How does the model make predictions? A systematic literature review on the explainability power of machine learning in healthcare.

Background: Medical use cases for machine learning (ML) are growing exponentially. The first hospitals are already using ML systems as decision support systems in their daily routine. At the same time, most ML systems are still opaque and it is not clear how these systems arrive at their predictions.

Explainable multiple abnormality classification of chest CT volumes.

Understanding model predictions is critical in healthcare, to facilitate rapid verification of model correctness and to guard against use of models that exploit confounding variables. We introduce the challenging new task of explainable multiple abnormality classification in volumetric medical images, in which a model must indicate the regions used to predict each abnormality. To solve this task, we propose a multiple instance learning convolutional neural network, AxialNet, that allows identification of top slices for each abnormality.

Machine Learning Algorithms to Predict Mortality and Allocate Palliative Care for Older Patients With Hip Fracture.

Objectives: To evaluate a machine learning model designed to predict mortality for Medicare beneficiaries aged >65 years treated for hip fracture in Inpatient Rehabilitation Facilities (IRFs).

Design: Retrospective design/cohort analysis of Centers for Medicare & Medicaid Services Inpatient Rehabilitation Facility-Patient Assessment Instrument data.

Setting And Participants: A total of 17,140 persons admitted to Medicare-certified IRFs in 2015 following hospitalization for hip fracture.