Advances in Interpretable Machine Learning for Healthcare and Energy Applications

The field of machine learning is rapidly advancing, with a growing emphasis on interpretability and explainability. Recent research has focused on developing innovative methods for risk identification, mortality prediction, and anomaly detection in various domains, including healthcare and energy. A key direction in this field is the development of modular warning systems, such as those used in proximity healthcare, which can ingest multi-modal data and provide predictive results. Another area of focus is the creation of clinically interpretable models, such as those used for mortality prediction in ICU patients with diabetes and atrial fibrillation.

Noteworthy papers in this area include one that presents a general automated pipeline for ingesting multi-modal data and providing predictive results, and another that develops an interpretable machine learning model for predicting 28-day mortality in ICU patients with concurrent diabetes and atrial fibrillation. Additionally, a paper on estimating deprivation cost functions for power outages during disasters using a discrete choice modeling approach is also noteworthy, as it provides a methodology for measuring the costs of power outages and enables policymakers to develop more equitable resilience strategies.

Sources

AI-based modular warning machine for risk identification in proximity healthcare

Clinically Interpretable Mortality Prediction for ICU Patients with Diabetes and Atrial Fibrillation: A Machine Learning Approach

Signatures to help interpretability of anomalies

Estimating Deprivation Cost Functions for Power Outages During Disasters: A Discrete Choice Modeling Approach

Failure Modes of Time Series Interpretability Algorithms for Critical Care Applications and Potential Solutions

Explaining deep neural network models for electricity price forecasting with XAI

Radiomic fingerprints for knee MR images assessment

A Visualization Framework for Exploring Multi-Agent-Based Simulations Case Study of an Electric Vehicle Home Charging Ecosystem

AI-assisted radiographic analysis in detecting alveolar bone-loss severity and patterns

E-ABIN: an Explainable module for Anomaly detection in BIological Networks

DPLib: A Standard Benchmark Library for Distributed Power System Analysis and Optimization

Estimating Technical Loss without Power Flows: A Practical, Data-Driven Approach for Loss Estimation in Distribution Grids

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