Feature attributions are ubiquitous tools for understanding the predictions of machine learning models. However, popular methods for scoring input variables such as SHAP and LIME suffer from high instability due to random sampling. Leveraging ideas from multiple hypothesis testing, we devise attribution methods that correctly rank the most important features with high probability. Our algorithm RankSHAP guarantees that the $K$ highest Shapley values have the proper ordering with probability exceeding $1-\alpha$. Empirical results demonstrate its validity and impressive computational efficiency. We also build on previous work to yield similar results for LIME, ensuring the most important features are selected in the right order.

This study introduces Ascle, a pioneering natural language processing (NLP) toolkit designed for medical text generation. Ascle is tailored for biomedical researchers and healthcare professionals with an easy-to-use, all-in-one solution that requires minimal programming expertise. For the first time, Ascle evaluates and provides interfaces for the latest pre-trained language models, encompassing four advanced and challenging generative functions: question-answering, text summarization, text simplification, and machine translation. In addition, Ascle integrates 12 essential NLP functions, along with query and search capabilities for clinical databases. The toolkit, its models, and associated data are publicly available via https://github.com/Yale-LILY/MedGen.

In layman's terms, Shapley values quantify how much information is gained from being told the value of each feature. Shapley values are among the most popular tools for explaining predictions of blackbox Shapley values are rarely computed exactly, as the machine learning models. However, their computational cost is exponential in the number of high computational cost motivates the use input features. Rather, they are typically estimated of sampling approximations, inducing a considerable using the Shapley Sampling or KernelSHAP algorithm degree of uncertainty. To stabilize (Lundberg and Lee, 2017; Strumbelj and Kononenko, these model explanations, we propose ControlSHAP, 2010, 2014). These algorithms, however, are subject an approach based on the Monte to sampling variability; as a result, running the same Carlo technique of control variates. Our procedure twice may yield different estimated Shapley methodology is applicable to any machine values, including different estimated orderings of learning model and requires virtually no extra features. This instability raises questions about the computation or modeling effort. On several trustworthiness of insights gleaned from Shapley values.

The Electronic Health Record (EHR) is an essential part of the modern medical system and impacts healthcare delivery, operations, and research. Unstructured text is attracting much attention despite structured information in the EHRs and has become an exciting research field. The success of the recent neural Natural Language Processing (NLP) method has led to a new direction for processing unstructured clinical notes. In this work, we create a python library for clinical texts, EHRKit. This library contains two main parts: MIMIC-III-specific functions and tasks specific functions. The first part introduces a list of interfaces for accessing MIMIC-III NOTEEVENTS data, including basic search, information retrieval, and information extraction. The second part integrates many third-party libraries for up to 12 off-shelf NLP tasks such as named entity recognition, summarization, machine translation, etc.