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Leveraging Big Data to Model the Likelihood of Developing Psychological Conditions After a Concussion

机译：利用大数据来模拟脑震荡后发展心理条件的可能性

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摘要

A concussion is an invisible and poorly understood mild traumatic brain injury (mTBI) that can alter the way the brain functions. Patients who have screened positive for mTBI are at an increased risk of depression, post-traumatic stress disorder (PTSD), headaches, sleep disorders, and other neurological and psychological problems. Early detection of psychological conditions such as PTSD following a concussion might improve the overall outcome of a patient and could potentially reduce the cost associated with intense interventions often required when conditions go untreated for a long time. Statistical and predictive models that leverage large-scale clinical repositories and use pre-existing conditions to determine the probability of a patient developing psychological conditions following a concussion have not been widely studied. This paper presents an SVM-based model that has been trained with a longitudinal dataset of over 5.3 million clinical encounters of 89,840 service members that have sustained a concussion. The model has been tested and validated with over 16,045 patients that developed PTSD and it has shown an accuracy of over 85% (AUC of 86.52%) at predicting the condition within the first year following the injury.
机译：脑震荡是一种看不见的，理解的轻度创伤性脑损伤（MTBI），可以改变大脑功能的方式。筛查MTBI阳性的患者处于抑郁风险，创伤后应激障碍（PTSD），头痛，睡眠障碍以及其他神经和心理问题。在脑震荡之后的早期发现PTSD等心理条件可能会改善患者的整体结果，并且可能会降低通常需要在长时间未经处理的情况下施加强烈干预措施的成本。统计和预测模型，利用大规模的临床储存库，并使用预先存在的条件来确定脑震荡后患者发育心理条件的概率尚未被广泛研究。本文介绍了一种基于SVM的模型，已培训，纵向数据集超过530万次临床遇到89,840个持续震荡的临床遭遇。该模型已经过测试，超过16,045例患者，发达国家PTSD验证，它已经在伤害后的第一年中预测的情形显示，超过85％（AUC的86.52％）的精度。

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