Bladder cancer, the leading urinary tract cancer, is responsible for 15
deaths daily in the UK. This cancer predominantly manifests as
non-muscle-invasive bladder cancer (NMIBC), characterised by tumours not yet
penetrating the muscle layer of the bladder wall. NMIBC is plagued by a very
high recurrence rate of 70-80% and hence the costliest treatments. Current
tools for predicting recurrence use scoring systems that overestimate risk and
have poor accuracy. Inaccurate and delayed prediction of recurrence
significantly elevates the likelihood of mortality. Accurate prediction of
recurrence is hence vital for cost-effective management and treatment planning.
This is where Machine learning (ML) techniques have emerged as a promising
approach for predicting NMIBC recurrence by leveraging molecular and clinical
data. This review provides a comprehensive analysis of ML approaches for
predicting NMIBC recurrence. Our systematic evaluation demonstrates the
potential of diverse ML algorithms and markers, including radiomic, clinical,
histopathological, genomic, and biochemical data in enhancing recurrence
prediction and personalised patient management. We summarise various prediction
tasks, data modalities, and ML models used, highlighting their performance,
limitations, and future directions of incorporating cost-effectiveness.
Challenges related to generalisability and interpretability of artificial
intelligent models are discussed, emphasising the need for collaborative
efforts and robust datasets.

机器学习技术在预测非肌层侵袭性膀胱癌复发方面具有潜力，并通过利用分子和临床数据提高复发预测和个性化患者管理的性能。该综述总结了使用多种机器学习算法和标记的非肌层侵袭性膀胱癌复发预测，并强调了人工智能模型的泛化能力和解释能力的挑战，强调了合作努力和强大数据集的需求。

从算法到结果：AI 在非肌层侵袭性膀胱癌复发预测中的作用评估

From Algorithms to Outcomes: Reviewing AI's Role in Non-Muscle-Invasive  Bladder Cancer Recurrence Prediction

Symbolic regression, i.e. predicting a function from the observation of its
values, is well-known to be a challenging task. In this paper, we train
Transformers to infer the function or recurrence relation underlying sequences
of integers or floats, a typical task in human IQ tests which has hardly been
tackled in the machine learning literature. We evaluate our integer model on a
subset of OEIS sequences, and show that it outperforms built-in Mathematica
functions for recurrence prediction. We also demonstrate that our float model
is able to yield informative approximations of out-of-vocabulary functions and
constants, e.g. $\operatorname{bessel0}(x)\approx
\frac{\sin(x)+\cos(x)}{\sqrt{\pi x}}$ and $1.644934\approx \pi^2/6$. An
interactive demonstration of our models is provided at
this https URL

本文研究利用 Transformers 模型推断整数或浮点数序列中函数或递归关系，模型在 OEIS 子集数据集上表现良好，并能够生成有关未知函数或常数的近似。