Ziqi Xu, Jingwen Zhang, Jacob K Greenberg, Madelyn Frumkin, Saad Javeed, Justin K. Zhang, Braeden Benedict, Kathleen Botterbush, Thomas L. Rodebaugh, Wilson Z. Ray, Chenyang Lu
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In recent years, the emergence of smartphones and wearable devices has presented new opportunities to capture longitudinal and dynamic information regarding patients' conditions outside the hospital. This paper proposes a novel machine learning approach, Multi-Modal Multi-Task Learning (M3TL), using smartphones and wristbands to predict multiple surgical outcomes after lumbar spine surgeries. We formulate the prediction of pain interference, physical function, and quality of recovery as a multi-task learning (MTL) problem. We leverage multi-modal data to capture the static and dynamic characteristics of patients, including (1) traditional features from PROMs and Electronic Health Records (EHR), (2) Ecological Momentary Assessment (EMA) collected from smartphones, and (3) sensing data from wristbands. Moreover, we introduce new features derived from the correlation of EMA and wearable features measured within the same time frame, effectively enhancing predictive performance by capturing the interdependencies between the two data modalities. Our model interpretation uncovers the complementary nature of the different data modalities and their distinctive contributions toward multiple surgical outcomes. Furthermore, through individualized decision analysis, our model identifies personal high risk factors to aid clinical decision making and approach personalized treatments. In a clinical study involving 122 patients undergoing lumbar spine surgery, our M3TL model outperforms a diverse set of baseline methods in predictive performance, demonstrating the value of integrating multi-modal data and learning from multiple surgical outcomes. 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引用次数: 0
摘要
术前预测患者的术后恢复情况对于临床决策和个性化治疗至关重要,尤其是腰椎手术,因为患者的术后恢复情况千差万别。现有的预测工具主要依赖于传统的患者报告结果指标(PROMs),而这些指标无法捕捉到患者术前病情的长期动态变化。此外,现有研究侧重于预测单一的手术结果。然而,脊柱手术后的恢复是多维度的,包括疼痛干扰、身体功能和恢复质量等多个不同但相互关联的结果。近年来,智能手机和可穿戴设备的出现为在医院外获取有关患者病情的纵向动态信息提供了新的机遇。本文提出了一种新颖的机器学习方法--多模态多任务学习(M3TL),利用智能手机和腕带来预测腰椎手术后的多种手术结果。我们将疼痛干扰、身体功能和恢复质量的预测制定为一个多任务学习(MTL)问题。我们利用多模态数据来捕捉患者的静态和动态特征,包括:(1)来自PROM和电子健康记录(EHR)的传统特征;(2)从智能手机收集的生态瞬间评估(EMA);以及(3)来自腕带的传感数据。此外,我们还引入了在同一时间段内测量的 EMA 和可穿戴设备特征的相关性所产生的新特征,通过捕捉两种数据模式之间的相互依存关系,有效提高了预测性能。我们的模型解释揭示了不同数据模式的互补性及其对多种手术结果的独特贡献。此外,通过个性化决策分析,我们的模型还能识别个人高风险因素,以帮助临床决策和个性化治疗。在一项涉及 122 名腰椎手术患者的临床研究中,我们的 M3TL 模型在预测性能方面优于各种基线方法,证明了整合多模态数据并从多种手术结果中学习的价值。这项工作有助于通过对多维结果进行准确的术前预测来推进个性化围手术期护理。
Predicting Multi-dimensional Surgical Outcomes with Multi-modal Mobile Sensing
Pre-operative prediction of post-surgical recovery for patients is vital for clinical decision-making and personalized treatments, especially with lumbar spine surgery, where patients exhibit highly heterogeneous outcomes. Existing predictive tools mainly rely on traditional Patient-Reported Outcome Measures (PROMs), which fail to capture the long-term dynamics of patient conditions before the surgery. Moreover, existing studies focus on predicting a single surgical outcome. However, recovery from spine surgery is multi-dimensional, including multiple distinctive but interrelated outcomes, such as pain interference, physical function, and quality of recovery. In recent years, the emergence of smartphones and wearable devices has presented new opportunities to capture longitudinal and dynamic information regarding patients' conditions outside the hospital. This paper proposes a novel machine learning approach, Multi-Modal Multi-Task Learning (M3TL), using smartphones and wristbands to predict multiple surgical outcomes after lumbar spine surgeries. We formulate the prediction of pain interference, physical function, and quality of recovery as a multi-task learning (MTL) problem. We leverage multi-modal data to capture the static and dynamic characteristics of patients, including (1) traditional features from PROMs and Electronic Health Records (EHR), (2) Ecological Momentary Assessment (EMA) collected from smartphones, and (3) sensing data from wristbands. Moreover, we introduce new features derived from the correlation of EMA and wearable features measured within the same time frame, effectively enhancing predictive performance by capturing the interdependencies between the two data modalities. Our model interpretation uncovers the complementary nature of the different data modalities and their distinctive contributions toward multiple surgical outcomes. Furthermore, through individualized decision analysis, our model identifies personal high risk factors to aid clinical decision making and approach personalized treatments. In a clinical study involving 122 patients undergoing lumbar spine surgery, our M3TL model outperforms a diverse set of baseline methods in predictive performance, demonstrating the value of integrating multi-modal data and learning from multiple surgical outcomes. This work contributes to advancing personalized peri-operative care with accurate pre-operative predictions of multi-dimensional outcomes.