Electronic Health Records (EHR) serve as a valuable source of patient
information, offering insights into medical histories, treatments, and
outcomes. Previous research has developed systems for detecting applicable ICD
codes that should be assigned while writing a given EHR document, mainly
focusing on discharge summaries written at the end of a hospital stay. In this
work, we investigate the potential of predicting these codes for the whole
patient stay at different time points during their stay, even before they are
officially assigned by clinicians. The development of methods to predict
diagnoses and treatments earlier in advance could open opportunities for
predictive medicine, such as identifying disease risks sooner, suggesting
treatments, and optimizing resource allocation. Our experiments show that
predictions regarding final ICD codes can be made already two days after
admission and we propose a custom model that improves performance on this early
prediction task.

通过研究，我们发现在入院后两天就可以预测最终的 ICD 代码，并且我们提出了一种定制模型，能够在这一早期预测任务上提高性能。

患者健康记录中临床笔记和 ICD 码的连续预测建模

Continuous Predictive Modeling of Clinical Notes and ICD Codes in  Patient Health Records

Personalized predictive medicine necessitates the modeling of patient illness
and care processes, which inherently have long-term temporal dependencies.
Healthcare observations, recorded in electronic medical records, are episodic
and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural
network that reads medical records, stores previous illness history, infers
current illness states and predicts future medical outcomes. At the data level,
DeepCare represents care episodes as vectors in space, models patient health
state trajectories through explicit memory of historical records. Built on Long
Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle
irregular timed events by moderating the forgetting and consolidation of memory
cells. DeepCare also incorporates medical interventions that change the course
of illness and shape future medical risk. Moving up to the health state level,
historical and present health states are then aggregated through multiscale
temporal pooling, before passing through a neural network that estimates future
outcomes. We demonstrate the efficacy of DeepCare for disease progression
modeling, intervention recommendation, and future risk prediction. On two
important cohorts with heavy social and economic burden -- diabetes and mental
health -- the results show improved modeling and risk prediction accuracy.

本文介绍了一种名为 DeepCare 的深度动态神经网络，用于预测医疗结果，其基于医疗记录建模，包括以往病史、当前病情以及将来的分析，表现出了良好的疾病进展建模、干预建议和风险预测的效果。