How doctors diagnose diseases and prescribe treatments: an fMRI study of diagnostic salience

Understanding the brain mechanisms involved in diagnostic reasoning may contribute to the development of methods that reduce errors in medical practice. In this study we identified similar brain systems for diagnosing diseases, prescribing treatments, and naming animals and objects using written information as stimuli. Employing time resolved modeling of blood oxygen level dependent (BOLD) responses enabled time resolved (400 milliseconds epochs) analyses. With this approach it was possible to study neural processes during successive stages of decision making. Our results showed that highly diagnostic information, reducing uncertainty about the diagnosis, decreased monitoring activity in the frontoparietal attentional network and may contribute to premature diagnostic closure, an important cause of diagnostic errors. We observed an unexpected and remarkable switch of BOLD activity within a right lateralized set of brain regions related to awareness and auditory monitoring at the point of responding. We propose that this neurophysiological response is the neural substrate of awareness of one’s own (verbal) response. Our results highlight the intimate relation between attentional mechanisms, uncertainty, and decision making and may assist the advance of approaches to prevent premature diagnostic closure

Diagnostics in the Extendable Integrated Support Environment (EISE)

Extendable Integrated Support Environment (EISE) is a real-time computer network consisting of commercially available hardware and software components to support systems level integration, modifications, and enhancement to weapons systems. The EISE approach offers substantial potential savings by eliminating unique support environments in favor of sharing common modules for the support of operational weapon systems. An expert system is being developed that will help support diagnosing faults in this network. This is a multi-level, multi-expert diagnostic system that uses experiential knowledge relating symptoms to faults and also reasons from structural and functional models of the underlying physical model when experiential reasoning is inadequate. The individual expert systems are orchestrated by a supervisory reasoning controller, a meta-level reasoner which plans the sequence of reasoning steps to solve the given specific problem. The overall system, termed the Diagnostic Executive, accesses systems level performance checks and error reports, and issues remote test procedures to formulate and confirm fault hypotheses

The Galileo PPS expert monitoring and diagnostic prototype

The Galileo PPS Expert Monitoring Module (EMM) is a prototype system implemented on the SUN workstation that will demonstrate a knowledge-based approach to monitoring and diagnosis for the Galileo spacecraft Power/Pyro subsystems. The prototype will simulate an analysis module functioning within the SFOC Engineering Analysis Subsystem Environment (EASE). This document describes the implementation of a prototype EMM for the Galileo spacecraft Power Pyro Subsystem. Section 2 of this document provides an overview of the issues in monitoring and diagnosis and comparison between traditional and knowledge-based solutions to this problem. Section 3 describes various tradeoffs which must be considered when designing a knowledge-based approach to monitoring and diagnosis, and section 4 discusses how these issues were resolved in constructing the prototype. Section 5 presents conclusions and recommendations for constructing a full-scale demonstration of the EMM. A Glossary provides definitions of terms used in this text

In defense of compilation: A response to Davis' form and content in model-based reasoning

In a recent paper entitled 'Form and Content in Model Based Reasoning', Randy Davis argues that model based reasoning research aimed at compiling task specific rules from underlying device models is mislabeled, misguided, and diversionary. Some of Davis' claims are examined and his basic conclusions are challenged about the value of compilation research to the model based reasoning community. In particular, Davis' claim is refuted that model based reasoning is exempt from the efficiency benefits provided by knowledge compilation techniques. In addition, several misconceptions are clarified about the role of representational form in compilation. It is concluded that techniques have the potential to make a substantial contribution to solving tractability problems in model based reasoning

Aggressive and violent behavior - the result of malfunction in the neural circuit regulating emotion

Mental illness is currently diagnosed using subjective observational criteria as outlined in the 5th Edition of the Diagnostic and Statistical Manual (DSM-V), yet many have argued for the medicalization of the diagnosis of mental illness by incorporating biomedical and neuroanatomical criteria. The following literature review explores the neural circuit responsible for regulating emotion, as well as the structural and chemical alterations to this circuit that have been shown to correlate with aggressive and/or violent behaviors characteristic of certain types of mental illness. The neural circuit regulating emotion is comprised of the prefrontal cortex, the subcortical limbic system, the dopaminergic pathway, the serotonergic pathway, catecholaminergic neurons, and GABAergic neurons. Alterations to these structures or chemicals have been associated with major depressive disorder, suicidal ideations, substance use disorders, schizophrenia, and personality disorders. Medicalization of mental illness has the potential to serve two purposes – first, to standardize diagnosis and treatment of mental illness, and second, to decrease the stigma often associated with mental illness – and to improve outcomes for those patients living with mental illness

The Impact of Implicit Bias on the Overdiagnosis of Schizophrenia

According to the American Psychiatric Association (2017), Black Americans are more likely to be diagnosed with schizophrenia and less likely to be diagnosed with a mood disorder in compassion to their White counterparts presenting with the same symptoms. To address mechanisms of implicit bias and the implications for mental health practice, it is imperative to investigate the diagnostic process of students who will enter the field of mental health. According to the Council on Social Work Education, 80% of MSW graduates work in positions providing micro-level direct service to individuals, families, and groups (CSWE, 2019). Moreover, 80% of MSW graduates are licensed clinicians or plan on pursuing licensure within the next five years. The purpose of this study was to explore the clinician-client dynamic by analyzing clinical-decision making in students currently enrolled in Master of Social Work (MSW) graduate programs. The goal of the study was to gain insight into the significance of race during the process of diagnosing individuals with mood disorders. The quantitative study was designed to examine the impact implicit bias on the diagnosis of mental illness using a vignette and clinical impressions survey. Based on the findings from 73 surveys, MSW students tend to under-diagnose major depressive disorder and over diagnose schizoaffective or schizophrenia disorder when assessing a Black client. However, despite these findings, race/ethnicity did not emerge as significantly associated with overdiagnosis; only the severity of diagnosis was associated with the overdiagnosis