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https://www.readbyqxmd.com/read/28440283/quantitative-diagnosis-of-breast-tumors-by-morphometric-classification-of-microenvironmental-myoepithelial-cells-using-a-machine-learning-approach
#1
Yoichiro Yamamoto, Akira Saito, Ayako Tateishi, Hisashi Shimojo, Hiroyuki Kanno, Shinichi Tsuchiya, Ken-Ichi Ito, Eric Cosatto, Hans Peter Graf, Rodrigo R Moraleda, Roland Eils, Niels Grabe
Machine learning systems have recently received increased attention for their broad applications in several fields. In this study, we show for the first time that histological types of breast tumors can be classified using subtle morphological differences of microenvironmental myoepithelial cell nuclei without any direct information about neoplastic tumor cells. We quantitatively measured 11661 nuclei on the four histological types: normal cases, usual ductal hyperplasia and low/high grade ductal carcinoma in situ (DCIS)...
April 25, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28438091/analytic-response-curves-of-clinical-breast-cancer-ihc-tests
#2
Kodela Vani, Seshi R Sompuram, Anika K Schaedle, Anuradha Balasubramanian, Steven A Bogen
An important limitation in the field of immunohistochemistry (IHC) is the inability to correlate stain intensity with specific analyte concentrations. Clinical immunohistochemical tests are not described in terms of analytic response curves, namely, the analyte concentrations in a tissue sample at which an immunohistochemical stain (1) is first visible, (2) increases in proportion to the analyte concentration, and (3) ultimately approaches a maximum color intensity. Using a new immunostaining tool ( IHControls), we measured the analytic response curves of the major clinical immunohistochemical tests for human epidermal growth factor receptor type II (HER-2), estrogen receptor (ER), and progesterone receptor (PR)...
May 2017: Journal of Histochemistry and Cytochemistry: Official Journal of the Histochemistry Society
https://www.readbyqxmd.com/read/28435346/mastery-learning-how-is-it-helpful-an-analytical-review
#3
REVIEW
Manjunath Siddaiah-Subramanya, Sabin Smith, James Lonie
The desire to be good at one's work grows out of the aspiration, competition, and a yearning to be the best. Surgeons, in their aim to provide the best care possible to their patients, adopt this behavior to achieve high levels of expert performance through mastery learning, and the surgical training attempts to prepare them optimally to lead a virtuous and productive life. The proponents of the framework reject evidence that suggests that other variables are also necessary to achieve high levels of expert performance...
2017: Advances in Medical Education and Practice
https://www.readbyqxmd.com/read/28435123/is-having-similar-eye-movement-patterns-during-face-learning-and-recognition-beneficial-for-recognition-performance-evidence-from-hidden-markov-modeling
#4
Tim Chuk, Antoni B Chan, Janet Hsiao
The hidden Markov model (HMM)-based approach for eye movement analysis is able to reflect individual differences in both spatial and temporal aspects of eye movements. Here we used this approach to understand the relationship between eye movements during face learning and recognition, and its association with recognition performance. We discovered holistic (i.e., mainly looking at the face center) and analytic (i.e., specifically looking at the two eyes in addition to the face center) patterns during both learning and recognition...
April 20, 2017: Vision Research
https://www.readbyqxmd.com/read/28423788/medical-and-healthcare-curriculum-exploratory-analysis
#5
Martin Komenda, Matěj Karolyi, Andrea Pokorná, Christos Vaitsis
In the recent years, medical and healthcare higher education institutions compile their curricula in different ways in order to cover all necessary topics and sections that the students will need to go through to success in their future clinical practice. A medical and healthcare curriculum consists of many descriptive parameters, which define statements of what, when, and how students will learn in the course of their studies. For the purpose of understanding a complicated medical and healthcare curriculum structure, we have developed a web-oriented platform for curriculum management covering in detail formal metadata specifications in accordance with the approved pedagogical background, namely outcome-based approach...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28423765/evaluation-of-machine-learning-methods-to-predict-coronary-artery-disease-using-metabolomic-data
#6
Henrietta Forssen, Riyaz Patel, Natalie Fitzpatrick, Aroon Hingorani, Adam Timmis, Harry Hemingway, Spiros Denaxas
Metabolomic data can potentially enable accurate, non-invasive and low-cost prediction of coronary artery disease. Regression-based analytical approaches however might fail to fully account for interactions between metabolites, rely on a priori selected input features and thus might suffer from poorer accuracy. Supervised machine learning methods can potentially be used in order to fully exploit the dimensionality and richness of the data. In this paper, we systematically implement and evaluate a set of supervised learning methods (L1 regression, random forest classifier) and compare them to traditional regression-based approaches for disease prediction using metabolomic data...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28421894/how-learning-analytics-can-early-predict-under-achieving-students-in-a-blended-medical-education-course
#7
Mohammed Saqr, Uno Fors, Matti Tedre
AIM: Learning analytics (LA) is an emerging discipline that aims at analyzing students' online data in order to improve the learning process and optimize learning environments. It has yet un-explored potential in the field of medical education, which can be particularly helpful in the early prediction and identification of under-achieving students. The aim of this study was to identify quantitative markers collected from students' online activities that may correlate with students' final performance and to investigate the possibility of predicting the potential risk of a student failing or dropping out of a course...
April 19, 2017: Medical Teacher
https://www.readbyqxmd.com/read/28418117/articulating-the-ideal-50-years-of-interprofessional-collaboration-in-medical-education
#8
Elise Paradis, Mandy Pipher, Carrie Cartmill, J Cristian Rangel, Cynthia R Whitehead
CONTEXT: Health care delivery and the education of clinicians have changed immensely since the creation of the journal Medical Education. In this project, we seek to answer the following three questions: How has the concept of collaboration changed over the past 50 years in Medical Education? Have the participants involved in collaboration shifted over time? Has the idea of collaboration itself been transformed over the past 50 years? METHODS: Starting from a constructionist view of scientific discourse, we used directed content analysis to sample, code and analyse 144 collaboration-related articles over the 50-year life span of Medical Education...
April 18, 2017: Medical Education
https://www.readbyqxmd.com/read/28418055/application-of-machine-statistical-learning-artificial-intelligence-and-statistical-experimental-design-for-the-modeling-and-optimization-of-methylene-blue-and-cd-ii-removal-from-a-binary-aqueous-solution-by-natural-walnut-carbon
#9
H Mazaheri, M Ghaedi, M H Ahmadi Azqhandi, A Asfaram
Analytical chemists apply statistical methods for both the validation and prediction of proposed models. Methods are required that are adequate for finding the typical features of a dataset, such as nonlinearities and interactions. Boosted regression trees (BRTs), as an ensemble technique, are fundamentally different to other conventional techniques, with the aim to fit a single parsimonious model. In this work, BRT, artificial neural network (ANN) and response surface methodology (RSM) models have been used for the optimization and/or modeling of the stirring time (min), pH, adsorbent mass (mg) and concentrations of MB and Cd(2+) ions (mg L(-1)) in order to develop respective predictive equations for simulation of the efficiency of MB and Cd(2+) adsorption based on the experimental data set...
April 18, 2017: Physical Chemistry Chemical Physics: PCCP
https://www.readbyqxmd.com/read/28410982/ehr-based-phenotyping-bulk-learning-and-evaluation
#10
Po-Hsiang Chiu, George Hripcsak
In data-driven phenotyping, a core computational task is to identify medical concepts and their variations from sources of electronic health records (EHR) to stratify phenotypic cohorts. A conventional analytic framework for phenotyping largely uses a manual knowledge engineering approach or a supervised learning approach where clinical cases are represented by variables encompassing diagnoses, medicinal treatments and laboratory tests, among others. In such a framework, tasks associated with feature engineering and data annotation remain a tedious and expensive exercise, resulting in poor scalability...
April 11, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28405214/a-real-time-phenotyping-framework-using-machine-learning-for-plant-stress-severity-rating-in-soybean
#11
Hsiang Sing Naik, Jiaoping Zhang, Alec Lofquist, Teshale Assefa, Soumik Sarkar, David Ackerman, Arti Singh, Asheesh K Singh, Baskar Ganapathysubramanian
BACKGROUND: Phenotyping is a critical component of plant research. Accurate and precise trait collection, when integrated with genetic tools, can greatly accelerate the rate of genetic gain in crop improvement. However, efficient and automatic phenotyping of traits across large populations is a challenge; which is further exacerbated by the necessity of sampling multiple environments and growing replicated trials. A promising approach is to leverage current advances in imaging technology, data analytics and machine learning to enable automated and fast phenotyping and subsequent decision support...
2017: Plant Methods
https://www.readbyqxmd.com/read/28403303/the-game-as-strategy-for-approach-to-sexuality-with-adolescents-theoretical-methodological-re%C3%AF-ections
#12
Vânia de Souza, Maria Flávia Gazzinelli, Amanda Nathale Soares, Marconi Moura Fernandes, Rebeca Nunes Guedes de Oliveira, Rosa Maria Godoy Serpa da Fonseca
OBJECTIVE: To describe the Papo Reto [Straight Talk] game and reflect on its theoretical-methodological basis. METHOD: Analytical study on the process of elaboration of the Papo Reto online game, destined to adolescents aged 15-18 years, with access to the Game between 2014 and 2015. RESULTS: the interactions of 60 adolescents from Belo Horizonte and São Paulo constituted examples of the potentialities of the Game to favor the approach to sexuality with adolescents through simulation of reality, invention and interaction...
April 2017: Revista Brasileira de Enfermagem
https://www.readbyqxmd.com/read/28399328/high-throughput-label-free-single-cell-microalgal-lipid-screening-by-machine-learning-equipped-optofluidic-time-stretch-quantitative-phase-microscopy
#13
Baoshan Guo, Cheng Lei, Hirofumi Kobayashi, Takuro Ito, Yaxiaer Yalikun, Yiyue Jiang, Yo Tanaka, Yasuyuki Ozeki, Keisuke Goda
The development of reliable, sustainable, and economical sources of alternative fuels to petroleum is required to tackle the global energy crisis. One such alternative is microalgal biofuel, which is expected to play a key role in reducing the detrimental effects of global warming as microalgae absorb atmospheric CO2 via photosynthesis. Unfortunately, conventional analytical methods only provide population-averaged lipid amounts and fail to characterize a diverse population of microalgal cells with single-cell resolution in a non-invasive and interference-free manner...
April 11, 2017: Cytometry. Part A: the Journal of the International Society for Analytical Cytology
https://www.readbyqxmd.com/read/28394905/use-of-a-machine-learning-framework-to-predict-substance-use-disorder-treatment-success
#14
Laura Acion, Diana Kelmansky, Mark van der Laan, Ethan Sahker, DeShauna Jones, Stephan Arndt
There are several methods for building prediction models. The wealth of currently available modeling techniques usually forces the researcher to judge, a priori, what will likely be the best method. Super learning (SL) is a methodology that facilitates this decision by combining all identified prediction algorithms pertinent for a particular prediction problem. SL generates a final model that is at least as good as any of the other models considered for predicting the outcome. The overarching aim of this work is to introduce SL to analysts and practitioners...
2017: PloS One
https://www.readbyqxmd.com/read/28392994/medical-big-data-promise-and-challenges
#15
Choong Ho Lee, Hyung-Jin Yoon
The concept of big data, commonly characterized by volume, variety, velocity, and veracity, goes far beyond the data type and includes the aspects of data analysis, such as hypothesis-generating, rather than hypothesis-testing. Big data focuses on temporal stability of the association, rather than on causal relationship and underlying probability distribution assumptions are frequently not required. Medical big data as material to be analyzed has various features that are not only distinct from big data of other disciplines, but also distinct from traditional clinical epidemiology...
March 2017: Kidney Research and Clinical Practice
https://www.readbyqxmd.com/read/28392970/generalized-hierarchical-sparse-model-for-arbitrary-order-interactive-antigenic-sites-identification-in-flu-virus-data
#16
Lei Han, Yu Zhang, Xiu-Feng Wan, Tong Zhang
Recent statistical evidence has shown that a regression model by incorporating the interactions among the original covariates/features can significantly improve the interpretability for biological data. One major challenge is the exponentially expanded feature space when adding high-order feature interactions to the model. To tackle the huge dimensionality, hierarchical sparse models (HSM) are developed by enforcing sparsity under heredity structures in the interactions among the covariates. However, existing methods only consider pairwise interactions, making the discovery of important high-order interactions a non-trivial open problem...
August 2016: KDD: Proceedings
https://www.readbyqxmd.com/read/28392728/external-quality-assessment-beyond-the-analytical-phase-an-australian-perspective
#17
REVIEW
Tony Badrick, Stephanie Gay, Euan J McCaughey, Andrew Georgiou
External Quality Assessment (EQA) is the verification, on a recurring basis, that laboratory results conform to expectations for the quality required for patient care. It is now widely recognised that both the pre- and post-laboratory phase of testing, termed the diagnostic phases, are a significant source of laboratory errors. These errors have a direct impact on both the effectiveness of the laboratory and patient safety. Despite this, Australian laboratories tend to be focussed on very narrow concepts of EQA, primarily surrounding test accuracy, with little in the way of EQA programs for the diagnostic phases...
February 15, 2017: Biochemia Medica: časopis Hrvatskoga Društva Medicinskih Biokemičara
https://www.readbyqxmd.com/read/28386181/analytical-fuzzy-approach-to-biological-data-analysis
#18
Weiping Zhang, Jingzhi Yang, Yanling Fang, Huanyu Chen, Yihua Mao, Mohit Kumar
The assessment of the physiological state of an individual requires an objective evaluation of biological data while taking into account both measurement noise and uncertainties arising from individual factors. We suggest to represent multi-dimensional medical data by means of an optimal fuzzy membership function. A carefully designed data model is introduced in a completely deterministic framework where uncertain variables are characterized by fuzzy membership functions. The study derives the analytical expressions of fuzzy membership functions on variables of the multivariate data model by maximizing the over-uncertainties-averaged-log-membership values of data samples around an initial guess...
March 2017: Saudi Journal of Biological Sciences
https://www.readbyqxmd.com/read/28384584/river-doctors-learning-from-medicine-to-improve-ecosystem-management
#19
Arturo Elosegi, Mark O Gessner, Roger G Young
Effective ecosystem management requires a robust methodology to analyse, remedy and avoid ecosystem damage. Here we propose that the overall conceptual framework and approaches developed over millennia in medical science and practice to diagnose, cure and prevent disease can provide an excellent template. Key principles to adopt include combining well-established assessment methods with new analytical techniques and restricting both diagnosis and treatment to qualified personnel at various levels of specialization, in addition to striving for a better mechanistic understanding of ecosystem structure and functioning, as well as identifying the proximate and ultimate causes of ecosystem impairment...
April 3, 2017: Science of the Total Environment
https://www.readbyqxmd.com/read/28379439/calibration-drift-in-regression-and-machine-learning-models-for-acute-kidney-injury
#20
Sharon E Davis, Thomas A Lasko, Guanhua Chen, Edward D Siew, Michael E Matheny
Objective: Predictive analytics create opportunities to incorporate personalized risk estimates into clinical decision support. Models must be well calibrated to support decision-making, yet calibration deteriorates over time. This study explored the influence of modeling methods on performance drift and connected observed drift with data shifts in the patient population. Materials and Methods: Using 2003 admissions to Department of Veterans Affairs hospitals nationwide, we developed 7 parallel models for hospital-acquired acute kidney injury using common regression and machine learning methods, validating each over 9 subsequent years...
March 31, 2017: Journal of the American Medical Informatics Association: JAMIA
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