Explanation-based learning for diagnosis

Diagnostic expert systems constructed using traditional knowledge-engineering techniques identify malfunctioning components using rules that associate symptoms with diagnoses. Model-based diagnosis (MBD) systems use models of devices to find faults given observations of abnormal behavior. These approaches to diagnosis are complementary. We consider hybrid diagnosis systems that include both associational and model-based diagnostic components. We present results on explanation-based learning (EBL) methods aimed at improving the performance of hybrid diagnostic problem solvers. We describe two architectures called EBL_IA and EBL(p). EBL_IA is a form fo "learning in advance" that pre-compiles models into associations. At run-time the diagnostic system is purely associational. In EBL(p), the run-time diagnosis system contains associational, MBD, and EBL components. Learned associational rules are preferred but when they are incomplete they may produce too many incorrect diagnoses. When errors cause performance to dip below a give threshold p, EBL(p) activates MBD and explanation-based "learning while doing". We present results of empirical studies comparing MBD without learning versus EBL_IA and EBL(p). The main conclusions are as follows. EBL_IA is superior when it is feasible but it is not feasible for large devices. EBL(p) can speed-up MBD and scale-up to larger devices in situations where perfect accuracy is not required

Caregiver and Clinician Assessment of Behavioral Disturbances: The California Dementia Behavior Questionnaire

As part of a multicenter project to study noncognitive behavioral disturbances in dementia, the authors developed a comprehensive caregiver-rated questionnaire for these behaviors. The authors determined the reliability of caregiver ratings and compared caregiver ratings with clinician ratings using standard instruments. Caregivers showed good test/retest reliability for ratings of all types of patient behavioral disturbance. Caregiver interrater reliability was highest for depression and lowest for psychosis. The correlation between caregiver reports and professional assessments was highest for agitation, intermediate for psychosis, and lowest for depression. The match between caregiver and clinician assessments of patient behaviors appears to vary significantly by the type of behavior assessed

Learning approximate diagnosis

Model-based diagnosis (MBD) provides several advantages over experiential rule-based systems. A principal shortcoming of MBD is that MBD learns nothing from any given example. An MBD system facing the same task a second time will incur the same computational effort as that incurred the first time. Our earlier work on incorporating explanation-based learning (EBL) in MBD [4] suggested a diagnostic architecture integrating EBL and MBD components. In this architecture, EBL was used to learn diagnostic rules. But the diagnoses proposed by the rules could be erroneous. So constraint suspension testing was used to check all proposed diagnoses. Insisting on perfect accuracy causes the performance of this scheme for "learning while doing" to deteriorate rapidly with the size of the device to be diagnosed. In this paper, we describe a method for trading off accuracy for efficiency. In this approach, most diagnosis problems are handled by the associational rules learned from previous problems. Model-based reasoning and learning are activated only when performance drops below a given threshold. We present empirical results on circuits of increasing number of components illustrating how this approach scales up

Performance of a Brief Assessment Tool for Identifying Substance Use Disorders

Objective: Evaluation of the performance of a brief assessment tool for identifying substance use disorders. The Triage Assessment for Addictive Disorders (TAAD) is a triage instrument that provides professionals with a tool to evaluate indications of current substance use disorders in accordance with the DSM-IV diagnostic criteria. The TAAD is a 31-item structured interview that addresses both alcohol and other drug issues to discriminate among those with no clear indications of a diagnosis, those with definite, current indications of abuse or dependence, and those with inconclusive diagnostic indications. Methods: Employing a sample of 1325 women between the ages of 18 and 60, reliability estimates and problem profiles produced by the TAAD were evaluated. Results: The Cronbach alpha coefficients for internal consistency for both the alcohol and drug dependence scales were .92. The alpha coefficients for the alcohol and drug abuse scales were .83 and .84 respectively. The diagnostic profiles elicited from the TAAD indicate that alcohol and drug dependences are the more definitive and distinct syndromes compared with the abuse syndromes. Conclusions: The diagnostic profiles from this sample are consistent with previous research. The Cronbach alpha coefficients suggest that the TAAD provides an internally consistent index for alcohol and drug dependence and abuse. Implications for use in clinical practice and the need for further research regarding the psychometric properties of the TAAD are discussed

An Essay towards an Epistemology of Responsibility: A Probabilistic Approach

This paper tries to develop an epistemological analysis on the notion of responsibility. After pointing out a peculiar kind of uncertainties concerning the notion of responsibility, I focus upon the issue of criminal responsibility, taking the case of mentally disordered offenders into account, and propose the distinction of the phases between sentence and practice with applying Slobogin's idea of integrationism. Finally, I propose a probabilistic approach to the problem of responsibility through considering the idea of relevance ratio, leading to my final proposal concerning the concept of the degrees of responsibility

Mobile Device Digital Photography for Teledermatology Consultation: Real-Life Situations

Objective: The use of mobile phones for teledermatology consultations is increasing. In this study, we aimed to describe photographic problems in teledermatology performed via mobile phones and their effects on diagnostic decision-making. Materials and Methods: Three dermatologists independently reviewed the medical histories and photographs of patients taken by primary-care physicians for teledermatology between January 2018 and August 2020. The consensus of the dermatologists’ decision-making was categorized into “definite diagnoses given,” “probable diagnoses given,” and “unable to provide any diagnosis.” Relationships between photographic errors and dermatologist decision-making were investigated. Factors related to photographic problems were evaluated. Results: In all, 899 images from 220 patients were reviewed. The most common purpose of teledermatology was to make a diagnosis. The most frequent diagnoses were eczema, infection, and autoimmune diseases. Consultants gave definite diagnoses for 63.2% of patients and probable diagnoses for another 29.5%. However, diagnoses were not made in 7.3% of cases. Defocusing and non-eczematous lesions were significantly associated with the inability to give diagnoses (P = 0.002 and 0.037, respectively). Pictures from peripheral areas showed higher frequencies of distortion errors, improper framing, wasted space, and improper background, while truncal regions tended to have lighting problems. The outpatient department setting was associated with a lack of overview and defocusing. Conclusion: Focusing was the central factor for making diagnoses in teledermatology. Lighting should be more concerned in truncal regions. While using smartphone cameras, distortion should be aware. These factors should be considered to improve the effectiveness of teledermatology

On the construction of hierarchic models

One of the main problems in the field of model-based diagnosis of technical systems today is finding the most useful model or models of the system being diagnosed. Often, a model showing the physical components and the connections between them is all that is available. As systems grow larger and larger, the run-time performance of diagnostic algorithms decreases considerably when using these detailed models. A solution to this problem is using a hierarchic model. This allows us to first diagnose the system using an abstract model, and then use this solution to guide the diagnostic process using a more detailed model. The main problem with this approach is acquiring the hierarchic model. We give a generic hierarchic diagnostic algorithm and show how the use of certain classes of hierarchic models can increase the performance of this algorithm. We then present linear time algorithms for the automatic construction of these hierarchic models, using the detailed model and extra information about cost of probing points and invertibility of components