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Dementia is a progressive neurological condition marked by cognitive decline affecting memory, thinking, and behavior. It poses significant challenges for patients, caregivers, and healthcare systems. As individuals age, the risk of developing dementia increases. This marks the urgent need for solutions that integrate medical expertise with technological advancements. The Dementia Application and Diagnosis and Tracking (DADT) app serves as a comprehensive platform connecting medical professionals with dementia patients, offering a range of tailored therapies. This paper introduces a hybrid recommender system embedded in the DADT app, combining collaborative, and demographic filtering techniques. Utilizing patient electronic health records (EHR) and app engagement data, the system aims to provide personalized therapy recommendations, reducing medical professional’s workload and enhancing patient care. Preliminary results showcase the system’s efficacy in generating personalized recommendations, with improved precision and recall over demographic filtering alone, and competitive performance compared to collaborative filtering. This highlights its potential to optimize dementia care workflows and enhance patient outcomes.

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The Rehab Reco—A Therapy Recommender System for Dementia

  • 会议论文

  • 发表日期：

    2025年

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  • Pritish Pore,
  • Sharvari Bhagwat,
  • Yash Desai,
  • Prutha Rinke,
  • Arati Deshpande,
  • Soubhik Das,
  • Pushkraj Marne

摘要

Dementia is a progressive neurological condition marked by cognitive decline affecting memory, thinking, and behavior. It poses significant challenges for patients, caregivers, and healthcare systems. As individuals age, the risk of developing dementia increases. This marks the urgent need for solutions that integrate medical expertise with technological advancements. The Dementia Application and Diagnosis and Tracking (DADT) app serves as a comprehensive platform connecting medical professionals with dementia patients, offering a range of tailored therapies. This paper introduces a hybrid recommender system embedded in the DADT app, combining collaborative, and demographic filtering techniques. Utilizing patient electronic health records (EHR) and app engagement data, the system aims to provide personalized therapy recommendations, reducing medical professional’s workload and enhancing patient care. Preliminary results showcase the system’s efficacy in generating personalized recommendations, with improved precision and recall over demographic filtering alone, and competitive performance compared to collaborative filtering. This highlights its potential to optimize dementia care workflows and enhance patient outcomes.