In recent advancements in proton therapy, MR-based treatment planning is
gaining momentum to minimize additional radiation exposure compared to
traditional CT-based methods. This transition highlights the critical need for
accurate MR-to-CT image synthesis, which is essential for precise proton dose
calculations. Our research introduces the Diffusion Schr\"odinger Bridge Models
(DSBM), an innovative approach for high-quality MR-to-CT synthesis. DSBM learns
the nonlinear diffusion processes between MR and CT data distributions. This
method improves upon traditional diffusion models by initiating synthesis from
the prior distribution rather than the Gaussian distribution, enhancing both
generation quality and efficiency. We validated the effectiveness of DSBM on a
head and neck cancer dataset, demonstrating its superiority over traditional
image synthesis methods through both image-level and dosimetric-level
evaluations. The effectiveness of DSBM in MR-based proton treatment planning
highlights its potential as a valuable tool in various clinical scenarios.

最近在质子治疗的进展中，基于磁共振的治疗计划正逐渐得到认可，以降低与传统基于 CT 的方法相比的额外辐射暴露。这种转变凸显了精确的磁共振与 CT 图像合成的关键性需求，对于精确的质子剂量计算至关重要。本研究引入了扩散薛定谔桥模型（DSBM），这是一种创新的高质量磁共振与 CT 图像合成方法。DSBM 学习了 MR 和 CT 数据分布之间的非线性扩散过程。该方法通过从先验分布而非高斯分布开始合成，提高了传统扩散模型的生成质量和效率。我们通过头颈癌数据集验证了 DSBM 的有效性，并通过图像级和剂量级评估证明了其优于传统图像合成方法。DSBM 在基于磁共振的质子治疗计划中的有效性突出了其在各种临床场景中作为有价值工具的潜力。

用于头颈部质子治疗计划的高质量 MR 到 CT 综合的扩散薛定谔桥模型

Diffusion Schrödinger Bridge Models for High-Quality MR-to-CT  Synthesis for Head and Neck Proton Treatment Planning

Large Language models (LLMs) have demonstrated significant potential in
transforming healthcare by automating tasks such as clinical documentation,
information retrieval, and decision support. In this aspect, carefully
engineered prompts have emerged as a powerful tool for using LLMs for medical
scenarios, e.g., patient clinical scenarios. In this paper, we propose a
modified version of the MedQA-USMLE dataset, which is subjective, to mimic
real-life clinical scenarios. We explore the Chain of Thought (CoT) reasoning
based on subjective response generation for the modified MedQA-USMLE dataset
with appropriate LM-driven forward reasoning for correct responses to the
medical questions. Keeping in mind the importance of response verification in
the medical setting, we utilize a reward training mechanism whereby the
language model also provides an appropriate verified response for a particular
response to a clinical question. In this regard, we also include
human-in-the-loop for different evaluation aspects. We develop better
in-contrast learning strategies by modifying the 5-shot-codex-CoT-prompt from
arXiv:2207.08143 for the subjective MedQA dataset and developing our
incremental-reasoning prompt. Our evaluations show that the incremental
reasoning prompt performs better than the modified codex prompt in certain
scenarios. We also show that greedy decoding with the incremental reasoning
method performs better than other strategies, such as prompt chaining and
eliminative reasoning.

使用大型语言模型（LLMs）自动化医疗任务，如临床记录、信息检索和决策支持。我们提出一种修改的 MedQA-USMLE 数据集，用于模拟真实的临床场景，并探索基于思维链（CoT）推理的主观响应生成，以获取正确的医疗问题答案。我们还利用回报训练机制和人类参与，开发了更好的对比学习策略，并通过增量推理的提示得出贪婪解码方法比其他策略更好的结论。

基于有限样本的思维链驱动推理用于开放式医学问题回答的 LLMs

Few shot chain-of-thought driven reasoning to prompt LLMs for open ended  medical question answering

Predictive modeling with electronic health record (EHR) data is anticipated
to drive personalized medicine and improve healthcare quality. Constructing
predictive statistical models typically requires extraction of curated
predictor variables from normalized EHR data, a labor-intensive process that
discards the vast majority of information in each patient's record. We propose
a representation of patients' entire, raw EHR records based on the Fast
Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep
learning methods using this representation are capable of accurately predicting
multiple medical events from multiple centers without site-specific data
harmonization. We validated our approach using de-identified EHR data from two
U.S. academic medical centers with 216,221 adult patients hospitalized for at
least 24 hours. In the sequential format we propose, this volume of EHR data
unrolled into a total of 46,864,534,945 data points, including clinical notes.
Deep learning models achieved high accuracy for tasks such as predicting
in-hospital mortality (AUROC across sites 0.93-0.94), 30-day unplanned
readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and
all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90).
These models outperformed state-of-the-art traditional predictive models in all
cases. We also present a case-study of a neural-network attribution system,
which illustrates how clinicians can gain some transparency into the
predictions. We believe that this approach can be used to create accurate and
scalable predictions for a variety of clinical scenarios, complete with
explanations that directly highlight evidence in the patient's chart.

使用电子健康记录进行循证医学和准确预测，通过 Fast Healthcare Interoperability Resources (FHIR) 格式表示病人的原始病历记录，并使用深度学习方法进行多中心的多个医疗事件预测，包括住院死亡率、未经计划的 30 天再入院率、医院停留时间延长、所有患者的最终出院诊断等。