Personalized Event Prediction for Electronic Health Records

• URL: http://arxiv.org/abs/2308.11013v1
• Date: Mon, 21 Aug 2023 20:03:16 GMT
• Title: Personalized Event Prediction for Electronic Health Records
• Authors: Jeong Min Lee and Milos Hauskrecht
• Abstract summary: 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.
• Score: 7.224184629864593
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Clinical event sequences consist of hundreds of clinical events that represent records of patient care in time. Developing accurate predictive models of such sequences is of a great importance for supporting a variety of models for interpreting/classifying the current patient condition, or predicting adverse clinical events and outcomes, all aimed to improve patient care. One important challenge of learning predictive models of clinical sequences is their patient-specific variability. Based on underlying clinical conditions, each patient's sequence may consist of different sets of clinical events (observations, lab results, medications, procedures). Hence, simple population-wide models learned from event sequences for many different patients may not accurately predict patient-specific dynamics of event sequences and their differences. To address the problem, we propose and investigate multiple new event sequence prediction models and methods that let us better adjust the prediction for individual patients and their specific conditions. The methods developed in this work pursue refinement of population-wide models to subpopulations, self-adaptation, and a meta-level model switching that is able to adaptively select the model with the best chance to support the immediate prediction. We analyze and test the performance of these models on clinical event sequences of patients in MIMIC-III database.

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)
• Ethical considerations of use of hold-out sets in clinical prediction model management [0.4194295877935868]
  We focus on the ethical principles of beneficence, non-maleficence, autonomy and justice. We also discuss statistical issues arising from different hold-out set sampling methods.
  arXiv  Detail & Related papers  (2024-06-05T11:42:46Z)
• SPOT: Sequential Predictive Modeling of Clinical Trial Outcome with Meta-Learning [67.8195828626489]
  Clinical trials are essential to drug development but time-consuming, costly, and prone to failure. We propose Sequential Predictive mOdeling of clinical Trial outcome (SPOT) that first identifies trial topics to cluster the multi-sourced trial data into relevant trial topics. With the consideration of each trial sequence as a task, it uses a meta-learning strategy to achieve a point where the model can rapidly adapt to new tasks with minimal updates.
  arXiv  Detail & Related papers  (2023-04-07T23:04:27Z)
• DeepJoint: Robust Survival Modelling Under Clinical Presence Shift [2.9745607433320926]
  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.
  arXiv  Detail & Related papers  (2022-05-26T16:42:38Z)
• Bridging the Gap Between Patient-specific and Patient-independent Seizure Prediction via Knowledge Distillation [7.2666838978096875]
  Existing approaches typically train models in a patient-specific fashion due to the highly personalized characteristics of epileptic signals. A patient-specific model can then be obtained with the help of distilled knowledge and additional personalized data. Five state-of-the-art seizure prediction methods are trained on the CHB-MIT sEEG database with our proposed scheme.
  arXiv  Detail & Related papers  (2022-02-25T10:30:29Z)
• 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)
• Literature-Augmented Clinical Outcome Prediction [10.46990394710927]
  We introduce techniques to help bridge this gap between EBM and AI-based clinical models. We propose a novel system that automatically retrieves patient-specific literature based on intensive care (ICU) patient information. Our model is able to substantially boost predictive accuracy on three challenging tasks in comparison to strong recent baselines.
  arXiv  Detail & Related papers  (2021-11-16T11:19:02Z)
• Neural Clinical Event Sequence Prediction through Personalized Online Adaptive Learning [11.574235466142833]
  Clinical event sequences consist of thousands of clinical events that represent records of patient care in time. One important challenge of learning a good predictive model of clinical sequences is patient-specific variability. We develop a new adaptive event sequence prediction framework that learns to adjust its prediction for individual patients through an online model update.
  arXiv  Detail & Related papers  (2021-04-05T06:22:56Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)
• Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes data [94.37521840642141]
  We suggest a semi-supervised methodology for the analysis of large clinical datasets, characterized by mixed data types and missing values. The methodology is based on application of elastic principal graphs which can address simultaneously the tasks of dimensionality reduction, data visualization, clustering, feature selection and quantifying the geodesic distances (pseudotime) in partially ordered sequences of observations.
  arXiv  Detail & Related papers  (2020-07-07T21:04:55Z)

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.