儿童青少年心理健康中患者轨迹与障碍共现的可视化
Visualizing Patient Trajectories and Disorder Co-occurrences in Child and Adolescent Mental Health
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中文总结 AI 辅助
该研究基于35年CAMHS数据构建患者轨迹与ADHD共现可视化工具，经临床医生评估有用，可辅助理解儿童青少年心理健康诊疗模式与决策。
中文摘要 AI 辅助
理解患者轨迹并识别诊疗阶段的模式对有效的医疗决策至关重要。我们提出一种患者时间线可视化方法，该方法基于超过35年的儿童青少年心理健康服务（CAMHS）数据生成的聚类诊疗阶段构建。为对相似患者进行分组，我们根据三个特征将患者分为12组：首次诊疗阶段开始时的年龄组（学龄前儿童、童年中期、青少年）、性别，以及是否存在注意缺陷多动障碍（ADHD）。该轨迹中展示了患者、带人口统计学信息的时间线以及诊疗阶段信息，以帮助理解患者及相关事件，从而观察时间模式与变化。这些图揭示了不同组间诊疗需求与模式的异同：未患ADHD的女性随年龄增长，诊疗阶段数量稳步增加；患ADHD的女性及所有男性在童年中期的诊疗阶段数量达到峰值，随后在青少年时期下降。为比较和理解不同组间ADHD的强度及共现障碍，我们绘制了ADHD共现图，图中显示抽动秽语综合征在所有年龄组中均为主要共现障碍。我们在临床医生的参与下对可视化结果进行了评估与优化，临床医生认为这些可视化工具有助于理解CAMHS诊疗的背景。这些可视化工具使电子健康记录（EHR）中的人群数据可用于决策制定，增强对不同组间诊疗及障碍模式的理解。
英文摘要
Understanding patient trajectories and identifying patterns in episodes of care is critical for effective healthcare decision-making. We present a patient timeline visualization using clustered episodes of care derived from over 35 years of Child and Adolescent Mental Health Services (CAMHS) data. Patients were categorized into 12 groups based on three features: age group (preschoolers, middle childhood, teenagers) at the start of the first episode, gender, and presence or absence of Attention-Deficit Hyperactivity Disorder (ADHD), in order to group similar patients. The patients, timeline with demographics, and episode of care information are displayed in the trajectory to facilitate understanding of the patient and associated events, allowing observation of temporal patterns and variations. These plots reveal similarities and differences in care needs and patterns across groups. Females without ADHD have a steady increase in the number of episodes of care with age. Females with ADHD and all males experienced a peak in the number of episodes during middle childhood, followed by a decline in the teenage years. To compare and understand the intensity and co-occurring disorders with ADHD across different groups, we plotted an ADHD co-occurrence graph, and Tourette's syndrome was co-occurring predominantly in all age groups. We evaluated and refined our visualizations with the involvement of clinicians, who found them useful for understanding the context of CAMHS care. These visual tools make the population data in the Electronic Health Records (EHR) available for decision-making and enhancing the understanding of care and disorder patterns across groups.
