Due to the steady rise in population demographics and longevity, emergency department visits are increasing across North America. As more patients visit the emergency department, traditional clinical workflows become overloaded and inefficient, leading to prolonged wait-times and reduced healthcare quality. One of such workflows is the triage medical directive, impeded by limited human workload, inaccurate diagnoses and invasive over-testing. To address this issue, we propose TriNet: a machine learning model for medical directives that automates first-line screening at triage for conditions requiring downstream testing for diagnosis confirmation. To verify screening potential, TriNet was trained on hospital triage data and achieved high positive predictive values in detecting pneumonia (0.86) and urinary tract infection (0.93). These models outperform current clinical benchmarks, indicating that machine-learning medical directives can offer cost-free, non-invasive screening with high specificity for common conditions, reducing the risk of over-testing while increasing emergency department efficiency.

鉴于人口统计数据和寿命的稳定增长，北美的急诊科就诊人次正在增加。由于病人访问急诊科增多，传统的临床工作流程变得过载和低效，导致等待时间延长和医疗质量降低。针对这个问题，我们提出了TriNet：一种用于医疗指导的机器学习模型，可以自动化对需要进行下游诊断确认的情况进行三级筛查。通过对医院急诊科数据的训练，TriNet在检测肺炎（0.86）和尿路感染（0.93）方面达到了高的阳性预测值。这些模型优于目前的临床基准，表明机器学习医疗指导可以提供成本免费、非侵入性的对常见疾病进行高特异性筛查，减少过度检测的风险，同时提高急诊科的效率。

应用TriNet在分诊过程中对肺炎和尿路感染进行筛查