Early diagnosis of diseases holds the potential for deep transformation in
healthcare by enabling better treatment options, improving long-term survival
and quality of life, and reducing overall cost. With the advent of medical big
data, advances in diagnostic tests as well as in machine learning and
statistics, early or timely diagnosis seems within reach. Early diagnosis
research often neglects the potential for optimizing individual diagnostic
paths. To enable personalized early diagnosis, a foundational framework is
needed that delineates the diagnosis process and systematically identifies the
time-dependent value of various diagnostic tests for an individual patient
given their unique characteristics. Here, we propose the first foundational
framework for early and timely diagnosis. It builds on decision-theoretic
approaches to outline the diagnosis process and integrates machine learning and
statistical methodology for estimating the optimal personalized diagnostic
path. To describe the proposed framework as well as possibly other frameworks,
we provide essential definitions.
The development of a foundational framework is necessary for several reasons:
1) formalism provides clarity for the development of decision support tools; 2)
observed information can be complemented with estimates of the future patient
trajectory; 3) the net benefit of counterfactual diagnostic paths and
associated uncertainties can be modeled for individuals 4) 'early' and 'timely'
diagnosis can be clearly defined; 5) a mechanism emerges for assessing the
value of technologies in terms of their impact on personalized early diagnosis,
resulting health outcomes and incurred costs.
Finally, we hope that this foundational framework will unlock the
long-awaited potential of timely diagnosis and intervention, leading to
improved outcomes for patients and higher cost-effectiveness for healthcare
systems.

早期诊断是医疗保健领域重大转型潜力，通过提供更好的治疗选择、改善长期生存率和生活质量，并减少总体成本。本文提出了一种早期和及时诊断的基础框架，以个性化诊断为基础，通过决策理论方法和机器学习统计方法来估计最佳个性化诊断路径。此框架的发展将有助于实现及时诊断和干预的潜力，并提高患者的治疗结果和医疗系统的成本效益。