Learning Predictive Checklists with Probabilistic Logic Programming

TL;DR

This paper introduces a novel probabilistic logic programming approach for automatically learning predictive checklists from diverse data types, enhancing interpretability and performance in complex prediction tasks.

Contribution

It presents a new method that learns predictive checklists from various data modalities using probabilistic logic programming, allowing for flexible interpretability and improved accuracy.

Findings

Outperforms existing explainable ML techniques on image sequence data

Effective in handling diverse data types like time series and clinical notes

Provides a tunable tradeoff between interpretability and data fidelity

Abstract

Checklists have been widely recognized as effective tools for completing complex tasks in a systematic manner. Although originally intended for use in procedural tasks, their interpretability and ease of use have led to their adoption for predictive tasks as well, including in clinical settings. However, designing checklists can be challenging, often requiring expert knowledge and manual rule design based on available data. Recent work has attempted to address this issue by using machine learning to automatically generate predictive checklists from data, although these approaches have been limited to Boolean data. We propose a novel method for learning predictive checklists from diverse data modalities, such as images and time series. Our approach relies on probabilistic logic programming, a learning paradigm that enables matching the discrete nature of checklist with continuous-valued data. We propose a regularization technique to tradeoff between the information captured in discrete concepts of continuous data and permit a tunable level of interpretability for the learned checklist concepts. We demonstrate that our method outperforms various explainable machine learning techniques on prediction tasks involving image sequences, time series, and clinical notes.