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Case Based Learning

Gustav Burström, Christian Buerger, Jurgen Hoppenbrouwers, Rami Nachabe, Cristian Lorenz, Drazenko Babic, Robert Homan, John M Racadio, Michael Grass, Oscar Persson, Erik Edström, Adrian Elmi Terander
OBJECTIVEThe goal of this study was to develop and validate a system for automatic segmentation of the spine, pedicle identification, and screw path suggestion for use with an intraoperative 3D surgical navigation system.METHODSCone-beam CT (CBCT) images of the spines of 21 cadavers were obtained. An automated model-based approach was used for segmentation. Using machine learning methodology, the algorithm was trained and validated on the image data sets. For measuring accuracy, surface area errors of the automatic segmentation were compared to the manually outlined reference surface on CBCT...
March 22, 2019: Journal of Neurosurgery. Spine
Shwetha Iyer, Erin Goss, Casey Browder, Gerald Paccione, Julia Arnsten
Background Errors in medicine are common and often tied to diagnosis. Educating physicians about the science of cognitive decision-making, especially during medical school and residency when trainees are still forming clinical habits, may enhance awareness of individual cognitive biases and has the potential to reduce diagnostic errors and improve patient safety. Methods The authors aimed to develop, implement and evaluate a clinical reasoning curriculum for Internal Medicine residents. The authors developed and delivered a clinical reasoning curriculum to 47 PGY2 residents in an Internal Medicine Residency Program at a large urban hospital...
March 22, 2019: Diagnosis
Ahammed Muneer K V, V R Rajendran, Paul Joseph K
Computer aided diagnosis using artificial intelligent techniques made tremendous improvement in medical applications especially for easy detection of tumor area, tumor type and grades. This paper presents automatic glioma tumor grade identification from magnetic resonant images using Wndchrm tool based classifier (Weighted Neighbour Distance using Compound Heirarchy of Algorithms Representing Morphology) and VGG-19 deep convolutional neural network (DNN). For experimentation, DICOM images are collected from reputed government hospital and the proposed intelligent system categorized the tumor into four grades such as low grade glioma, oligodendroglioma, anaplastic glioma and glioblastoma multiform...
March 21, 2019: Journal of Medical Systems
Aisling Kerr, Hannah O'Connor, Teresa Pawlikowska, Paul Gallagher, Judith Strawbridge
BACKGROUND: Integrated health professions curricula aim to produce graduates who are capable of meeting current and future healthcare needs. This is reflected in pharmacy education where integration is increasingly advocated by pharmacy regulators as the perceived optimal way of preparing students for registration as pharmacists. There is, however, no definition of integration. Integration can be described according to a model of horizontal, vertical or spiral integration. It can also be described by the themes used to integrate, such as a systems-based approach or by integrative teaching and learning approaches...
March 15, 2019: Research in Social & Administrative Pharmacy: RSAP
S F La Vincente, C von Mollendorf, M Ulziibayar, C Satzke, L Dashtseren, K K Fox, E M Dunne, C D Nguyen, J de Campo, M de Campo, H Thomson, G Surenkhand, S Demberelsuren, S Bujinlkham, L A H Do, D Narangerel, T Cherian, T Mungun, E K Mulholland
BACKGROUND: Streptococcus pneumoniae causes substantial morbidity and mortality among children. The introduction of pneumococcal conjugate vaccines (PCV) has the potential to dramatically reduce disease burden. As with any vaccine, it is important to evaluate PCV impact, to help guide decision-making and resource-allocation. Measuring PCV impact can be complex, particularly to measure impact on one of the most common and significant diseases caused by the pneumococcus, namely pneumonia...
March 21, 2019: BMC Public Health
Charlie Beirnaert, Laura Peeters, Pieter Meysman, Wout Bittremieux, Kenn Foubert, Deborah Custers, Anastasia Van der Auwera, Matthias Cuykx, Luc Pieters, Adrian Covaci, Kris Laukens
Data analysis for metabolomics is undergoing rapid progress thanks to the proliferation of novel tools and the standardization of existing workflows. As untargeted metabolomics datasets and experiments continue to increase in size and complexity, standardized workflows are often not sufficiently sophisticated. In addition, the ground truth for untargeted metabolomics experiments is intrinsically unknown and the performance of tools is difficult to evaluate. Here, the problem of dynamic multi-class metabolomics experiments was investigated using a simulated dataset with a known ground truth...
March 20, 2019: Metabolites
Shasha Li, Xuchun Ye, Wenting Chen
BACKGROUND: Case-based Learning was an effective and highly efficient teaching approach that was extensively applied in education systems across a variety of countries. Critical thinking ability is an important indicator for access the study ability for baccalaureate nursing education. OBJECTIVES: The study aimed to explore the effect of "nursing case-based learning" course on the critical thinking ability of nursing student. PARTICIPANTS: A total of 80 students who were in Junior were included in this study...
March 14, 2019: Nurse Education in Practice
Fabio Fabris, Daniel Palmer, João Pedro de Magalhães, Alex A Freitas
Biologists very often use enrichment methods based on statistical hypothesis tests to identify gene properties that are significantly over-represented in a given set of genes of interest, by comparison with a 'background' set of genes. These enrichment methods, although based on rigorous statistical foundations, are not always the best single option to identify patterns in biological data. In many cases, one can also use classification algorithms from the machine-learning field. Unlike enrichment methods, classification algorithms are designed to maximize measures of predictive performance and are capable of analysing combinations of gene properties, instead of one property at a time...
March 20, 2019: Briefings in Bioinformatics
Kishore Balasubramanian, N P Ananthamoorthy
Retinal image analysis relies on the effectiveness of computational techniques to discriminate various abnormalities in the eye like diabetic retinopathy, macular degeneration and glaucoma. The onset of the disease is often unnoticed in case of glaucoma, the effect of which is felt only at a later stage. Diagnosis of such degenerative diseases warrants early diagnosis and treatment. In this work, performance of statistical and textural features in retinal vessel segmentation is evaluated through classifiers like extreme learning machine, support vector machine and Random Forest...
March 20, 2019: Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine
Tsung-Ting Kuo, Rodney A Gabriel, Lucila Ohno Machado
OBJECTIVE: Decentralized privacy-preserving predictive modeling enables multiple institutions to learn a more generalizable model on healthcare or genomic data by sharing the partially trained models instead of patient-level data, while avoiding risks such as single point of control. State-of-the-art blockchain-based methods remove the "server" role but can be less accurate than models that rely on a server. Therefore, we aim at developing a general model sharing framework to preserve predictive correctness, mitigate the risks of a centralized architecture, and compute the models in a fair way...
March 20, 2019: Journal of the American Medical Informatics Association: JAMIA
Wooseok Yi, Junki Park, Jae-Joon Kim
The probabilistic Bayesian inference of real-time input data is becoming more popular, and the importance of semisupervised learning is growing. We present a classification restricted Boltzmann machine (ClassRBM)-based hardware accelerator with on-chip semisupervised learning and Bayesian inference capability. ClassRBM is a specific type of Markov network that can perform classification tasks and reconstruct its input data. ClassRBM has several advantages in terms of hardware implementation compared to other backpropagation-based neural networks...
March 15, 2019: IEEE Transactions on Neural Networks and Learning Systems
Sunan Cui, Yi Luo, Huan-Hsin Tseng, Randall K Ten Haken, Issam El Naqa
PURPOSE: There has been burgeoning interest in applying machine learning methods for predicting radiotherapy outcomes. However, the imbalanced ratio of a large number of variables to a limited sample size in radiation oncology constitutes a major challenge. Therefore, dimensionality reduction methods can be a key to success. The study investigates and contrasts the application of traditional machine learning methods and deep learning approaches for outcome modeling in radiotherapy. In particular, new joint architectures based on variational autoencoder (VAE) for dimensionality reduction are presented and their application is demonstrated for the prediction of lung radiation pneumonitis (RP) from a large-scale heterogeneous dataset...
March 19, 2019: Medical Physics
Sagar V Parikh, Jolene R Bostwick, Danielle S Taubman
OBJECTIVE: Psychopharmacology requires practitioners to continually upgrade knowledge and skills, but attendance at live continuing medical education events presents many barriers. In addition, technology has generated new learning approaches. In response, a videoconference-based course on psychopharmacology was developed and evaluated for feasibility and acceptability. Specific goals included whether learners would engage and whether the technology would work well for both learners and instructors...
March 19, 2019: Academic Psychiatry
Uma Sharan Tiwari, Ankita Aishwarya, Akanksha Bhale
Aim. To find out the influence of learning effect on various reliability parameters and global indices of standard automated perimetry in cases of primary open angle glaucoma. Method. Thirty eyes of 30 patients of Primary Open Angle Glaucoma constituted material for this prospective observational study. All the patients underwent standard automated perimetry three times on three different days within one week on G program of Octopus Visual Field Analyzer. The details of reliability indices, global indices, and test duration were compiled and analyzed...
October 2018: Romanian Journal of Ophthalmology
Syeda Sadia Fatima, Kulsoom Ghias, Kauser Jabeen, Saniya Sabzwari
Background Problem-based learning (PBL) is one of the main pedagogical approaches utilized in the undergraduate medical education (UGME) program at a private medical college in Karachi, Pakistan. Video-enhanced cases and formative assessments were introduced at the end of PBL sessions to evaluate their effectiveness in enhancing student engagement. Methods A mixed methods study was conducted with Year 2 medical students (n=102; divided into 11 groups) and faculty (n=11) facilitating the PBL process. Of the 10 PBL cases, five were converted to video-enhanced cases and five were kept as paper-based, "traditional" cases...
January 6, 2019: Curēus
Ke Lin, Liang Gong, Yixiang Huang, Chengliang Liu, Junsong Pan
Powdery mildew is a common disease in plants, and it is also one of the main diseases in the middle and final stages of cucumber ( Cucumis sativus ). Powdery mildew on plant leaves affects the photosynthesis, which may reduce the plant yield. Therefore, it is of great significance to automatically identify powdery mildew. Currently, most image-based models commonly regard the powdery mildew identification problem as a dichotomy case, yielding a true or false classification assertion. However, quantitative assessment of disease resistance traits plays an important role in the screening of breeders for plant varieties...
2019: Frontiers in Plant Science
G Maragatham, Shobana Devi
The combination of big data and deep learning is a world-shattering technology that can make a great impact on any industry if used in a proper way. With the availability of large volume of health care datasets and progressions in deep learning techniques, systems are now well equipped in diagnosing many health problems. Utilizing the intensity of substantial historical information in electronic health record (EHR), we built up, a conventional predictive temporal model utilizing recurrent neural systems (RNN) like LSTM and connected to longitudinal time stepped EHR...
March 19, 2019: Journal of Medical Systems
Wei Zhang, Zheng-Rong Li, Zhi Li
BACKGROUND: Problem-Based-Learning (PBL) has been widely accepted in student-centered medical education. Since WeChat is the most popular communication app in China, we have chosen to use WeChat as new platform for online PBL in order to reduce the limitations of traditional PBL in dental practical clerkships. OBJECTIVE: This study aims to demonstrate the feasibility and acceptability of online PBL using WeChat (WeChat-PBL) in a dental practical clerkship. METHODS: A total of 72 students in a dental practical clerkship and 10 tutors participated in this study from June to August 2017...
March 19, 2019: Journal of Medical Internet Research
Aderibigbe Israel Adekitan, Odunayo Salau
Research studies on educational data mining are on the increase due to the benefits obtained from the knowledge acquired from machine learning processes which help to improve decision making processes in higher institutions of learning. In this study, predictive analysis was carried out to determine the extent to which the fifth year and final Cumulative Grade Point Average (CGPA) of engineering students in a Nigerian University can be determined using the program of study, the year of entry and the Grade Point Average (GPA) for the first three years of study as inputs into a Konstanz Information Miner (KNIME) based data mining model...
February 2019: Heliyon
Ning Zhao, Zhigang Bai, Changsheng Teng, Zhongtao Zhang
We investigated the learning curve for using intraoperative neural monitoring technology in thyroid cancer, with a view to reducing recurrent laryngeal nerve injury complications. Radical or combined radical surgery for thyroid cancer was performed in 82 patients with thyroid cancer and 147 recurrent laryngeal nerves were dissected. Intraoperative neural monitoring technology was applied and the "four-step method" used to monitor recurrent laryngeal nerve function. When the intraoperative signal was attenuated by more than 50%, recurrent laryngeal nerve injury was diagnosed, and the point and causes of injury were determined...
2019: BioMed Research International
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