hoc feature importance methods to identify features that influence the model's predictions.
  arXiv  Detail & Related papers  (2022-06-16T17:59:05Z)
• What Do You See in this Patient? Behavioral Testing of Clinical NLP Models [69.09570726777817]
  We introduce an extendable testing framework that evaluates the behavior of clinical outcome models regarding changes of the input. We show that model behavior varies drastically even when fine-tuned on the same data and that allegedly best-performing models have not always learned the most medically plausible patterns.
  arXiv  Detail & Related papers  (2021-11-30T15:52:04Z)
• Building Deep Learning Models to Predict Mortality in ICU Patients [0.0]
  We propose several deep learning models using the same features as the SAPS II score. Several experiments have been conducted based on the well known clinical dataset Medical Information Mart for Intensive Care III.
  arXiv  Detail & Related papers  (2020-12-11T16:27:04Z)
• Longitudinal modeling of MS patient trajectories improves predictions of disability progression [2.117653457384462]
  This work addresses the task of optimally extracting information from longitudinal patient data in the real-world setting. We show that with machine learning methods suited for patient trajectories modeling, we can predict disability progression of patients in a two-year horizon. Compared to the models available in the literature, this work uses the most complete patient history for MS disease progression prediction.
  arXiv  Detail & Related papers  (2020-11-09T20:48:00Z)
• MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response [58.0291320452122]
  This paper aims at a unified deep learning approach to predict patient prognosis and therapy response. We formalize the prognosis modeling as a multi-modal asynchronous time series classification task. Our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.
  arXiv  Detail & Related papers  (2020-10-08T15:30:17Z)
• Temporal Phenotyping using Deep Predictive Clustering of Disease Progression [97.88605060346455]
  We develop a deep learning approach for clustering time-series data, where each cluster comprises patients who share similar future outcomes of interest. Experiments on two real-world datasets show that our model achieves superior clustering performance over state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2020-06-15T20:48:43Z)
• Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations [114.16762407465427]
  We introduce the Counterfactual Recurrent Network (CRN) to estimate treatment effects over time. CRN uses domain adversarial training to build balancing representations of the patient history. We show how our model achieves lower error in estimating counterfactuals and in choosing the correct treatment and timing of treatment.
  arXiv  Detail & Related papers  (2020-02-10T20:47:36Z)

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.