Understanding Learned Models by Identifying Important Features at the Right Resolution

In many application domains, it is important to characterize how complex learned models make their decisions across the distribution of instances. One way to do this is to identify the features and interactions among them that contribute to a model's predictive accuracy. We present a model-agnostic approach to this task that makes the following specific contributions. Our approach (i) tests feature groups, in addition to base features, and tries to determine the level of resolution at which important features can be determined, (ii) uses hypothesis testing to rigorously assess the effect of each feature on the model's loss, (iii) employs a hierarchical approach to control the false discovery rate when testing feature groups and individual base features for importance, and (iv) uses hypothesis testing to identify important interactions among features and feature groups. We evaluate our approach by analyzing random forest and LSTM neural network models learned in two challenging biomedical applications.Comment: First two authors contributed equally to this work, Accepted for presentation at the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19

Process diagnosis with timed observation

International audienceIn this paper we propose the use of the Timed Observation theory as a powerful frameworks for model-based diagnosis. In fact, they provide a global formalism for modelling a dynamic system (TOM4D), for characterizing and computing diagnoses of the system under investigation

Modelling and diagnosis of dynamic systems from timed observations

International audienceThis paper proposes the use of the Timed Observation theory as a powerful framework for model-based diagnosis. In fact, this theory provides a global formalism for modelling a dynamic system (TOM4D), for characterizing and computing diagnoses of the system under investigation

Diagnostic Medical Errors: Patient\u27s Perspectives on a Pervasive Problem

Introduction. The Institute of Medicine defines diagnostic error as the failure to establish an accurate or timely explanation for the patient\u27s health problem(s), or effectively communicate the explanation to the patient. To our knowledge, no studies exist characterizing diagnostic error from patient perspectives using this definition. Objective. We sought to characterize diagnostic errors experienced by patients and describe patient perspectives on causes, impacts, and prevention strategies. Methods. We screened 77 adult inpatients at University of Vermont Medical Center and conducted 27 structured interviews with patients who experienced diagnostic error in the past five years. We performed qualitative analysis using Grounded Theory. Results. In the past five years, 39% of interviewed patients experienced diagnostic error. The errors mapped to the following categories: accuracy (30%), communication (34%) and timeliness (36%). Poor communication (13 responses) and inadequate time with doctors (7) were the most identified causes of errors. Impacts of errors included emotional distress (17 responses), adverse health outcomes (7) and impaired activities of daily living (6). Patients suggested improved communication (11 responses), clinical management (7) and access to doctors (5) as prevention strategies. For communication, patients rated talk to your doctor highest (mean 8.4, on 1-10 Likert scale) and text message lowest (4.8). Conclusions/Recommendations. Diagnostic errors are common and have dramatic impact on patients\u27 well-being. We suggest routine surveillance to identify errors, support for patients who have experienced errors, and implementation of patient and provider checklists to enhance communication. Future studies should investigate strategies to allow care providers adequate time with patients.https://scholarworks.uvm.edu/comphp_gallery/1246/thumbnail.jp

Mobility deficit – Rehabilitate, an opportunity for functionality

There are many pathological conditions that cause mobility deficits and that ultimately influence someone’s autonomy.Aims: to evaluate patients with mobility deficits functional status; to implement a Rehabilitation Nursing intervention plan; to monitor health gains through mobility deficits rehabilitation.Conclusion: Early intervention and the implementation of a nursing rehabilitation intervention plan results in health gains (direct or indirect), decreases the risk of developing Pressure Ulcers (PU) and the risk of developing a situation of immobility that affects patients’ autonomy and quality of life

The Language of Mental Illness

This paper surveys some philosophical issues with the language surrounding mental illness, but is especially focused on pejoratives relating to mental illness. I argue that though 'crazy' and similar mental illness-based epithets (MI-epithets) are not best understood as slurs, they do function to isolate, exclude, and marginalize members of the targeted group in ways similar to the harmfulness of slurs more generally. While they do not generally express the hate/contempt characteristic of weaponized uses of slurs, MI-epithets perpetuate epistemic injustice by portraying sufferers of mental illness as deserving minimal credibility. After outlining the ways in which these epithets can cause harm, I examine available legal and social remedies, and suggest that the best path going forward is to pursue a reclamation project rather than aiming to censure the use of MI-epithets

An Integration of FDI and DX Techniques for Determining the Minimal Diagnosis in an Automatic Way

Two communities work in parallel in model-based diagnosis: FDI and DX. In this work an integration of the FDI and the DX communities is proposed. Only relevant information for the identification of the minimal diagnosis is used. In the first step, the system is divided into clusters of components, and each cluster is separated into nodes. The minimal and necessary set of contexts is then obtained for each cluster. These two steps automatically reduce the computational complexity since only the essential contexts are generated. In the last step, a signature matrix and a set of rules are used in order to obtain the minimal diagnosis. The evaluation of the signature matrix is on-line, the rest of the process is totally off-line.Ministerio de Ciencia y Tecnología DPI2003-07146-C02-0