Objective: To investigate GPT-3.5 in generating and coding medical documents
with ICD-10 codes for data augmentation on low-resources labels.
Materials and Methods: Employing GPT-3.5 we generated and coded 9,606
discharge summaries based on lists of ICD-10 code descriptions of patients with
infrequent (generation) codes within the MIMIC-IV dataset. Combined with the
baseline training set, this formed an augmented training set. Neural coding
models were trained on baseline and augmented data and evaluated on a MIMIC-IV
test set. We report micro- and macro-F1 scores on the full codeset, generation
codes, and their families. Weak Hierarchical Confusion Matrices were employed
to determine within-family and outside-of-family coding errors in the latter
codesets. The coding performance of GPT-3.5 was evaluated both on prompt-guided
self-generated data and real MIMIC-IV data. Clinical professionals evaluated
the clinical acceptability of the generated documents.
Results: Augmentation slightly hinders the overall performance of the models
but improves performance for the generation candidate codes and their families,
including one unseen in the baseline training data. Augmented models display
lower out-of-family error rates. GPT-3.5 can identify ICD-10 codes by the
prompted descriptions, but performs poorly on real data. Evaluators note the
correctness of generated concepts while suffering in variety, supporting
information, and narrative.
Discussion and Conclusion: GPT-3.5 alone is unsuitable for ICD-10 coding.
Augmentation positively affects generation code families but mainly benefits
codes with existing examples. Augmentation reduces out-of-family errors.
Discharge summaries generated by GPT-3.5 state prompted concepts correctly but
lack variety, and authenticity in narratives. They are unsuitable for clinical
practice.

GPT-3.5 在生成和编码医疗文件中的应用进行了调查，以进行数据增强和低资源标签的 ICD-10 编码。

GPT-3.5 能否生成和编码出院小结？

Can GPT-3.5 Generate and Code Discharge Summaries?

In medical documents, it is possible that an entity of interest not only
contains a discontiguous sequence of words but also overlaps with another
entity. Entities of such structures are intrinsically hard to recognize due to
the large space of possible entity combinations. In this work, we propose a
neural two-stage approach to recognize discontiguous and overlapping entities
by decomposing this problem into two subtasks: 1) it first detects all the
overlapping spans that either form entities on their own or present as segments
of discontiguous entities, based on the representation of segmental hypergraph,
2) next it learns to combine these segments into discontiguous entities with a
classifier, which filters out other incorrect combinations of segments. Two
neural components are designed for these subtasks respectively and they are
learned jointly using a shared encoder for text. Our model achieves the
state-of-the-art performance in a standard dataset, even in the absence of
external features that previous methods used.

本文提出了一种神经网络模型来识别医学文档中具有不连续序列和重叠结构的实体，通过将这个问题分解成两个子任务：检测所有重叠跨度，并将这些跨度组合成不连续的实体。使用两个神经组件学习这些子任务的过程，并能够在标准数据集上实现最先进的性能。