Enhancing Causal Estimation through Unlabeled Offline Data

• URL: http://arxiv.org/abs/2202.07895v1
• Date: Wed, 16 Feb 2022 07:02:42 GMT
• Title: Enhancing Causal Estimation through Unlabeled Offline Data
• Authors: Ron Teichner, Ron Meir, Danny Eitan
• Abstract summary: We wish to assess relevant unmeasured physiological variables that have a strong effect on the patients diagnosis and treatment. Extensive offline information is available about previous patients, that may only be partially related to the present patient. Our proposed approach consists of three stages: (i) Use the abundant offline data in order to create both a non-causal and a causal estimator. We demonstrate the effectiveness of this methodology on a (non-medical) real-world task, in situations where the offline data is only partially related to the new observations.
• Score: 7.305019142196583
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Consider a situation where a new patient arrives in the Intensive Care Unit (ICU) and is monitored by multiple sensors. We wish to assess relevant unmeasured physiological variables (e.g., cardiac contractility and output and vascular resistance) that have a strong effect on the patients diagnosis and treatment. We do not have any information about this specific patient, but, extensive offline information is available about previous patients, that may only be partially related to the present patient (a case of dataset shift). This information constitutes our prior knowledge, and is both partial and approximate. The basic question is how to best use this prior knowledge, combined with online patient data, to assist in diagnosing the current patient most effectively. Our proposed approach consists of three stages: (i) Use the abundant offline data in order to create both a non-causal and a causal estimator for the relevant unmeasured physiological variables. (ii) Based on the non-causal estimator constructed, and a set of measurements from a new group of patients, we construct a causal filter that provides higher accuracy in the prediction of the hidden physiological variables for this new set of patients. (iii) For any new patient arriving in the ICU, we use the constructed filter in order to predict relevant internal variables. Overall, this strategy allows us to make use of the abundantly available offline data in order to enhance causal estimation for newly arriving patients. We demonstrate the effectiveness of this methodology on a (non-medical) real-world task, in situations where the offline data is only partially related to the new observations. We provide a mathematical analysis of the merits of the approach in a linear setting of Kalman filtering and smoothing, demonstrating its utility.

Related papers

• MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation [58.93221876843639]
  This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
  arXiv  Detail & Related papers  (2023-10-04T01:36:30Z)
• Hypergraph Convolutional Networks for Fine-grained ICU Patient Similarity Analysis and Risk Prediction [15.06049250330114]
  The Intensive Care Unit (ICU) is one of the most important parts of a hospital, which admits critically ill patients and provides continuous monitoring and treatment. Various patient outcome prediction methods have been attempted to assist healthcare professionals in clinical decision-making.
  arXiv  Detail & Related papers  (2023-08-24T05:26:56Z)
• Enabling scalable clinical interpretation of ML-based phenotypes using real world data [0.0]
  This study investigates approaches to perform patient stratification analysis at scale using large EHR datasets. We have developed several tools to facilitate the clinical evaluation and interpretation of unsupervised patient stratification results.
  arXiv  Detail & Related papers  (2022-08-02T17:31:03Z)
• Adherence Forecasting for Guided Internet-Delivered Cognitive Behavioral Therapy: A Minimally Data-Sensitive Approach [59.535699822923]
  Internet-delivered psychological treatments (IDPT) are seen as an effective and scalable pathway to improving the accessibility of mental healthcare. This work proposes a deep-learning approach to perform automatic adherence forecasting, while relying on minimally sensitive login/logout data. The proposed Self-Attention Network achieved over 70% average balanced accuracy, when only 1/3 of the treatment duration had elapsed.
  arXiv  Detail & Related papers  (2022-01-11T13:55:57Z)
• Predicting Patient Readmission Risk from Medical Text via Knowledge Graph Enhanced Multiview Graph Convolution [67.72545656557858]
  We propose a new method that uses medical text of Electronic Health Records for prediction. We represent discharge summaries of patients with multiview graphs enhanced by an external knowledge graph. Experimental results prove the effectiveness of our method, yielding state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-12-19T01:45:57Z)
• Optimal discharge of patients from intensive care via a data-driven policy learning framework [58.720142291102135]
  It is important that the patient discharge task addresses the nuanced trade-off between decreasing a patient's length of stay and the risk of readmission or even death following the discharge decision. This work introduces an end-to-end general framework for capturing this trade-off to recommend optimal discharge timing decisions. A data-driven approach is used to derive a parsimonious, discrete state space representation that captures a patient's physiological condition.
  arXiv  Detail & Related papers  (2021-12-17T04:39:33Z)
• Sequential Diagnosis Prediction with Transformer and Ontological Representation [35.88195694025553]
  We propose an end-to-end robust transformer-based model called SETOR to handle irregular intervals between a patient's visits with admitted timestamps and length of stay in each visit. Experiments conducted on two real-world healthcare datasets show that, our sequential diagnoses prediction model SETOR achieves better predictive results than previous state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-09-07T13:09:55Z)
• 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)
• 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)
• BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes [53.163089893876645]
  We propose a novel self-attention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey. An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys. We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset.
  arXiv  Detail & Related papers  (2020-09-24T00:42:36Z)
• Segmentation analysis and the recovery of queuing parameters via the Wasserstein distance: a study of administrative data for patients with chronic obstructive pulmonary disease [0.0]
  This work uses a data-driven approach to analyse how the resource requirements of patients with chronic obstructive pulmonary disease (COPD) may change. It is composed of a novel combination of often distinct modes of analysis: segmentation, operational queuing theory, and the recovery of parameters from incomplete data.
  arXiv  Detail & Related papers  (2020-08-10T17:47:34Z)

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.