Source-linked AI summary
Interpretability of machine learning based prediction models in healthcare
Gregor Stiglic, Primoz Kocbek, Nino Fijacko, Marinka Zitnik, Katrien Verbert, Leona Cilar
TL;DR
Healthcare machine-learning predictions require interpretability because they affect patient health, user confidence, and explainable decision making. The paper categorizes interpretability by local or global scope and model-specific or model-agnostic methods, reviews healthcare applications, and identifies scope boundaries and open challenges including scalability, evaluation, and causality.
Problem
Limited interpretability can hinder trust and wider adoption of healthcare machine-learning models, while clinical users need confidence in predictions that may affect patient health.
Method
The paper provides a practical overview that categorizes interpretability as local or global and model-specific or model-agnostic, with examples from healthcare applications.
Results
The review describes practical interpretability applications across healthcare, including SHAP-based support for anticipating hypoxaemia events and visualization for detecting acute intracranial haemorrhage.
Takeaways & Limitations
Interpretable healthcare ML should be considered in relation to high-stakes clinical decisions and accompanying ethical, legal, and regulatory challenges.
Takeaways & Limitations
The overview excludes interpretability approaches for nonstructured data such as medical images, text, and signal-based data, and does not provide an in-depth analysis of several open topics.
Abstract
from arXiv · showhide
There is a need of ensuring machine learning models that are interpretable. Higher interpretability of the model means easier comprehension and explanation of future predictions for end-users. Further, interpretable machine learning models allow healthcare experts to make reasonable and data-driven decisions to provide personalized decisions that can ultimately lead to higher quality of service in healthcare. Generally, we can classify interpretability approaches in two groups where the first focuses on personalized interpretation (local interpretability) while the second summarizes prediction models on a population level (global interpretability). Alternatively, we can group interpretability methods into model-specific techniques, which are designed to interpret predictions generated by a specific model, such as a neural network, and model-agnostic approaches, which provide easy-to-understand explanations of predictions made by any machine learning model. Here, we give an overview of interpretability approaches and provide examples of practical interpretability of machine learning in different areas of healthcare, including prediction of health-related outcomes, optimizing treatments or improving the efficiency of screening for specific conditions. Further, we outline future directions for interpretable machine learning and highlight the importance of developing algorithmic solutions that can enable machine-learning driven decision making in high-stakes healthcare problems.
Introduction
Interpretable machine learning is especially important in healthcare because predictions affect patient health and must be understandable, trustworthy, and potentially explainable. The paper organizes interpretability approaches by local versus global scope and model-specific versus model-agnostic techniques, while excluding nonstructured data modalities.
- Motivation: Higher model interpretability makes it easier for end-users to comprehend why predictions were made and supports retraceable algorithmic decisions.The discussion connects this need with the GDPR right to explanation and related concerns including fairness, privacy, reliability, robustness, causality, and trust.
- Taxonomy: Local interpretability explains an individual prediction or small region, whereas global interpretability summarizes relationships between inputs and the model’s overall prediction space.Both approaches have been used in healthcare and continue to develop alongside new prediction-model methods.
- Scope: The paper reviews interpretability methods and practical healthcare examples, but excludes approaches for nonstructured data such as medical images, text, and signal-based data.The authors identify this exclusion as a scope boundary and refer readers to broader work on interpretability and responsible machine learning.
- Taxonomy: Model-specific methods inspect internal parameters of particular models, while model-agnostic methods explain predictions from any machine learning model, usually post hoc.Model-agnostic approaches may use surrogate or proxy models to approximate complex black-box behavior.
- Motivation: Healthcare ML predictions can affect patient health, making user confidence and understandable explanations important for clinical use.The paper also notes that limited interpretability can hinder wider adoption and that bias may undermine trust.
Model-specific or model-agnostic interpretability
Interpretability methods can be specific to a model or applicable across models. Model-agnostic techniques commonly treat the original model as a black box and approximate its behavior with a simpler surrogate, while specialized tools can address complex representations such as graphs.
- Model-specific or model-agnostic interpretability: Model-specific methods interpret predictions by examining internal parameters of a designated model, whereas model-agnostic methods can be applied to any machine learning model.Model-agnostic methods are usually applied post hoc without inspecting the original model’s internal parameters.
- Model-specific or model-agnostic interpretability: A model-agnostic explanation can use a surrogate or proxy model to learn a locally faithful approximation of a complex black-box model from its outputs.The passage links this strategy to model compression and later knowledge-distillation approaches.
- Model-specific or model-agnostic interpretability: GNNExplainer is a model-specific method for graph representations and provides both global and local interpretability for Graph Neural Networks.Its relevance arises from the specialized framework required to handle graph-structured data.
- Local or global interpretability: Local methods explain a single prediction using model architecture or similar instances, while global methods describe model behavior at an abstract level.Local examples may emphasize patient characteristics shared with a smaller group but differing from other patients.
Examples of interpretable machine learning models
Table 1 presents examples of approaches used to interpret prediction models. It serves as an overview of interpretability techniques rather than reporting a specific comparative result in the supplied passages.
- The table is used to situate examples within the paper’s broader discussion of interpretable machine learning models.
- Table 1 lists examples of approaches to interpretability for prediction models.
- The supplied table passages identify the table’s purpose but do not state a specific outcome or comparison among the listed approaches.
Global Local
The overview organizes healthcare ML interpretability by global versus local scope and model-specific versus model-agnostic techniques, illustrating applications and unresolved practical challenges.
- Global versus local interpretability: Global interpretation summarizes relationships between all inputs and predictions, whereas local interpretation explains an individual prediction or a small region of the prediction function.
- Model-specific versus model-agnostic interpretability: Model-specific approaches interpret a model directly, while model-agnostic methods can use techniques such as knowledge distillation to produce simpler explanations.
- Healthcare applications: Healthcare applications include regression and Naive Bayes models across specialties, SHAP for hypoxaemia prevention, and attention-map visualization for intracranial haemorrhage detection.
- Healthcare applications: 15% increased anaesthesiologists’ anticipation of hypoxaemia events when SHAP was used for prediction interpretation during surgery.
- Healthcare applications: MUSE generates global if-then rules and separate feature-subspace rules, combining global model-agnostic and personalized interpretation characteristics.
- Open challenges: The overview identifies open challenges involving explanation simplification, fairness and bias auditing, scalability, explanation evaluation, and causality.
Sidebar title: Visual analytics and interpretability
Visual analytics supports healthcare experts in interpreting and diagnosing ML models through interactive interfaces, with prototypes spanning model-specific and model-agnostic approaches. The paper concludes that important challenges remain and calls for further algorithmic development for healthcare decision-making.
- Visual analytics and interpretability: Visual analytics combines data mining, machine learning, human-computer interaction, and cognition to support model interpretation and steering through interactive visual interfaces.
- Visual analytics and interpretability: RetainVis visualizes a recurrent neural network by projecting patients into two dimensions, showing nearby patients in latent space for heart-failure and cataract prediction.
- Visual analytics and interpretability: RuleMatrix extracts rule-based knowledge from model input-output behavior to help experts inspect classification models used for cancer and diabetes classification.
- Conclusion: The conclusion categorizes interpretability as model-specific or model-agnostic and global or local, while noting that key challenges remain unresolved.
- Future directions: Future work may supplement global interpretations with individual or subgroup explanations and develop algorithmic solutions for healthcare problems affecting disease course and outcome.