Abstract：
Evaluating the rehabilitation status of individuals with serious mental illnesses (SMI) necessitates a comprehensive analysis of multimodal data, including unstructured text records and structured diagnostic data. However, progress in the effective assessment of rehabilitation status remains limited. Our study develops a deep learning model integrating Bidirectional Encoder Representations from Transformers (BERT) and TabNet through a late fusion strategy to enhance rehabilitation prediction, including referral risk, dangerous behaviors, self-awareness, and medication adherence, in patients with SMI. BERT processes unstructured textual data, such as doctor\'s notes, whereas TabNet manages structured diagnostic information. The model\'s interpretability function serves to assist healthcare professionals in understanding the model\'s predictive decisions, improving patient care. Our model exhibited excellent predictive performance for all four tasks, with an accuracy exceeding 0.78 and an area under the curve of 0.70. In addition, a series of tests proved the model\'s robustness, fairness, and interpretability. This study combines multimodal and multitask learning strategies into a model and applies it to rehabilitation assessment tasks, offering a promising new tool that can be seamlessly integrated with the clinical workflow to support the provision of optimized patient care.
摘要：
评估患有严重精神疾病（SMI）的个人的康复状况需要对多模态数据进行全面分析，包括非结构化文本记录和结构化诊断数据。然而,有效评估康复状况的进展仍然有限。我们的研究开发了一种深度学习模型，通过后期融合策略将变形金刚（BERT）和TabNet的双向编码器表示集成，以增强康复预测。包括转诊风险，危险的行为,自我意识,和药物依从性，SMI患者。BERT处理非结构化文本数据，如医生的笔记，而TabNet管理结构化的诊断信息。该模型的可解释性功能可帮助医疗保健专业人员理解模型的预测性决策，改善患者护理。我们的模型对所有四个任务都表现出出色的预测性能，精度超过0.78，曲线下面积为0.70。此外,一系列测试证明了模型的鲁棒性，公平,和可解释性。本研究将多模态和多任务学习策略结合到一个模型中，并将其应用于康复评估任务，提供了一种有前途的新工具，可以与临床工作流程无缝集成，以支持提供优化的患者护理。
