We aimed to investigate the impact of social circumstances on cancer therapy
selection using natural language processing to derive insights from social
worker documentation. We developed and employed a Bidirectional Encoder
Representations from Transformers (BERT) based approach, using a hierarchical
multi-step BERT model (BERT-MS) to predict the prescription of targeted cancer
therapy to patients based solely on documentation by clinical social workers.
Our corpus included free-text clinical social work notes, combined with
medication prescription information, for all patients treated for breast
cancer. We conducted a feature importance analysis to pinpoint the specific
social circumstances that impact cancer therapy selection. Using only social
work notes, we consistently predicted the administration of targeted therapies,
suggesting systematic differences in treatment selection exist due to
non-clinical factors. The UCSF-BERT model, pretrained on clinical text at UCSF,
outperformed other publicly available language models with an AUROC of 0.675
and a Macro F1 score of 0.599. The UCSF BERT-MS model, capable of leveraging
multiple pieces of notes, surpassed the UCSF-BERT model in both AUROC and
Macro-F1. Our feature importance analysis identified several clinically
intuitive social determinants of health (SDOH) that potentially contribute to
disparities in treatment. Our findings indicate that significant disparities
exist among breast cancer patients receiving different types of therapies based
on social determinants of health. Social work reports play a crucial role in
understanding these disparities in clinical decision-making.

研究使用自然语言处理探究社会因素对癌症治疗选择的影响，发现社会工作者的记录在预测靶向治疗中起着重要作用，并发现许多与临床决策 - making 有关的健康社会决定因素可能会对治疗差异造成贡献。

通过社会工作笔记的自然语言处理揭示社会环境对癌症治疗选择的影响

Revealing the impact of social circumstances on the selection of cancer  therapy through natural language processing of social work notes

Large-scale comparisons between the poetry of Tang and Song dynasties shed
light on how words, collocations, and expressions were used and shared among
the poets. That some words were used only in the Tang poetry and some only in
the Song poetry could lead to interesting research in linguistics. That the
most frequent colors are different in the Tang and Song poetry provides a trace
of the changing social circumstances in the dynasties. Results of the current
work link to research topics of lexicography, semantics, and social
transitions. We discuss our findings and present our algorithms for efficient
comparisons among the poems, which are crucial for completing billion times of
comparisons within acceptable time.

通过大规模比较唐宋诗歌，揭示诗人之间如何使用和分享词汇、搭配和表达方式，研究唐宋诗歌中词汇的使用情况及背后的语言学现象、社会情境变化，为词典学、语义学和社会转型提供参考。