Individualised recovery trajectories of patients with impeded mobility, using distance between probability distributions of learnt graphs

• URL: http://arxiv.org/abs/2410.21983v1
• Date: Tue, 29 Oct 2024 12:13:00 GMT
• Title: Individualised recovery trajectories of patients with impeded mobility, using distance between probability distributions of learnt graphs
• Authors: Chuqiao Zhang, Crina Grosan, Dalia Chakrabarty,
• Abstract summary: We provide a method for the learning of the recovery trajectory of an individual patient, as they undertake exercises as part of their physical therapy. The difference between the Movement Recovery Scores attained by a patient, when undertaking a given exercise routine on successive instances, is given by a statistical distance/divergence.
• Score: 0.3604879434384176
• License:
• Abstract: Patients who are undergoing physical rehabilitation, benefit from feedback that follows from reliable assessment of their cumulative performance attained at a given time. In this paper, we provide a method for the learning of the recovery trajectory of an individual patient, as they undertake exercises as part of their physical therapy towards recovery of their loss of movement ability, following a critical illness. The difference between the Movement Recovery Scores (MRSs) attained by a patient, when undertaking a given exercise routine on successive instances, is given by a statistical distance/divergence between the (posterior) probabilities of random graphs that are Bayesianly learnt using time series data on locations of 20 of the patient's joints, recorded on an e-platform as the patient exercises. This allows for the computation of the MRS on every occasion the patient undertakes this exercise, using which, the recovery trajectory is drawn. We learn each graph as a Random Geometric Graph drawn in a probabilistic metric space, and identify the closed-form marginal posterior of any edge of the graph, given the correlation structure of the multivariate time series data on joint locations. On the basis of our recovery learning, we offer recommendations on the optimal exercise routines for patients with given level of mobility impairment.

Related papers

• A Medical Low-Back Pain Physical Rehabilitation Dataset for Human Body Movement Analysis [0.6990493129893111]
  This article addresses four challenges to address and propose a medical dataset of clinical patients carrying out low back-pain rehabilitation exercises. The dataset includes 3D Kinect skeleton positions and orientations, RGB videos, 2D skeleton data, and medical annotations to assess the correctness, and error classification and localisation of body part and timespan.
  arXiv  Detail & Related papers  (2024-06-29T19:50:06Z)
• Rehabilitation Exercise Quality Assessment through Supervised Contrastive Learning with Hard and Soft Negatives [2.166000001057538]
  Exercise-based rehabilitation programs have proven to be effective in enhancing the quality of life and reducing mortality and rehospitalization rates. These programs commonly prescribe a variety of exercise types, leading to a distinct challenge in rehabilitation exercise assessment datasets. This paper introduces a novel supervised contrastive learning framework with hard and soft negative samples to train a single model applicable to all exercise types.
  arXiv  Detail & Related papers  (2024-03-05T08:38:25Z)
• Rehabilitation Exercise Repetition Segmentation and Counting using Skeletal Body Joints [6.918076156491651]
  This paper presents a novel approach for segmenting and counting the repetitions of rehabilitation exercises performed by patients. Skeletal body joints can be acquired through depth cameras or computer vision techniques applied to RGB videos of patients. Various sequential neural networks are designed to analyze the sequences of skeletal body joints and perform repetition segmentation and counting.
  arXiv  Detail & Related papers  (2023-04-19T15:22:15Z)
• Mimetic Muscle Rehabilitation Analysis Using Clustering of Low Dimensional 3D Kinect Data [1.53119329713143]
  This paper discusses an unsupervised approach to rehabilitating patients who have temporary facial paralysis due to damage in mimetic muscles. The work aims to make the rehabilitation process objective compared to the current subjective approach, such as House-Brackmann (HB) scale. The study contains data set of 85 distinct patients with 120 measurements obtained using a Kinect stereo-vision camera.
  arXiv  Detail & Related papers  (2023-02-15T09:45:27Z)
• Easing Automatic Neurorehabilitation via Classification and Smoothness Analysis [1.44744639843118]
  We propose an automatic assessment pipeline that starts by recognizing patients' movements by means of a shallow deep learning architecture, then measuring the movement quality using jerk measure and related measures. A particularity of this work is that the dataset used is clinically relevant, since it represents movements inspired from Fugl-Meyer a well common upper-limb clinical stroke assessment scale for stroke patients. We show that it is possible to detect the contrast between healthy and patients movements in terms of smoothness, besides achieving conclusions about the patients' progress during the rehabilitation sessions that correspond to the clinicians' findings about each case.
  arXiv  Detail & Related papers  (2022-12-09T13:59:14Z)
• 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)
• Disentangled Counterfactual Recurrent Networks for Treatment Effect Inference over Time [71.30985926640659]
  We introduce the Disentangled Counterfactual Recurrent Network (DCRN), a sequence-to-sequence architecture that estimates treatment outcomes over time. With an architecture that is completely inspired by the causal structure of treatment influence over time, we advance forecast accuracy and disease understanding. We demonstrate that DCRN outperforms current state-of-the-art methods in forecasting treatment responses, on both real and simulated data.
  arXiv  Detail & Related papers  (2021-12-07T16:40:28Z)
• Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectories [55.786207368853084]
  We address the problem of modeling constrained hospital resources in the midst of the COVID-19 pandemic. For broad applicability, we focus on the common yet challenging scenario where patient-level data for a region of interest are not available. We propose an aggregate count explicit-duration hidden Markov model, nicknamed the ACED-HMM, with an interpretable, compact parameterization.
  arXiv  Detail & Related papers  (2021-04-28T15:32:42Z)
• 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)
• 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.