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Some of the activities encompassed in this category include the practice of business profiling, the implementation of accountability care systems, and the adjustment of capitated scientifically pricing models using patient spending estimates. The present systems in use have significant challenges related to their predictive capabilities and data requirements. These approaches heavily depend on manually constructed models based on point and linear regression techniques. This article presents a multi-view deep learning system that use past claims data to forecast individual-level healthcare expenses. Our multi-view technique is capable of successfully representing several types of data, including patient demographics, clinical codes, drug execution, and facility utilization. Budget prediction study was conducted with data obtained from an authentic paediatric database. The strategy we offer demonstrates superior performance compared to all baseline methods in accurately estimating research expenditures, as supported by empirical evidence from the obtained data. The results of this research enhance the advancement of healthcare by enhancing approaches for both preventative and curative measures.

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Assessment on Deep Learning Framework for Forecasting Patient Expenditure in Healthcare Sector

  • 会议论文

  • 发表日期：

    2025年

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  • K. Srinivas,
  • Mudimela Madhusudhan,
  • G. Mallikarjun,
  • M. Shiva Kumar,
  • Chandrugonda Malleshwar Rao,
  • Botcha Kishore Kumar

摘要

Some of the activities encompassed in this category include the practice of business profiling, the implementation of accountability care systems, and the adjustment of capitated scientifically pricing models using patient spending estimates. The present systems in use have significant challenges related to their predictive capabilities and data requirements. These approaches heavily depend on manually constructed models based on point and linear regression techniques. This article presents a multi-view deep learning system that use past claims data to forecast individual-level healthcare expenses. Our multi-view technique is capable of successfully representing several types of data, including patient demographics, clinical codes, drug execution, and facility utilization. Budget prediction study was conducted with data obtained from an authentic paediatric database. The strategy we offer demonstrates superior performance compared to all baseline methods in accurately estimating research expenditures, as supported by empirical evidence from the obtained data. The results of this research enhance the advancement of healthcare by enhancing approaches for both preventative and curative measures.