Personalising Digital Health Behaviour Change Interventions using Machine Learning and Domain Knowledge

• URL: http://arxiv.org/abs/2304.03392v4
• Date: Tue, 6 Jun 2023 22:57:41 GMT
• Title: Personalising Digital Health Behaviour Change Interventions using Machine Learning and Domain Knowledge
• Authors: Aneta Lisowska, Szymon Wilk, Mor Peleg
• Abstract summary: We are developing a virtual coaching system that helps patients adhere to behavior change interventions (BCI) Our proposed system predicts whether a patient will perform the targeted behaviour and uses counterfactual examples with feature control to guide personalisation of BCI.
• Score: 0.7476901945542385
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We are developing a virtual coaching system that helps patients adhere to behavior change interventions (BCI). Our proposed system predicts whether a patient will perform the targeted behaviour and uses counterfactual examples with feature control to guide personalisation of BCI. We use simulated patient data with varying levels of receptivity to intervention to arrive at the study design which would enable evaluation of our system.

Related papers

• Policy Learning for Social Robot-Led Physiotherapy [0.9666308705700454]
  Social robots offer a promising solution for autonomously guiding patients through physiotherapy exercise sessions.<n>Key challenge is the scarcity of patient behavior data for developing robust policies.<n>We engaged 33 expert healthcare practitioners as patient proxies, using their interactions with our robot to inform a patient behavior model.
  arXiv  Detail & Related papers  (2025-09-14T14:40:30Z)
• A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis [3.5280004326441365]
  PatWay-Net is an ML framework designed for interpretable predictions of admission to the intensive care unit for patients with sepsis. We propose a novel type of recurrent neural network and combine it with multi-layer perceptrons to process the patient pathways. We demonstrate its utility through a comprehensive dashboard that visualizes patient health trajectories, predictive outcomes, and associated risks.
  arXiv  Detail & Related papers  (2024-05-21T20:31:42Z)
• Probabilistic emotion and sentiment modelling of patient-reported experiences [0.04096453902709291]
  This study introduces a novel methodology for modelling patient emotions from online patient experience narratives. We employ metadata network topic modelling to analyse patient-reported experiences from Care Opinion. We develop a probabilistic, context-specific emotion recommender system capable of predicting both multilabel emotions and binary sentiments.
  arXiv  Detail & Related papers  (2024-01-09T05:39:20Z)
• Counterfactual Explanations and Predictive Models to Enhance Clinical Decision-Making in Schizophrenia using Digital Phenotyping [0.966840768820136]
  We propose a machine learning system capable of predicting, detecting, and explaining individual changes in symptoms of patients with Schizophrenia. The system detects decreases in symptoms using changepoint algorithms and uses counterfactual explanations as a recourse in a simulated continuous monitoring scenario in healthcare.
  arXiv  Detail & Related papers  (2023-06-06T19:33:03Z)
• Design, Development, and Evaluation of an Interactive Personalized Social Robot to Monitor and Coach Post-Stroke Rehabilitation Exercises [68.37238218842089]
  We develop an interactive social robot exercise coaching system for personalized rehabilitation. This system integrates a neural network model with a rule-based model to automatically monitor and assess patients' rehabilitation exercises. Our system can adapt to new participants and achieved 0.81 average performance to assess their exercises, which is comparable to the experts' agreement level.
  arXiv  Detail & Related papers  (2023-05-12T17:37:04Z)
• 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)
• Personalized Rehabilitation Robotics based on Online Learning Control [62.6606062732021]
  We propose a novel online learning control architecture, which is able to personalize the control force at run time to each individual user. We evaluate our method in an experimental user study, where the learning controller is shown to provide personalized control, while also obtaining safe interaction forces.
  arXiv  Detail & Related papers  (2021-10-01T15:28:44Z)
• Development of patients triage algorithm from nationwide COVID-19 registry data based on machine learning [1.0323063834827415]
  This paper provides the development processes of the severity assessment model using machine learning techniques. Model only requires basic patients' basic personal data, allowing for them to judge their own severity. We aim to establish a medical system that allows patients to check their own severity and informs them to visit the appropriate clinic center based on the past treatment details of other patients with similar severity.
  arXiv  Detail & Related papers  (2021-09-18T19:56:27Z)
• AI-Augmented Behavior Analysis for Children with Developmental Disabilities: Building Towards Precision Treatment [2.0324247356209835]
  We present an AI-Augmented Learning and Applied Behavior Analytics (AI-ABA) platform to provide personalized treatment and learning plans. By defining systematic experiments along with automated data collection and analysis, AI-ABA can promote self-regulative behavior.
  arXiv  Detail & Related papers  (2021-02-21T16:15:40Z)
• 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)
• Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records [57.75125067744978]
  We propose a data augmentation method to facilitate domain adaptation. adversarially generated samples are used during domain adaptation. Results confirm the effectiveness of our method and the generality on different tasks.
  arXiv  Detail & Related papers  (2021-01-13T03:20:20Z)
• Opportunities of a Machine Learning-based Decision Support System for Stroke Rehabilitation Assessment [64.52563354823711]
  Rehabilitation assessment is critical to determine an adequate intervention for a patient. Current practices of assessment mainly rely on therapist's experience, and assessment is infrequently executed due to the limited availability of a therapist. We developed an intelligent decision support system that can identify salient features of assessment using reinforcement learning.
  arXiv  Detail & Related papers  (2020-02-27T17:04:07Z)

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.