earticle

논문검색

Human-Machine Interaction Technology (HIT)

Prediction of Cognitive Ability Utilizing a Machine Learning approach based on Digital Therapeutics Log Data

초록

영어

Given the surge in the elderly population, and increasing in dementia cases, there is a growing interest in digital therapies that facilitate steady remote treatment. However, in the cognitive assessment of digital therapies through clinical trials, the absence of log data as an essential evaluation factor is a significant issue. To address this, we propose a solution of utilizing weighted derived variables based on high-importance variables' accuracy in log data utilization as an indirect cognitive assessment factor for digital therapies. We have validated the effectiveness of this approach using machine learning techniques such as XGBoost, LGBM, and CatBoost. Thus, we suggest the use of log data as a rapid and indirect cognitive evaluation factor for digital therapy users.

목차

Abstract
1. INTRODUCTION
2. RELATED WORK
3. METHOD
3.1 Data Collection
3.2 Data Preprocessing
3.3 Modeling
3.4 Creation of CogScore, A New Derived Variable for Cognitive Ability
4. RESULTS AND DISCUSSION
5. CONCLUSION
ACKNOWLEDGEMENT
REFERENCES

저자정보

  • Yeojin Kim Master Student, Department of Computer Science and Engineering, Ewha Womans University, Seoul, Korea
  • Jiseon Yang AI Researcher, Department of Artificial Intelligence, Rowan, Korea
  • Dohyoung Rim Chief Technology Officer, Department of Artificial Intelligence, Rowan, Korea
  • Uran Oh Professor, Department of Computer Science and Engineering, Ewha Womans University, Seoul, Korea

참고문헌

자료제공 : 네이버학술정보

    함께 이용한 논문

      ※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.

      0개의 논문이 장바구니에 담겼습니다.