Composable Strategy Framework with Integrated Video-Text based Large Language Models for Heart Failure Assessment
This work addresses the unmet needs in heart failure management for patients and healthcare providers by offering a more holistic evaluation and optimized treatment plan, though it appears incremental as it builds on existing multi-modal AI approaches.
The paper tackled the problem of heart failure assessment by proposing a composable strategy framework that integrates video, text, and other data to simulate doctor-patient consultations, resulting in improved accuracy in prognosis prediction compared to single-modal AI algorithms.
Heart failure is one of the leading causes of death worldwide, with millons of deaths each year, according to data from the World Health Organization (WHO) and other public health agencies. While significant progress has been made in the field of heart failure, leading to improved survival rates and improvement of ejection fraction, there remains substantial unmet needs, due to the complexity and multifactorial characteristics. Therefore, we propose a composable strategy framework for assessment and treatment optimization in heart failure. This framework simulates the doctor-patient consultation process and leverages multi-modal algorithms to analyze a range of data, including video, physical examination, text results as well as medical history. By integrating these various data sources, our framework offers a more holistic evaluation and optimized treatment plan for patients. Our results demonstrate that this multi-modal approach outperforms single-modal artificial intelligence (AI) algorithms in terms of accuracy in heart failure (HF) prognosis prediction. Through this method, we can further evaluate the impact of various pathological indicators on HF prognosis,providing a more comprehensive evaluation.