“InterpretML is an open-source Python package which exposes machine learning interpretability algorithms to practitioners and researchers. InterpretML exposes two types of interpretability – glassbox, which are machine learning models designed for interpretability (ex: linear models, rule lists, generalized additive models), and blackbox explainability techniques for explaining existing systems (ex: Partial Dependence, LIME). The package enables practitioners to easily compare interpretability algorithms by exposing multiple methods under a unified API, and by having a built-in, extensible visualization platform. InterpretML also includes the first implementation of the Explainable Boosting Machine, a powerful, interpretable, glassbox model that can be as accurate as many blackbox models.”
InterpretML: A Unified Framework for Machine Learning Interpretability 
Harsha Nori et al.
arXiv Sept 2019

”As part of the framework, InterpretML also includes a new interpretability algorithm – the Explainable Boosting Machine (EBM). EBM is a glassbox model, designed to have accuracy comparable to state-of-the-art machine learning methods like Random Forest and Boosted Trees, while being highly intelligibile and explainable. EBM is a generalized additive model (GAM) of the form:

where g is the link function that adapts the GAM to different settings such as regression or classification.”
InterpretML: A Unified Framework for Machine Learning Interpretability 
Harsha Nori et al.
arXiv Sept 2019

EBMs are highly intelligible, because the contribution of each feature to a final prediction can be visualized and understood by plotting fj. Because EBM is an additive model, each feature contributes to predictions in a modular way that makes it easy to reason about the contribution of each feature to the prediction.

Key finding: A total of 55/103 (53.4%) patients with clinical stage I PDAC had focal pancreatic abnormalities on CT obtained at least one year before diagnosis, most commonly focal atrophy (37.9%), faint enhancement (26.2%), and MPD change (13.6%); atrophy, enhancement, and MPD change appeared 4.6, 3.3, and 1.1 years before diagnosis, respectively.
Importance: Focal pancreatic abnormalities predicting subsequent PDAC development, including atrophy, faint enhancement, and MPD change, could allow an earlier diagnosis, thereby improving management and prognosis. 
CT Abnormalities of the Pancreas Associated With the Subsequent Diagnosis of Clinical Stage I Pancreatic Ductal Adenocarcinoma More Than One Year Later: A Case-Control Study 
Fumihito Toshima et al.
AJR 2021(in press) https://doi.org/10.2214/AJR.21.26014

Conclusion: Most patients with clinical stage I PDAC demonstrated focal pancreatic abnormalities on pre-diagnostic CT obtained at least one year before diagnosis. Focal MPD change exhibited the shortest duration from its development to subsequent diagnosis, where atrophy and faint enhancement exhibited a relatively prolonged course. 
Clinical impact: These findings could facilitate earlier PDAC diagnosis and thus improve prognosis. 
CT Abnormalities of the Pancreas Associated With the Subsequent Diagnosis of Clinical Stage I Pancreatic Ductal Adenocarcinoma More Than One Year Later: A Case-Control Study 
Fumihito Toshima et al.
AJR 2021(in press) https://doi.org/10.2214/AJR.21.26014

“as we know, there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns- the ones we don't know we don't know.” 
― Donald Rumsfeld