Interpretability is crucial for doctors, hospitals, pharmaceutical companies
and biotechnology corporations to analyze and make decisions for high stakes
problems that involve human health. Tree-based methods have been widely adopted
for \textit{survival analysis} due to their appealing interpretablility and
their ability to capture complex relationships. However, most existing methods
to produce survival trees rely on heuristic (or greedy) algorithms, which risk
producing sub-optimal models. We present a dynamic-programming-with-bounds
approach that finds provably-optimal sparse survival tree models, frequently in
only a few seconds.

解释性对于医生、医院、制药公司和生物技术公司来分析和做出决策具有重要意义，而树状方法作为生存分析中广泛采用的方法之一，具有解释性强和捕捉复杂关系能力的优点，但是现有的大多数方法依赖启发式算法来生成生存树模型，存在产生次优模型的风险，本文提出了一种动态规划和界限方法，可以在几秒内找到证明最优稀疏生存树模型。

最优稀疏生存树

Optimal Sparse Survival Trees

Efficiently finding doctors and locations is an important search problem for
patients in the healthcare domain, for which traditional information retrieval
methods tend not to work optimally. In the last ten years, knowledge graphs
(KGs) have emerged as a powerful way to combine the benefits of gleaning
insights from semi-structured data using semantic modeling, natural language
processing techniques like information extraction, and robust querying using
structured query languages like SPARQL and Cypher. In this short paper, we
present a KG-based search engine architecture for robustly finding doctors and
locations in the healthcare domain. Early results demonstrate that our approach
can lead to significantly higher coverage for complex queries without degrading
quality.

KG-based search engine architecture for finding doctors and locations in healthcare domain, with significantly higher coverage for complex queries without degrading quality.