With the widespread of machine learning models for healthcare applications,
there is increased interest in building applications for personalized medicine.
Despite the plethora of proposed research for personalized medicine, very few
focus on representing missingness and learning from the missingness patterns in
time-series Electronic Health Records (EHR) data. The lack of focus on
missingness representation in an individualized way limits the full utilization
of machine learning applications towards true personalization. In this brief
communication, we highlight new insights into patterns of missingness with
real-world examples and implications of missingness in EHRs. The insights in
this work aim to bridge the gap between theoretical assumptions and practical
observations in real-world EHRs. We hope this work will open new doors for
exploring directions for better representation in predictive modelling for true
personalization.

研究介绍了对于个性化医学中的缺失表示和学习缺失模式的关注不足，提出了关于实际电子健康记录中缺失模式的新见解和影响，旨在实现真正个性化的预测建模。

个体化表征中的时间序列电子健康记录缺失理解

Understanding Missingness in Time-series Electronic Health Records for  Individualized Representation

Deep learning models trained with large amounts of data have become a recent
and effective approach to predictive problem solving -- these have become known
as "foundation models" as they can be used as fundamental tools for other
applications. While the paramount examples of image classification (earlier)
and large language models (more recently) led the way, the Segment Anything
Model (SAM) was recently proposed and stands as the first foundation model for
image segmentation, trained on over 10 million images and with recourse to over
1 billion masks. However, the question remains -- what are the limits of this
foundation? Given that magnetic resonance imaging (MRI) stands as an important
method of diagnosis, we sought to understand whether SAM could be used for a
few tasks of zero-shot segmentation using MRI data. Particularly, we wanted to
know if selecting masks from the pool of SAM predictions could lead to good
segmentations.
Here, we provide a critical assessment of the performance of SAM on magnetic
resonance imaging data. We show that, while acceptable in a very limited set of
cases, the overall trend implies that these models are insufficient for MRI
segmentation across the whole volume, but can provide good segmentations in a
few, specific slices. More importantly, we note that while foundation models
trained on natural images are set to become key aspects of predictive
modelling, they may prove ineffective when used on other imaging modalities.

深度学习模型在大量数据训练下已成为最近有效的预测问题解决方法，研究了基于自然图像训练的基础模型在磁共振成像数据上的分割任务性能，结果表明这些模型虽然在少数情况下可接受，但根据整体趋势，它们在整个体积的磁共振成像分割上不够有效，而在少数特定切片上表现良好，这提示基于自然图像训练的基础模型可能在其他成像模态上效果不佳。

在放射学数据上测试 segment anything 模型

Testing the Segment Anything Model on radiology data

Predictive modelling using machine learning has become very popular for
spatial mapping of the environment. Models are often applied to make
predictions far beyond sampling locations where new geographic locations might
considerably differ from the training data in their environmental properties.
However, areas in the predictor space without support of training data are
problematic. Since the model has no knowledge about these environments,
predictions have to be considered uncertain.
Estimating the area to which a prediction model can be reliably applied is
required. Here, we suggest a methodology that delineates the "area of
applicability" (AOA) that we define as the area, for which the cross-validation
error of the model applies. We first propose a "dissimilarity index" (DI) that
is based on the minimum distance to the training data in the predictor space,
with predictors being weighted by their respective importance in the model. The
AOA is then derived by applying a threshold based on the DI of the training
data where the DI is calculated with respect to the cross-validation strategy
used for model training. We test for the ideal threshold by using simulated
data and compare the prediction error within the AOA with the cross-validation
error of the model. We illustrate the approach using a simulated case study.
Our simulation study suggests a threshold on DI to define the AOA at the .95
quantile of the DI in the training data. Using this threshold, the prediction
error within the AOA is comparable to the cross-validation RMSE of the model,
while the cross-validation error does not apply outside the AOA. This applies
to models being trained with randomly distributed training data, as well as
when training data are clustered in space and where spatial cross-validation is
applied.
We suggest to report the AOA alongside predictions, complementary to
validation measures.

通过提出新的 “不相似指数” 和 “适用范围” 的概念，研究了预测模型的适用性问题，并得出了一种确定模型可靠应用区域的方法。