MODEL INTERPRETATION AND EXPLAINABILITY Towards Creating Transparency in Prediction Models

Explainable AI (XAI) has a counterpart in analytical modeling which we refer to as model explainability. We tackle the issue of model explainability in the context of prediction models. We analyze a dataset of loans from a credit card company and apply three stages: execute and compare four different prediction methods, apply the best known explainability techniques in the current literature to the model training sets to identify feature importance (FI) (static case), and finally to cross-check whether the FI set holds up under “what if” prediction scenarios for continuous and categorical variables (dynamic case). We found inconsistency in FI identification between the static and dynamic cases. We summarize the “state of the art” in model explainability and suggest further research to advance the field

Unified Explanations in Machine Learning Models: A Perturbation Approach

A high-velocity paradigm shift towards Explainable Artificial Intelligence (XAI) has emerged in recent years. Highly complex Machine Learning (ML) models have flourished in many tasks of intelligence, and the questions have started to shift away from traditional metrics of validity towards something deeper: What is this model telling me about my data, and how is it arriving at these conclusions? Previous work has uncovered predictive models generating explanations contrasting domain experts, or excessively exploiting bias in data that renders a model useless in highly-regulated settings. These inconsistencies between XAI and modeling techniques can have the undesirable effect of casting doubt upon the efficacy of these explainability approaches. To address these problems, we propose a systematic, perturbation-based analysis against a popular, model-agnostic method in XAI, SHapley Additive exPlanations (Shap). We devise algorithms to generate relative feature importance in settings of dynamic inference amongst a suite of popular machine learning and deep learning methods, and metrics that allow us to quantify how well explanations generated under the static case hold. We propose a taxonomy for feature importance methodology, measure alignment, and observe quantifiable similarity amongst explanation models across several datasets

Ensemble approach combining multiple methods improves human transcription start site prediction

Dineen DG, Schroeder M, Higgins DG, Cunningham P. Ensemble approach combining multiple methods improves human transcription start site prediction. BMC Genomics. 2010;11(1): 677.Background: The computational prediction of transcription start sites is an important unsolved problem. Some recent progress has been made, but many promoters, particularly those not associated with CpG islands, are still difficult to locate using current methods. These methods use different features and training sets, along with a variety of machine learning techniques and result in different prediction sets. Results: We demonstrate the heterogeneity of current prediction sets, and take advantage of this heterogeneity to construct a two-level classifier ('Profisi Ensemble') using predictions from 7 programs, along with 2 other data sources. Support vector machines using 'full' and 'reduced' data sets are combined in an either/or approach. We achieve a 14% increase in performance over the current state-of-the-art, as benchmarked by a third-party tool. Conclusions: Supervised learning methods are a useful way to combine predictions from diverse sources

Exploring orthopaedic patients’ experiences of hospital discharge: Implications for nursing care

Background Nurses play a key role in providing discharge education. With the increased demand for orthopaedic surgery and subsequent fast‐track surgical programmes resulting in reduction in hospital length of stay, obtaining patient feedback about discharge is important to inform nursing practice of discharge. Aim To explore patients’ experiences of discharge from hospital following orthopaedic surgery. Methods A descriptive qualitative study was undertaken with a sample of 34 patients discharged following orthopaedic surgery at a private acute Australian hospital. Individual semistructured telephone interviews were conducted and analysed using inductive thematic analysis. Findings From the analysis, patient experiences have been described in three themes: (1) experiences of hospital discharge, (2) perceptions of discharge information, and (3) limitations of discharge information. Although participants reported being informed when discharged from hospital, more information about medication management, constipation, and wound care would have better supported their recovery to assist in their self-care. Discussion Discharge experiences and perceptions varied between participants, highlighting the importance of nurses and other health professionals, in providing discharge information to meet individual patient needs. This included improved communication, information about the discharge process, management of medication, wound, and prevention of constipation as part of recovery. Conclusion Patient feedback has highlighted that nurses need to provide more tailored discharge information for orthopaedic patients to support recovery to prevent postdischarge problems and hospital readmission

Neuropsychological outcomes of children with Optic Pathway Glioma

Optic Pathway Glioma (OPG) is a relatively common brain tumour in childhood; however, there is scarce understanding of neuropsychological sequelae in these survivors. In this study, 12 children with diagnosis of OPG before 6 years of age received a comprehensive standardised assessment of visual perception, general intelligence and academic achievement, using adjustments to visual materials of the tests, to examine the extent of concurrent impairment in these functional domains. Information about vision, clinical and sociodemographic factors were extracted from medical records to assess the associations of neuropsychological outcomes with clinical and socio-demographic factors. Children with OPG exhibited high within-patient variability and moderate group-level impairment compared to test norms. Visual perception was the most impaired domain, while scholastic progression was age-appropriate overall. For cognition, core verbal and visuo-spatial reasoning skills were intact, whereas deficits were found in working memory and processing speed. Visual function was associated with tasks that rely on visual input. Children with OPG are at moderate risk of neuropsychological impairment, especially for visual perception and cognitive proficiency. Future research should elucidate further the relative contribution of vision loss and neurofibromatosis type 1 co-diagnosis within a large sample

Maine Perspective, v 2, i 39

The Maine Perspective, a publication for the University of Maine, was a campus newsletter produced by the Department of Public Affairs which eventually transformed into the Division of Marketing and Communication. Regular columns included the UM Calendar, a list of ongoing activities and events, Campus Notes, From the Library, and job listings as well as feature stories. Headlines from this issue of Maine Perspective include: Changes Coming in Campus Parking; Internationally Renowned Pulp and Paper Researcher Named to J. Larcom Ober Chair; Maine Forest and Logging Museum Now Affiliated with the University of Maine; and Recommendations of the UM Committee on Women\u27s Programs Being Implemented

Maine Perspective, v 2, i 31

The Maine Perspective, a publication for the University of Maine, was a campus newsletter produced by the Department of Public Affairs which eventually transformed into the Division of Marketing and Communication. Regular columns included the UM Calendar, a list of ongoing activities and events, Campus Notes, From the Library, and job listings as well as feature stories. Headlines from this issue of Maine Perspective include: Nationally Acclaimed Editor Calls for Support of Education\u27s \u27Holy Trinity\u27; and Learning Resource Center Provides Unique Environment for Nursing Students

Maine Perspective, v 2, i 30

The Maine Perspective, a publication for the University of Maine, was a campus newsletter produced by the Department of Public Affairs which eventually transformed into the Division of Marketing and Communication. Regular columns included the UM Calendar, a list of ongoing activities and events, Campus Notes, From the Library, and job listings as well as feature stories. Headlines from this issue of Maine Perspective include: UM President Dale Lick Bids Farewell to University Community; UM Student Finds Himself in the Spotlight Promoting Math and Science to People in His Hometown; and Overseas Outlooks

Maine Perspective, v 2, i 27

The Maine Perspective, a publication for the University of Maine, was a campus newsletter produced by the Department of Public Affairs which eventually transformed into the Division of Marketing and Communication. Regular columns included the UM Calendar, a list of ongoing activities and events, Campus Notes, From the Library, and job listings as well as feature stories. Headlines from this issue of Maine Perspective include: Legislative Commission Confirms Need for University Funding; TV Commercials: The Messages Behind the Medium; and Commission Report Analyzes System\u27s Financial Health