This paper describes the results of SemEval 2023 task 7 -- Multi-Evidence
Natural Language Inference for Clinical Trial Data (NLI4CT) -- consisting of 2
tasks, a Natural Language Inference (NLI) task, and an evidence selection task
on clinical trial data. The proposed challenges require multi-hop biomedical
and numerical reasoning, which are of significant importance to the development
of systems capable of large-scale interpretation and retrieval of medical
evidence, to provide personalized evidence-based care.
Task 1, the entailment task, received 643 submissions from 40 participants,
and Task 2, the evidence selection task, received 364 submissions from 23
participants. The tasks are challenging, with the majority of submitted systems
failing to significantly outperform the majority class baseline on the
entailment task, and we observe significantly better performance on the
evidence selection task than on the entailment task. Increasing the number of
model parameters leads to a direct increase in performance, far more
significant than the effect of biomedical pre-training. Future works could
explore the limitations of large models for generalization and numerical
inference, and investigate methods to augment clinical datasets to allow for
more rigorous testing and to facilitate fine-tuning.
We envisage that the dataset, models, and results of this task will be useful
to the biomedical NLI and evidence retrieval communities. The dataset,
competition leaderboard, and website are publicly available.

这篇论文介绍了 SemEval 2023 任务 7 的结果 -- 临床试验数据的多证据自然语言推断（NLI4CT），包括两个任务：自然语言推断任务和临床试验数据的证据选择任务。

SemEval-2023 任务 7：面向临床试验数据的多证据自然语言推理

SemEval-2023 Task 7: Multi-Evidence Natural Language Inference for  Clinical Trial Data

With the increasing number of clinical trial reports generated every day, it
is becoming hard to keep up with novel discoveries that inform evidence-based
healthcare recommendations. To help automate this process and assist medical
experts, NLP solutions are being developed. This motivated the SemEval-2023
Task 7, where the goal was to develop an NLP system for two tasks: evidence
retrieval and natural language inference from clinical trial data. In this
paper, we describe our two developed systems. The first one is a pipeline
system that models the two tasks separately, while the second one is a joint
system that learns the two tasks simultaneously with a shared representation
and a multi-task learning approach. The final system combines their outputs in
an ensemble system. We formalize the models, present their characteristics and
challenges, and provide an analysis of achieved results.

本研究旨在研究开发一个基于 NLP 技术，应用于临床试验数据中的证据检索和自然语言推理任务的系统。该研究介绍了两个系统，一个是将两个任务分别建模的 Pipeline 系统，另一个是同时学习两个任务并采用共享表示和多任务学习方法的 Joint 系统。最终系统采用集成学习方法来结合这两个系统的输出，并提供了结果分析。

Sebis 参加 SemEval-2023 第 7 项任务：针对临床试验报告的自然语言推理和证据检索的联合系统

Sebis at SemEval-2023 Task 7: A Joint System for Natural Language  Inference and Evidence Retrieval from Clinical Trial Reports

Randomized controlled trials (RCTs) are considered as the gold standard for
testing causal hypotheses in the clinical domain. However, the investigation of
prognostic variables of patient outcome in a hypothesized cause-effect route is
not feasible using standard statistical methods. Here, we propose a new
automated causal inference method (AutoCI) built upon the invariant causal
prediction (ICP) framework for the causal re-interpretation of clinical trial
data. Compared to existing methods, we show that the proposed AutoCI allows to
efficiently determine the causal variables with a clear differentiation on two
real-world RCTs of endometrial cancer patients with mature outcome and
extensive clinicopathological and molecular data. This is achieved via
suppressing the causal probability of non-causal variables by a wide margin. In
ablation studies, we further demonstrate that the assignment of causal
probabilities by AutoCI remain consistent in the presence of confounders. In
conclusion, these results confirm the robustness and feasibility of AutoCI for
future applications in real-world clinical analysis.

本文提出了一种新的自动因果推断方法（AutoCI），该方法基于不变的因果预测（ICP）框架，可以对临床试验数据进行因果重新解释，并在两个真实的子宫内膜癌临床试验中有效地确定因果变量，证实 AutoCI 在临床分析中的鲁棒性和可行性。

随机对照临床试验中自动因果推断

Automated causal inference in application to randomized controlled clinical trials

Because many illnesses show heterogeneous response to treatment, there is
increasing interest in individualizing treatment to patients [Arch. Gen.
Psychiatry 66 (2009) 128--133]. An individualized treatment rule is a decision
rule that recommends treatment according to patient characteristics. We
consider the use of clinical trial data in the construction of an
individualized treatment rule leading to highest mean response. This is a
difficult computational problem because the objective function is the
expectation of a weighted indicator function that is nonconcave in the
parameters. Furthermore, there are frequently many pretreatment variables that
may or may not be useful in constructing an optimal individualized treatment
rule, yet cost and interpretability considerations imply that only a few
variables should be used by the individualized treatment rule. To address these
challenges, we consider estimation based on $l_1$-penalized least squares. This
approach is justified via a finite sample upper bound on the difference between
the mean response due to the estimated individualized treatment rule and the
mean response due to the optimal individualized treatment rule.

利用临床试验数据构建个体化治疗规则，采用基于 $l_{1}$- 惩罚最小二乘的估计方法，并找到最高的平均反应率。