Hospitalizations that follow closely on the heels of one or more emergency
department visits are often symptoms of missed opportunities to form a proper
diagnosis. These diagnostic errors imply a failure to recognize the need for
hospitalization and deliver appropriate care, and thus also bear important
connotations for patient safety. In this paper, we show how data mining
techniques can be applied to a large existing hospitalization data set to learn
useful models that predict these upcoming hospitalizations with high accuracy.
Specifically, we use an ensemble of logistics regression, na\"ive Bayes and
association rule classifiers to successfully predict hospitalization within 3,
7 and 14 days of an emergency department discharge. Aside from high accuracy,
one of the advantages of the techniques proposed here is that the resulting
classifier is easily inspected and interpreted by humans so that the learned
rules can be readily operationalized. These rules can then be easily
distributed and applied directly by physicians in emergency department settings
to predict the risk of early admission prior to discharging their emergency
department patients.

用数据挖掘技术分析医院数据集，构建高准确度的预测模型，帮助医生在急诊科病人出院前预测早期住院的风险。

预测急诊科出院后住院风险的升高

Predicting Elevated Risk of Hospitalization Following Emergency  Department Discharges

Many diagnostic errors occur because clinicians cannot easily access relevant
information in patient Electronic Health Records (EHRs). In this work we
propose a method to use LLMs to identify pieces of evidence in patient EHR data
that indicate increased or decreased risk of specific diagnoses; our ultimate
aim is to increase access to evidence and reduce diagnostic errors. In
particular, we propose a Neural Additive Model to make predictions backed by
evidence with individualized risk estimates at time-points where clinicians are
still uncertain, aiming to specifically mitigate delays in diagnosis and errors
stemming from an incomplete differential. To train such a model, it is
necessary to infer temporally fine-grained retrospective labels of eventual
"true" diagnoses. We do so with LLMs, to ensure that the input text is from
before a confident diagnosis can be made. We use an LLM to retrieve an initial
pool of evidence, but then refine this set of evidence according to
correlations learned by the model. We conduct an in-depth evaluation of the
usefulness of our approach by simulating how it might be used by a clinician to
decide between a pre-defined list of differential diagnoses.

使用 LLMs 方法识别患者电子健康记录中表明特定诊断风险增加或减少的证据，以提高证据获取并减少诊断错误。通过神经附加模型在临床医生不确定时点进行预测，具有个体化的风险估计，旨在减少诊断延误和因不完整鉴别引起的错误。使用 LLMs 推断细粒度的过去真实诊断标签，确保输入文本在自信诊断之前。通过模型学习的相关性，从初始证据集中筛选出更精确的证据，通过模拟临床医生在预定义鉴别诊断列表中选择来详细评估我们方法的实用性。