In the context of medical decision making, counterfactual prediction enables
clinicians to predict treatment outcomes of interest under alternative courses
of therapeutic actions given observed patient history. Prior machine learning
approaches for counterfactual predictions under time-varying treatments focus
on static time-varying treatment regimes where treatments do not depend on
previous covariate history. In this work, we present G-Transformer, a
Transformer-based framework supporting g-computation for counterfactual
prediction under dynamic and time-varying treatment strategies. G-Transfomer
captures complex, long-range dependencies in time-varying covariates using a
Transformer architecture. G-Transformer estimates the conditional distribution
of relevant covariates given covariate and treatment history at each time point
using an encoder architecture, then produces Monte Carlo estimates of
counterfactual outcomes by simulating forward patient trajectories under
treatment strategies of interest. We evaluate G-Transformer extensively using
two simulated longitudinal datasets from mechanistic models, and a real-world
sepsis ICU dataset from MIMIC-IV. G-Transformer outperforms both classical and
state-of-the-art counterfactual prediction models in these settings. To the
best of our knowledge, this is the first Transformer-based architecture for
counterfactual outcome prediction under dynamic and time-varying treatment
strategies. Code will be released upon publication of the paper.

基于 G-Transformer 框架的对动态和时间变化的治疗策略进行反事实预测的研究中，通过模拟患者在感兴趣的治疗策略下的前进轨迹，使用 Transformer 架构捕获时间变化的协变量的复杂长程依赖并评估该框架的性能。

G-Transformer：动态和时变治疗制度下的反事实结果预测

G-Transformer: Counterfactual Outcome Prediction under Dynamic and  Time-varying Treatment Regimes

A key challenge in medical decision making is learning treatment policies for
patients with limited observational data. This challenge is particularly
evident in personalized healthcare decision-making, where models need to take
into account the intricate relationships between patient characteristics,
treatment options, and health outcomes. To address this, we introduce
prescriptive networks (PNNs), shallow 0-1 neural networks trained with mixed
integer programming that can be used with counterfactual estimation to optimize
policies in medium data settings. These models offer greater interpretability
than deep neural networks and can encode more complex policies than common
models such as decision trees. We show that PNNs can outperform existing
methods in both synthetic data experiments and in a case study of assigning
treatments for postpartum hypertension. In particular, PNNs are shown to
produce policies that could reduce peak blood pressure by 5.47 mm Hg (p=0.02)
over existing clinical practice, and by 2 mm Hg (p=0.01) over the next best
prescriptive modeling technique. Moreover PNNs were more likely than all other
models to correctly identify clinically significant features while existing
models relied on potentially dangerous features such as patient insurance
information and race that could lead to bias in treatment.

医学决策制定中的一个关键挑战是学习对具有有限观察数据的患者进行治疗政策。本研究引入了预测网络（PNNs），使用混合整数规划进行训练的浅层 0-1 神经网络，可与反事实估计一起在中等数据环境中优化政策。通过合成数据实验和产后高血压治疗案例研究，证明 PNNs 可以在性能上超越现有方法，并能够输出具有临床意义的治疗政策。

0-1 神经网络在处方和预测中的应用

Applications of 0-1 Neural Networks in Prescription and Prediction

Machine learning (ML) is increasingly being used in image retrieval systems
for medical decision making. One application of ML is to retrieve visually
similar medical images from past patients (e.g. tissue from biopsies) to
reference when making a medical decision with a new patient. However, no
algorithm can perfectly capture an expert's ideal notion of similarity for
every case: an image that is algorithmically determined to be similar may not
be medically relevant to a doctor's specific diagnostic needs. In this paper,
we identified the needs of pathologists when searching for similar images
retrieved using a deep learning algorithm, and developed tools that empower
users to cope with the search algorithm on-the-fly, communicating what types of
similarity are most important at different moments in time. In two evaluations
with pathologists, we found that these refinement tools increased the
diagnostic utility of images found and increased user trust in the algorithm.
The tools were preferred over a traditional interface, without a loss in
diagnostic accuracy. We also observed that users adopted new strategies when
using refinement tools, re-purposing them to test and understand the underlying
algorithm and to disambiguate ML errors from their own errors. Taken together,
these findings inform future human-ML collaborative systems for expert
decision-making.

本文研究了机器学习在医学决策中图像检索系统中的应用，通过开发改进工具，以满足病理学家在使用深度学习算法检索相似图像时的需要，提高了检测到的图像的诊断实用性并增强了用户对算法的信任。在使用改进工具时，用户还采用了新的策略，以测试和理解基础算法，并从自己的错误中消除机器学习中的错误。