A Deployed Online Reinforcement Learning Algorithm In An Oral Health Clinical Trial
- URL: http://arxiv.org/abs/2409.02069v2
- Date: Wed, 18 Dec 2024 23:01:54 GMT
- Title: A Deployed Online Reinforcement Learning Algorithm In An Oral Health Clinical Trial
- Authors: Anna L. Trella, Kelly W. Zhang, Hinal Jajal, Inbal Nahum-Shani, Vivek Shetty, Finale Doshi-Velez, Susan A. Murphy,
- Abstract summary: Dental disease is a chronic condition associated with substantial financial burden, personal suffering, and increased risk of systemic diseases.
Despite widespread recommendations for twice-daily tooth brushing, adherence to recommended oral self-care behaviors remains sub-optimal due to factors such as forgetfulness and disengagement.
We developed Oralytics, a mHealth intervention system designed to complement clinician-delivered preventative care for marginalized individuals at risk for dental disease.
- Score: 20.944037982124037
- License:
- Abstract: Dental disease is a prevalent chronic condition associated with substantial financial burden, personal suffering, and increased risk of systemic diseases. Despite widespread recommendations for twice-daily tooth brushing, adherence to recommended oral self-care behaviors remains sub-optimal due to factors such as forgetfulness and disengagement. To address this, we developed Oralytics, a mHealth intervention system designed to complement clinician-delivered preventative care for marginalized individuals at risk for dental disease. Oralytics incorporates an online reinforcement learning algorithm to determine optimal times to deliver intervention prompts that encourage oral self-care behaviors. We have deployed Oralytics in a registered clinical trial. The deployment required careful design to manage challenges specific to the clinical trials setting in the U.S. In this paper, we (1) highlight key design decisions of the RL algorithm that address these challenges and (2) conduct a re-sampling analysis to evaluate algorithm design decisions. A second phase (randomized control trial) of Oralytics is planned to start in spring 2025.
Related papers
- Oralytics Reinforcement Learning Algorithm [5.54328512723076]
Dental disease is one of the most common chronic diseases in the United States.
We have developed Oralytics, an online, reinforcement learning (RL) algorithm that optimize the delivery of personalized intervention prompts to improve oral self-care (OSCB)
The finalized RL algorithm was deployed in the Oralytics clinical trial, conducted from fall 2023 to summer 2024.
arXiv Detail & Related papers (2024-06-19T00:44:11Z) - Monitoring Fidelity of Online Reinforcement Learning Algorithms in Clinical Trials [20.944037982124037]
This paper proposes algorithm fidelity as a critical requirement for deploying online RL algorithms in clinical trials.
We present a framework for pre-deployment planning and real-time monitoring to help algorithm developers and clinical researchers ensure algorithm fidelity.
arXiv Detail & Related papers (2024-02-26T20:19:14Z) - RL-Based Guidance in Outpatient Hysteroscopy Training: A Feasibility
Study [4.614579113754949]
This work presents an RL-based agent for outpatient hysteroscopy training.
Recent advancements enabled performing this type of intervention in the outpatient setup without anaesthesia.
While being beneficial to the patient, this approach introduces new challenges for clinicians, who should take additional measures to maintain the level of patient comfort and prevent tissue damage.
arXiv Detail & Related papers (2022-11-26T11:16:17Z) - Automated Fidelity Assessment for Strategy Training in Inpatient
Rehabilitation using Natural Language Processing [53.096237570992294]
Strategy training is a rehabilitation approach that teaches skills to reduce disability among those with cognitive impairments following a stroke.
Standardized fidelity assessment is used to measure adherence to treatment principles.
We developed a rule-based NLP algorithm, a long-short term memory (LSTM) model, and a bidirectional encoder representation from transformers (BERT) model for this task.
arXiv Detail & Related papers (2022-09-14T15:33:30Z) - Reward Design For An Online Reinforcement Learning Algorithm Supporting
Oral Self-Care [24.283342018185028]
Dental disease is one of the most common chronic diseases despite being largely preventable.
We develop an online reinforcement learning (RL) algorithm for use in optimizing the delivery of mobile-based prompts to encourage oral hygiene behaviors.
The RL algorithm discussed in this paper will be deployed in Oralytics, an oral self-care app that provides behavioral strategies to boost patient engagement in oral hygiene practices.
arXiv Detail & Related papers (2022-08-15T18:47:09Z) - Adaptive Identification of Populations with Treatment Benefit in
Clinical Trials: Machine Learning Challenges and Solutions [78.31410227443102]
We study the problem of adaptively identifying patient subpopulations that benefit from a given treatment during a confirmatory clinical trial.
We propose AdaGGI and AdaGCPI, two meta-algorithms for subpopulation construction.
arXiv Detail & Related papers (2022-08-11T14:27:49Z) - Learning Hierarchical Attention for Weakly-supervised Chest X-Ray
Abnormality Localization and Diagnosis [28.747482895051103]
deep learning has driven much recent progress in medical imaging, but many clinical challenges are not fully addressed.
One potential way to address this problem is to further train these models to localize abnormalities in addition to just classifying them.
In this work, we take a step towards addressing these issues by means of a new attention-driven weakly supervised algorithm.
arXiv Detail & Related papers (2021-12-23T04:12:51Z) - The Medkit-Learn(ing) Environment: Medical Decision Modelling through
Simulation [81.72197368690031]
We present a new benchmarking suite designed specifically for medical sequential decision making.
The Medkit-Learn(ing) Environment is a publicly available Python package providing simple and easy access to high-fidelity synthetic medical data.
arXiv Detail & Related papers (2021-06-08T10:38:09Z) - Resource Planning for Hospitals Under Special Consideration of the
COVID-19 Pandemic: Optimization and Sensitivity Analysis [87.31348761201716]
Crises like the COVID-19 pandemic pose a serious challenge to health-care institutions.
BaBSim.Hospital is a tool for capacity planning based on discrete event simulation.
We aim to investigate and optimize these parameters to improve BaBSim.Hospital.
arXiv Detail & Related papers (2021-05-16T12:38:35Z) - Inheritance-guided Hierarchical Assignment for Clinical Automatic
Diagnosis [50.15205065710629]
Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making.
We propose a novel framework to combine the inheritance-guided hierarchical assignment and co-occurrence graph propagation for clinical automatic diagnosis.
arXiv Detail & Related papers (2021-01-27T13:16:51Z) - Contextual Constrained Learning for Dose-Finding Clinical Trials [102.8283665750281]
C3T-Budget is a contextual constrained clinical trial algorithm for dose-finding under both budget and safety constraints.
It recruits patients with consideration of the remaining budget, the remaining time, and the characteristics of each group.
arXiv Detail & Related papers (2020-01-08T11:46:48Z)
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.