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Case Based Learning

Jördis-Ann Schüler, Steffen Neumann, Matthias Müller-Hannemann, Wolfgang Brandt
Identification and structural determination of small molecules by mass spectrometry is an important step in chemistry and biochemistry. However, the chemically realistic annotation of a fragment ion spectrum can be a difficult challenge. We developed ChemFrag, for the detection of fragmentation pathways and the annotation of fragment ions with chemically reasonable structures. ChemFrag combines a quantum chemical with a rule-based approach. For different doping substances as test instances, ChemFrag correctly annotates fragment ions...
August 13, 2018: Journal of Mass Spectrometry: JMS
Balazs Harangi
Skin cancer is a major public health problem with over 123,000 newly diagnosed cases worldwide in every year. Melanoma is the deadliest form of skin cancer, responsible for over 9,000 deaths in the United States each year. Thus, reliable automatic melanoma screening systems would provide a great help for clinicians to detect the malignant skin lesions as early as possible. In the last five years, the efficiency of deep learning-based methods increased dramatically and their performances seem to outperform conventional image processing methods in classification tasks...
August 10, 2018: Journal of Biomedical Informatics
Yiye Zhang, Richard Trepp, Weiguang Wang, Jorge Luna, David K Vawdrey, Victoria Tiase
Development and maintenance of order sets is a knowledge-intensive task for off-the-shelf machine-learning algorithms alone. We hypothesize that integrating clinical knowledge with machine learning can facilitate effective development and maintenance of order sets while promoting best practices in ordering. To this end, we simulated the revision of an "AM Lab Order Set" under 6 revision approaches. Revisions included changes in the order set content or default settings through 1) population statistics, 2) individualized prediction using machine learning, and 3) clinical knowledge...
August 7, 2018: Journal of the American Medical Informatics Association: JAMIA
Timothy J Bauler, Shanna Cole, Tyler Gibb, Richard Van Enk, Larry Lutwick, Bonny L Dickinson
In medical and healthcare-related education, case-based learning (CBL) is a teaching strategy that uses clinical cases to engage students in active learning using course concepts to solve important problems. Here we describe the design and implementation of a CBL module to teach first year medical students about the human immunodeficiency virus (HIV), acute retroviral syndrome, clinical progression to acquired immunodeficiency syndrome, HIV diagnostics, assays used to assess stage of disease and response to antiretroviral treatment, and highly active antiretroviral therapy...
2018: Journal of Microbiology & Biology Education: JMBE
Manuel Lillo-Crespo, Jorge Riquelme, Rhoda Macrae, Wilson De Abreu, Elizabeth Hanson, Iva Holmerova, Mª José Cabañero, Rosario Ferrer, Debbie Tolson
BACKGROUND: There is a paucity of robust research concerning the care experiences of peoplewith advanced dementia within Europe. It is essential to understand these experiences if weare to address care inequalities and create impactful dementia policies to improve servicesthat support individuals and enable family caring. OBJECTIVES: To identify the strengths and weaknesses in daily life perceived by people with dementia and family caring across Europe by exemplifying experiences and the range of typical care settings for advanced dementia care in seven partner countries...
2018: Global Health Action
Hanna Sandelowsky, Ingvar Krakau, Sonja Modin, Björn Ställberg, Sven-Erik Johansson, Anna Nager
OBJECTIVES: To study the effects of continuing medical education (CME) about chronic obstructive pulmonary disease (COPD) for general practitioners (GPs) by comparing two commonly used CME methods with each other and no CME (reference group). DESIGN: A pragmatic cluster randomised controlled trial with primary healthcare centres (PHCCs) as units of randomisation. SETTING, PARTICIPANTS AND INTERVENTIONS: 24 PHCCs in Stockholm County, Sweden, were randomised into two CME intervention arms: case method learning (CM) (n=12) and traditional lectures (TL) (n=12)...
August 10, 2018: BMJ Open
Bulat Ibragimov, Diego Toesca, Daniel Chang, Yixuan Yuan, Albert Koong, Lei Xing
BACKGROUND: Accurate prediction of radiation toxicity of healthy organs-at-risks (OARs) critically determines the radiation therapy (RT) success. The existing dose volume histogram-based metric may grossly under/over-estimate the therapeutic toxicity after 27% in liver RT and 50% in head-and-neck RT. We propose the novel paradigm for toxicity prediction by leveraging the enormous potential of deep learning and go beyond the existing dose/volume histograms. EXPERIMENTAL DESIGN: We employed a database of 125 liver stereotactic body RT (SBRT) cases with follow-up data to train deep learning-based toxicity predictor...
August 11, 2018: Medical Physics
Ying Shen, Daoyuan Chen, Buzhou Tang, Min Yang, Kai Lei
BACKGROUND: Entropy has become increasingly popular in computer science and information theory because it can be used to measure the predictability and redundancy of knowledge bases, especially ontologies. However, current entropy applications that evaluate ontologies consider only single-point connectivity rather than path connectivity, and they assign equal weights to each entity and path. RESULTS: We propose an Entropy-Aware Path-Based (EAPB) metric for ontology quality by considering the path information between different vertices and textual information included in the path to calculate the connectivity path of the whole network and dynamic weights between different nodes...
August 10, 2018: Journal of Biomedical Semantics
Sean Tackett, Mark Raymond, Rishi Desai, Steven A Haist, Amy Morales, Shiv Gaglani, Stephen G Clyman
PURPOSE: Adaptive learning requires frequent and valid assessments for learners to track progress against their goals. This study determined if multiple-choice questions (MCQs) "crowdsourced" from medical learners could meet the standards of many large-scale testing programs. METHODS: Users of a medical education app (, Baltimore, MD) volunteered to submit case-based MCQs. Eleven volunteers were selected to submit MCQs targeted to second year medical students...
August 10, 2018: Medical Teacher
Ann Marie P Mauro, Debora L Tracey, Maria Torchia LoGrippo, Diane Brienza-Arcilla, Mona Williams-Gregory, Suzanne Shugg, Angelica Bravo, Claire Byrne
Entry-level nurses require health promotion, chronic disease self-management, care coordination, data utilization, and evidence translation competencies to address complex population health needs. An innovative PhD-DNP faculty collaboration implemented a descriptive survey design to evaluate simulation-based strategies using an unfolding chronically ill adult case to address population health. Results showed the PhD-DNP faculty team was effective in developing clinically meaningful learning experiences to assist baccalaureate students to develop population health competencies...
September 2018: Nursing Education Perspectives
Michał Byra, Grzegorz Styczynski, Cezary Szmigielski, Piotr Kalinowski, Łukasz Michałowski, Rafał Paluszkiewicz, Bogna Ziarkiewicz-Wróblewska, Krzysztof Zieniewicz, Piotr Sobieraj, Andrzej Nowicki
PURPOSE: The nonalcoholic fatty liver disease is the most common liver abnormality. Up to date, liver biopsy is the reference standard for direct liver steatosis quantification in hepatic tissue samples. In this paper we propose a neural network-based approach for nonalcoholic fatty liver disease assessment in ultrasound. METHODS: We used the Inception-ResNet-v2 deep convolutional neural network pre-trained on the ImageNet dataset to extract high-level features in liver B-mode ultrasound image sequences...
August 9, 2018: International Journal of Computer Assisted Radiology and Surgery
Ana M Montalvo, Jorge Fraga, Orestes Blanco, Daniel González, Lianet Monzote, Lynn Soong, Virginia Capó
Background: Leishmaniasis is a neglected parasitic disease caused by Leishmania spp., which is not endemic in Cuba. However, several factors (such as human activities, climate changes, and tourism) have led to an increase in the number of leishmaniasis cases in all regions, raising diagnosis and surveillance issues. We aim to present the retrospective analysis of 16 human cases suspicious of leishmaniasis, which were received during 2006-2016 for diagnosis at the Department of Parasitology from the Institute of Tropical Medicine Pedro Kourí, Cuba...
2018: Tropical Diseases, Travel Medicine and Vaccines
Qi Feng, Yuanjun Chen, Zhengluan Liao, Hongyang Jiang, Dewang Mao, Mei Wang, Enyan Yu, Zhongxiang Ding
Background: Alzheimer's disease (AD) is a progressive neurodegenerative disease that causes the decline of some cognitive impairments. The present study aimed to identify the corpus callosum (CC) radiomic features related to the diagnosis of AD and build and evaluate a classification model. Methods: Radiomics analysis was applied to the three-dimensional T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) images of 78 patients with AD and 44 healthy controls (HC). The CC, in each subject, was segmented manually and 385 features were obtained after calculation...
2018: Frontiers in Neurology
Mateusz Buda, Atsuto Maki, Maciej A Mazurowski
In this study, we systematically investigate the impact of class imbalance on classification performance of convolutional neural networks (CNNs) and compare frequently used methods to address the issue. Class imbalance is a common problem that has been comprehensively studied in classical machine learning, yet very limited systematic research is available in the context of deep learning. In our study, we use three benchmark datasets of increasing complexity, MNIST, CIFAR-10 and ImageNet, to investigate the effects of imbalance on classification and perform an extensive comparison of several methods to address the issue: oversampling, undersampling, two-phase training, and thresholding that compensates for prior class probabilities...
July 29, 2018: Neural Networks: the Official Journal of the International Neural Network Society
Jianlin Shi, John F Hurdle
OBJECTIVE: To develop and evaluate an efficient Trie structure for large-scale, rule-based clinical natural language processing (NLP), which we call n-trie. BACKGROUND: Despite the popularity of machine learning techniques in natural language processing, rule-based systems boast important advantages: distinctive transparency, ease of incorporating external knowledge, and less demanding annotation requirements. However, processing efficiency remains a major obstacle for adopting standard rule-base NLP solutions in big data analyses...
August 6, 2018: Journal of Biomedical Informatics
Miranda Huffman, Sarah Gustafson, Souvik Chatterjee, Marc Rabner, Shantanu Nundy, Mary M Gerkovich, Scott M Wright
PURPOSE: Adaptive learning emerges when precise assessment informs delivery of educational materials. This study will demonstrate how data from Human Dx, a case-based e-learning platform, can characterize an individual's diagnostic reasoning skills, and deliver tailored content to improve accuracy. METHODS: Pearson Chi-square analysis was used to assess variability in accuracy across three groups of participants (attendings, residents, and medical students) and three categories of cases (core medical, surgical, and other)...
August 9, 2018: Medical Teacher
Martha Peñuela-Epalza, Karla De la Hoz
BACKGROUND: Concept maps and case-based learning (CBL) are recognized and useful strategies to enhance undergraduate medical learning. However, research on the use of a mixed approach is limited. AIMS: To incorporate serial concept mapping (CM) into CBL tutorials, to explore students' perspectives on the worth of the method to better understand patients' problems and elicit diagnoses, and to assess the student's learning. METHODS: We designed a four-phase method of CBL that incorporated serial mapping to assist students in the process of knowledge construction regarding the underlying principles of the patients' present complaints, the recognition of disease patterns and the eliciting of diagnostic hypotheses...
August 9, 2018: Medical Teacher
Maximilian Treder, Jost Lennart Lauermann, Nicole Eter
PURPOSE: To automatically detect and classify geographic atrophy (GA) in fundus autofluorescence (FAF) images using a deep learning algorithm. METHODS: In this study, FAF images of patients with GA, a healthy comparable group and a comparable group with other retinal diseases (ORDs) were used to train a multi-layer deep convolutional neural network (DCNN) (1) to detect GA and (2) to differentiate in GA between a diffuse-trickling pattern (dt-GA) and other GA FAF patterns (ndt-GA) in FAF images...
August 8, 2018: Graefe's Archive for Clinical and Experimental Ophthalmology
B C Rao, Ramakrishna Prasad
The term "principles" refers to a set of defining attributes and values that lie at the heart of a discipline. These are largely discovered by reflection and practice rather than learned by formal instruction. This article is written as a reflective dialogue between two teachers of family medicine, one who has been practicing for nearly five decades and another with training in contemporary academic family medicine, using a selection of case stories drawn from the practice of the first author. Several principles of family medicine such as "broad-based specialty"; "person and family orientation"; "continuity of care"; "community based care"; "building a trusting relationship"; "counseling"; and "an effective steward of resources" are highlighted...
March 2018: Journal of Family Medicine and Primary Care
Kanika Mehrotra, Prabhat Chand, Mrunal Bandawar, Mallikarjun Rao Sagi, Sandeepa Kaur, Aurobind G, Aravind Raj, Sumi Jain, Miriam Komaromy, Pratima Murthy, Sanjeev Arora
The present study was conducted to ascertain the effectiveness of Project ECHO, a Hub and Spokes tele-mentoring model to bridge the urban-rural divide in mental health and addiction care in the context of a developing country like India. The Counsellors from 11 rural and underserved districts of Chhattisgarh were periodically connected to NIMHANS multidisciplinary specialists by smartphone app and underwent virtual mentoring to learn and translate "best practices" in Mental health and Addiction by using "patient-centric learning", a core component of NIMHANS ECHO model...
July 18, 2018: Asian Journal of Psychiatry
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