Temporal context in medicine is valuable in assessing key changes in patient health over time. We developed a machine learning framework to integrate diverse context from prior visits to improve health monitoring, especially when prior visits are limited and their frequency is variable. Our model first estimates initial risk of disease using medical data from the most recent patient visit, then refines this assessment using information digested from previously collected imaging and/or clinical biomarkers. We applied our framework to prostate cancer (PCa) risk prediction using data from a large population (28,342 patients, 39,013 magnetic resonance imaging scans, 68,931 blood tests) collected over nearly a decade. For predictions of the risk of clinically significant PCa at the time of the visit, integrating prior context directly converted false positives to true negatives, increasing overall specificity while preserving high sensitivity. False positive rates were reduced progressively from 51% to 33% when integrating information from up to three prior imaging examinations, as compared to using data from a single visit, and were further reduced to 24% when also including additional context from prior clinical data. For predicting the risk of PCa within five years of the visit, incorporating prior context reduced false positive rates still further (64% to 9%). Our findings show that information collected over time provides relevant context to enhance the specificity of medical risk prediction. For a wide range of progressive conditions, sufficient reduction of false positive rates using context could offer a pathway to expand longitudinal health monitoring programs to large populations with comparatively low baseline risk of disease, leading to earlier detection and improved health outcomes.

翻译：医学中的时间上下文对于评估患者健康状况随时间变化具有重要意义。我们开发了一个机器学习框架，用于整合既往就诊的多样化上下文信息以改善健康监测，特别是在既往就诊次数有限且频率不固定的情况下。我们的模型首先利用最近一次就诊的医疗数据估算疾病的初始风险，随后通过消化既往采集的影像学和/或临床生物标志物信息来优化该评估。我们将该框架应用于前列腺癌（PCa）风险预测，使用了近十年间收集的大规模人群数据（28,342名患者，39,013次磁共振成像扫描，68,931次血液检测）。对于就诊时临床显著性前列腺癌风险的预测，整合既往上下文信息直接将假阳性转化为真阴性，在保持高灵敏度的同时提高了整体特异性。与使用单次就诊数据相比，当整合多达三次既往影像学检查信息时，假阳性率从51%逐步降低至33%；当额外纳入既往临床数据的上下文信息后，假阳性率进一步降至24%。对于预测就诊后五年内前列腺癌风险，整合既往上下文信息使假阳性率降幅更为显著（从64%降至9%）。我们的研究结果表明，随时间收集的信息能为提升医疗风险预测的特异性提供相关上下文。对于广泛的进展性疾病，利用上下文信息充分降低假阳性率，可为将纵向健康监测项目扩展至基线疾病风险相对较低的大规模人群提供可行路径，从而实现早期检测并改善健康结局。