Juvenile myoclonic epilepsy: The impact of clinical variables and psychiatric disorders on executive profile assessed with a comprehensive neuropsychological battery

Executive dysfunction is reported in juvenile myoclonic epilepsy (JME). However, batteries employed in previous studies included no more than three tests of executive function. In this study, we aimed to assess executive and attentional functions in JME using a comprehensive battery of eight tests (encompassing fifteen subtests). We also evaluated neuropsychological profiles using a clinical criterion of severity and correlated these findings with epilepsy clinical variables and the presence of psychiatric disorders. We prospectively evaluated 42 patients with JME and a matched control group with Digit Span tests (forward and backward), Stroop Color-Word Test, Trail Making Test, Wisconsin Card-Sorting Test, Matching Familiar Figures Test and Word Fluency Test. We estimated IQ with the Matrix Reasoning and Vocabulary subtests of the Wechsler Abbreviated Intelligence Scale. The patients with JME showed specific deficits in working memory, inhibitory control, concept formation, goal maintenance, mental flexibility, and verbal fluency. We observed attentional deficits in processes such as alertness and attention span and those requiring sustained and divided attention. We found that 83.33% of the patients had moderate or severe executive dysfunction. In addition, attentional and executive impairment was correlated with higher frequency of seizures and the presence of psychiatric disorders. Furthermore, executive dysfunction correlated with a longer duration of epilepsy. Our findings indicate the need for comprehensive neuropsychological batteries in patients with JME, in order to provide a more extensive evaluation of attentional and executive functions and to show that some relevant deficits have been overlooked. (C) 2012 Elsevier Inc. All rights reserved.

Information extraction from clinical and radiology notes for liver cancer staging

Thesis (Ph.D.)--University of Washington, 2016-06

Using secondary knowledge to support decision tree classification of retrospective clinical data

Retrospective clinical data presents many challenges for data mining and machine learning. The transcription of patient records from paper charts and subsequent manipulation of data often results in high volumes of noise as well as a loss of other important information. In addition, such datasets often fail to represent expert medical knowledge and reasoning in any explicit manner. In this research we describe applying data mining methods to retrospective clinical data to build a prediction model for asthma exacerbation severity for pediatric patients in the emergency department. Difficulties in building such a model forced us to investigate alternative strategies for analyzing and processing retrospective data. This paper describes this process together with an approach to mining retrospective clinical data by incorporating formalized external expert knowledge (secondary knowledge sources) into the classification task. This knowledge is used to partition the data into a number of coherent sets, where each set is explicitly described in terms of the secondary knowledge source. Instances from each set are then classified in a manner appropriate for the characteristics of the particular set. We present our methodology and outline a set of experiential results that demonstrate some advantages and some limitations of our approach. © 2008 Springer-Verlag Berlin Heidelberg.

A method for automatically eliciting node weights in a hierarchical knowledge-based structure for reasoning with uncertainty

Hierarchical knowledge structures are frequently used within clinical decision support systems as part of the model for generating intelligent advice. The nodes in the hierarchy inevitably have varying influence on the decisionmaking processes, which needs to be reflected by parameters. If the model has been elicited from human experts, it is not feasible to ask them to estimate the parameters because there will be so many in even moderately-sized structures. This paper describes how the parameters could be obtained from data instead, using only a small number of cases. The original method [1] is applied to a particular web-based clinical decision support system called GRiST, which uses its hierarchical knowledge to quantify the risks associated with mental-health problems. The knowledge was elicited from multidisciplinary mental-health practitioners but the tree has several thousand nodes, all requiring an estimation of their relative influence on the assessment process. The method described in the paper shows how they can be obtained from about 200 cases instead. It greatly reduces the experts’ elicitation tasks and has the potential for being generalised to similar knowledge-engineering domains where relative weightings of node siblings are part of the parameter space.

Designing multiple user perspectives and functionality for clinical decision support systems

Clinical Decision Support Systems (CDSSs) need to disseminate expertise in formats that suit different end users and with functionality tuned to the context of assessment. This paper reports research into a method for designing and implementing knowledge structures that facilitate the required flexibility. A psychological model of expertise is represented using a series of formally specified and linked XML trees that capture increasing elements of the model, starting with hierarchical structuring, incorporating reasoning with uncertainty, and ending with delivering the final CDSS. The method was applied to the Galatean Risk and Safety Tool, GRiST, which is a web-based clinical decision support system (www.egrist.org) for assessing mental-health risks. Results of its clinical implementation demonstrate that the method can produce a system that is able to deliver expertise targetted and formatted for specific patient groups, different clinical disciplines, and alternative assessment settings. The approach may be useful for developing other real-world systems using human expertise and is currently being applied to a logistics domain. © 2013 Polish Information Processing Society.

Classifying clinical decision making:interpreting nursing intuition, heuristics and medical diagnosis

This is the second of two linked papers exploring decision making in nursing. The first paper, 'Classifying clinical decision making: a unifying approach' investigated difficulties with applying a range of decision-making theories to nursing practice. This is due to the diversity of terminology and theoretical concepts used, which militate against nurses being able to compare the outcomes of decisions analysed within different frameworks. It is therefore problematic for nurses to assess how good their decisions are, and where improvements can be made. However, despite the range of nomenclature, it was argued that there are underlying similarities between all theories of decision processes and that these should be exposed through integration within a single explanatory framework. A proposed solution was to use a general model of psychological classification to clarify and compare terms, concepts and processes identified across the different theories. The unifying framework of classification was described and this paper operationalizes it to demonstrate how different approaches to clinical decision making can be re-interpreted as classification behaviour. Particular attention is focused on classification in nursing, and on re-evaluating heuristic reasoning, which has been particularly prone to theoretical and terminological confusion. Demonstrating similarities in how different disciplines make decisions should promote improved multidisciplinary collaboration and a weakening of clinical elitism, thereby enhancing organizational effectiveness in health care and nurses' professional status. This is particularly important as nurses' roles continue to expand to embrace elements of managerial, medical and therapeutic work. Analysing nurses' decisions as classification behaviour will also enhance clinical effectiveness, and assist in making nurses' expertise more visible. In addition, the classification framework explodes the myth that intuition, traditionally associated with nurses' decision making, is less rational and scientific than other approaches.

Cognitive-behavioural phenotype in a group of girls from 1.2 to 12 years old with the Incontinentia Pigmenti syndrome:recommendations for clinical management

Incontinentia Pigmenti (IP, OMIM#308300) is a rare X-linked genomic disorder (about 1,400 cases) that affects the neuroectodermal tissue and Central Nervous System (CNS). The objective of this study was to describe the cognitive-behavioural profile in children in order to plan a clinical intervention to improve their quality of life. A total of 14 girls (age range: from 1 year and 2 months to 12 years and 10 months) with IP and the IKBKG/NEMO gene deletion were submitted to a cognitive assessment including intelligence scales, language and visuo-spatial competence tests, learning ability tests, and a behavioural assessment. Five girls had severe to mild intellectual deficiencies and the remaining nine had a normal neurodevelopment. Four girls were of school age and two of these showed no intellectual disability, but had specific disabilities in calculation and arithmetic reasoning. This is the first description of the cognitive-behavioural profile in relation to developmental age. We stress the importance of an early assessment of learning abilities in individuals with IP without intellectual deficiencies to prevent the onset of any such deficit.

Ethical reasoning through simulation: a phenomenological analysis of student experience

Background
Medical students transitioning into professional practice feel underprepared to deal with the emotional complexities of real-life ethical situations. Simulation-based learning (SBL) may provide a safe environment for students to probe the boundaries of ethical encounters. Published studies of ethics simulation have not generated sufficiently deep accounts of student experience to inform pedagogy. The aim of this study was to understand students’ lived experiences as they engaged with the emotional challenges of managing clinical ethical dilemmas within a SBL environment.

Methods
This qualitative study was underpinned by an interpretivist epistemology. Eight senior medical students participated in an interprofessional ward-based SBL activity incorporating a series of ethically challenging encounters. Each student wore digital video glasses to capture point-of-view (PoV) film footage. Students were interviewed immediately after the simulation and the PoV footage played back to them. Interviews were transcribed verbatim. An interpretative phenomenological approach, using an established template analysis approach, was used to iteratively analyse the data.

Results
Four main themes emerged from the analysis: (1) ‘Authentic on all levels?’, (2)‘Letting the emotions flow’, (3) ‘Ethical alarm bells’ and (4) ‘Voices of children and ghosts’. Students recognised many explicit ethical dilemmas during the SBL activity but had difficulty navigating more subtle ethical and professional boundaries. In emotionally complex situations, instances of moral compromise were observed (such as telling an untruth). Some participants felt unable to raise concerns or challenge unethical behaviour within the scenarios due to prior negative undergraduate experiences.

Conclusions
This study provided deep insights into medical students’ immersive and embodied experiences of ethical reasoning during an authentic SBL activity. By layering on the human dimensions of ethical decision-making, students can understand their personal responses to emotion, complexity and interprofessional working. This could assist them in framing and observing appropriate ethical and professional boundaries and help smooth the transition into clinical practice.

The thinking doctor: clinical decision making in contemporary medicine

Diagnostic errors are responsible for a significant number of adverse events. Logical reasoning and good decision-making skills are key factors in reducing such errors, but little emphasis has traditionally been placed on how these thought processes occur, and how errors could be minimised. In this article, we explore key cognitive ideas that underpin clinical decision making and suggest that by employing some simple strategies, physicians might be better able to understand how they make decisions and how the process might be optimised.

Relations among Topic Knowledge, Individual Interest, and Relational Reasoning, and Critical Thinking in Maternity Nursing

Critical thinking in learners is a goal of educators and professional organizations in nursing as well as other professions. However, few studies in nursing have examined the role of the important individual difference factors topic knowledge, individual interest, and general relational reasoning strategies in predicting critical thinking. In addition, most previous studies have used domain-general, standardized measures, with inconsistent results. Moreover, few studies have investigated critical thinking across multiple levels of experience. The major purpose of this study was to examine the degree to which topic knowledge, individual interest, and relational reasoning predict critical thinking in maternity nurses. For this study, 182 maternity nurses were recruited from national nursing listservs explicitly chosen to capture multiple levels of experience from prelicensure to very experienced nurses. The three independent measures included a domain-specific Topic Knowledge Assessment (TKA), consisting of 24 short-answer questions, a Professed and Engaged Interest Measure (PEIM), with 20 questions indicating level of interest and engagement in maternity nursing topics and activities, and the Test of Relational Reasoning (TORR), a graphical selected response measure with 32 items organized in scales corresponding to four forms of relational reasoning: analogy, anomaly, antithesis, and antinomy. The dependent measure was the Critical Thinking Task in Maternity Nursing (CT2MN), composed of a clinical case study providing cues with follow-up questions relating to nursing care. These questions align with the cognitive processes identified in a commonly-used definition of critical thinking in nursing. Reliable coding schemes for the measures were developed for this study. Key findings included a significant correlation between topic knowledge and individual interest. Further, the three individual difference factors explained a significant proportion of the variance in critical thinking with a large effect size. While topic knowledge was the strongest predictor of critical thinking performance, individual interest had a moderate significant effect, and relational reasoning had a small but significant effect. The findings suggest that these individual difference factors should be included in future studies of critical thinking in nursing. Implications for nursing education, research, and practice are discussed.

Healthcare Computer Reasoning Addressing Chronically Ill Societies Using IoT: Deep Learning AI to the Rescue of Home-Based Healthcare

The authors present a proposal to develop intelligent assisted living environments for home based healthcare. These environments unite the chronical patient clinical history sematic representation with the ability of monitoring the living conditions and events recurring to a fully managed Semantic Web of Things (SWoT). Several levels of acquired knowledge and the case based reasoning that is possible by knowledge representation of the health-disease history and acquisition of the scientific evidence will deliver, through various voice based natural interfaces, the adequate support systems for disease auto management but prominently by activating the less differentiated caregiver for any specific need. With these capabilities at hand, home based healthcare providing becomes a viable possibility reducing the institutionalization needs. The resulting integrated healthcare framework will provide significant savings while improving the generality of health and satisfaction indicators.

Bio-QA-GNN: Reasoning with Language Models and Knowledge Graphs for Interpretable Biomedical Question Answering

Nowadays the idea of injecting world or domain-specific structured knowledge into pre-trained language models (PLMs) is becoming an increasingly popular approach for solving problems such as biases, hallucinations, huge architectural sizes, and explainability lack—critical for real-world natural language processing applications in sensitive fields like bioinformatics. One recent work that has garnered much attention in Neuro-symbolic AI is QA-GNN, an end-to-end model for multiple-choice open-domain question answering (MCOQA) tasks via interpretable text-graph reasoning. Unlike previous publications, QA-GNN mutually informs PLMs and graph neural networks (GNNs) on top of relevant facts retrieved from knowledge graphs (KGs). However, taking a more holistic view, existing PLM+KG contributions mainly consider commonsense benchmarks and ignore or shallowly analyze performances on biomedical datasets. This thesis start from a propose of a deep investigation of QA-GNN for biomedicine, comparing existing or brand-new PLMs, KGs, edge-aware GNNs, preprocessing techniques, and initialization strategies. By combining the insights emerged in DISI's research, we introduce Bio-QA-GNN that include a KG. Working with this part has led to an improvement in state-of-the-art of MCOQA model on biomedical/clinical text, largely outperforming the original one (+3.63\% accuracy on MedQA). Our findings also contribute to a better understanding of the explanation degree allowed by joint text-graph reasoning architectures and their effectiveness on different medical subjects and reasoning types. Codes, models, datasets, and demos to reproduce the results are freely available at: \url{https://github.com/disi-unibo-nlp/bio-qagnn}.

The Clinical Impact Of Cerebellar Grey Matter Pathology In Multiple Sclerosis.

The cerebellum is an important site for cortical demyelination in multiple sclerosis, but the functional significance of this finding is not fully understood. To evaluate the clinical and cognitive impact of cerebellar grey-matter pathology in multiple sclerosis patients. Forty-two relapsing-remitting multiple sclerosis patients and 30 controls underwent clinical assessment including the Multiple Sclerosis Functional Composite, Expanded Disability Status Scale (EDSS) and cerebellar functional system (FS) score, and cognitive evaluation, including the Paced Auditory Serial Addition Test (PASAT) and the Symbol-Digit Modalities Test (SDMT). Magnetic resonance imaging was performed with a 3T scanner and variables of interest were: brain white-matter and cortical lesion load, cerebellar intracortical and leukocortical lesion volumes, and brain cortical and cerebellar white-matter and grey-matter volumes. After multivariate analysis high burden of cerebellar intracortical lesions was the only predictor for the EDSS (p<0.001), cerebellar FS (p = 0.002), arm function (p = 0.049), and for leg function (p<0.001). Patients with high burden of cerebellar leukocortical lesions had lower PASAT scores (p = 0.013), while patients with greater volumes of cerebellar intracortical lesions had worse SDMT scores (p = 0.015). Cerebellar grey-matter pathology is widely present and contributes to clinical dysfunction in relapsing-remitting multiple sclerosis patients, independently of brain grey-matter damage.

An Efficient Protocol For The Generation Of Monocyte Derived Dendritic Cells Using Serum-free Media For Clinical Applications In Post Remission Aml Patients.

Protocols for the generation of dendritic cells (DCs) using serum as a supplementation of culture media leads to reactions due to animal proteins and disease transmissions. Several types of serum-free media (SFM), based on good manufacture practices (GMP), have recently been used and seem to be a viable option. The aim of this study was to evaluate the results of the differentiation, maturation, and function of DCs from Acute Myeloid Leukemia patients (AML), generated in SFM and medium supplemented with autologous serum (AS). DCs were analyzed by phenotype characteristics, viability, and functionality. The results showed the possibility of generating viable DCs in all the conditions tested. In patients, the X-VIVO 15 medium was more efficient than the other media tested in the generation of DCs producing IL-12p70 (p=0.05). Moreover, the presence of AS led to a significant increase of IL-10 by DCs as compared with CellGro (p=0.05) and X-Vivo15 (p=0.05) media, both in patients and donors. We concluded that SFM was efficient in the production of DCs for immunotherapy in AML patients. However, the use of AS appears to interfere with the functional capacity of the generated DCs.

Vascular Pattern In Enchondroma And Chondrosarcoma: Clinical And Immunohistologic Study.

Although cartilaginous tumors have low microvascular density, vessels are important for the provision of nutrition so that the tumor can grow and generate metastasis. The aim of this study was to assess the value of the vascular pattern classification as a prognostic tool in chondrosarcomas (CSs) and its relation with vascular endothelial growth factor (VEGF) expression. This was a retrospective study of 21 enchondromas and 57 conventional CSs. Clinical data and outcome were retrieved from medical files. CSs histologic grades (on a scale of 1 to 3) were determined according to the World Health Organization classification. The vascular pattern (on a scale of A to C) was assessed through CD34, according to Kalinski. CD105 and VEGF were also evaluated. Poor outcome was significantly associated with vascular pattern groups B and C. Higher vascular pattern were 6.5 times more frequent in moderate-grade and high-grade CSs than in grade 1 CS. On multivariate analysis, a clear correlation was found between VEGF overexpression and B/C vascular patterns. Only 18 (benign and malignant) tumors stained for CD105. The results point to the use of the vascular pattern classification as a prognostic tool in CSs and to differentiate low-grade from moderate-grade/high-grade CSs. Vascular pattern might be also used to complement histologic grade, VEGF immunostaining, and microvascular density, for indicating a patient's prognosis. Low-grade CSs develop under low neoangiogenesis, which conforms to the slow growth rate of these tumors.