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https://www.readbyqxmd.com/read/27917508/estimating-personalized-diagnostic-rules-depending-on-individualized-characteristics
#1
Ying Liu, Yuanjia Wang, Chaorui Huang, Donglin Zeng
There is an increasing demand for personalization of disease screening based on assessment of patient risk and other characteristics. For example, in breast cancer screening, advanced imaging technologies have made it possible to move away from 'one-size-fits-all' screening guidelines to targeted risk-based screening for those who are in need. Because diagnostic performance of various imaging modalities may vary across subjects, applying the most accurate modality to the patients who would benefit the most requires personalized strategy...
December 4, 2016: Statistics in Medicine
https://www.readbyqxmd.com/read/27916234/phantom-motor-execution-facilitated-by-machine-learning-and-augmented-reality-as-treatment-for-phantom-limb-pain-a-single-group-clinical-trial-in-patients-with-chronic-intractable-phantom-limb-pain
#2
Max Ortiz-Catalan, Rannveig A Guðmundsdóttir, Morten B Kristoffersen, Alejandra Zepeda-Echavarria, Kerstin Caine-Winterberger, Katarzyna Kulbacka-Ortiz, Cathrine Widehammar, Karin Eriksson, Anita Stockselius, Christina Ragnö, Zdenka Pihlar, Helena Burger, Liselotte Hermansson
BACKGROUND: Phantom limb pain is a debilitating condition for which no effective treatment has been found. We hypothesised that re-engagement of central and peripheral circuitry involved in motor execution could reduce phantom limb pain via competitive plasticity and reversal of cortical reorganisation. METHODS: Patients with upper limb amputation and known chronic intractable phantom limb pain were recruited at three clinics in Sweden and one in Slovenia. Patients received 12 sessions of phantom motor execution using machine learning, augmented and virtual reality, and serious gaming...
December 1, 2016: Lancet
https://www.readbyqxmd.com/read/27915117/from-intentions-to-actions-neural-oscillations-encode-motor-processes-through-phase-amplitude-and-phase-amplitude-coupling
#3
Etienne Combrisson, Marcela Perrone-Bertolotti, Juan Lp Soto, Golnoush Alamian, Philippe Kahane, Jean-Philippe Lachaux, Aymeric Guillot, Karim Jerbi
Goal-directed motor behavior is associated with changes in patterns of rhythmic neuronal activity across widely distributed brain areas. In particular, movement initiation and execution are mediated by patterns of synchronization and desynchronization that occur concurrently across distinct frequency bands and across multiple motor cortical areas. To date, motor-related local oscillatory modulations have been predominantly examined by quantifying increases or suppressions in spectral power. However, beyond signal power, spectral properties such as phase and phase-amplitude coupling (PAC) have also been shown to carry information with regards to the oscillatory dynamics underlying motor processes...
November 30, 2016: NeuroImage
https://www.readbyqxmd.com/read/27911325/autonomic-nervous-system-dysfunctions-as-a-basis-for-a-predictive-model-of-risk-of%C3%A2-neurological-disorders-in-subjects-with%C3%A2-prior-history-of-traumatic-brain-injury-implications-in-alzheimer-s-disease
#4
Lap Ho, Marc Legere, Tongbin Li, Samara Levine, Ke Hao, Breanna Valcarcel, Giulio M Pasinetti
Autonomic dysfunction is very common in patients with dementia, and its presence might also help in differential diagnosis among dementia subtypes. Various central nervous system structures affected in Alzheimer's disease (AD) are also implicated in the central autonomic nervous system (ANS) regulation. For example, deficits in central cholinergic function in AD could likely lead to autonomic dysfunction. We recently developed a simple, readily applicable evaluation for monitoring ANS disturbances in response to traumatic brain injury (TBI)...
December 1, 2016: Journal of Alzheimer's Disease: JAD
https://www.readbyqxmd.com/read/27905893/computational-prediction-of-multidisciplinary-team-decision-making-for-adjuvant-breast-cancer-drug-therapies-a-machine-learning-approach
#5
Frank P Y Lin, Adrian Pokorny, Christina Teng, Rachel Dear, Richard J Epstein
BACKGROUND: Multidisciplinary team (MDT) meetings are used to optimise expert decision-making about treatment options, but such expertise is not digitally transferable between centres. To help standardise medical decision-making, we developed a machine learning model designed to predict MDT decisions about adjuvant breast cancer treatments. METHODS: We analysed MDT decisions regarding adjuvant systemic therapy for 1065 breast cancer cases over eight years. Machine learning classifiers with and without bootstrap aggregation were correlated with MDT decisions (recommended, not recommended, or discussable) regarding adjuvant cytotoxic, endocrine and biologic/targeted therapies, then tested for predictability using stratified ten-fold cross-validations...
December 1, 2016: BMC Cancer
https://www.readbyqxmd.com/read/27903489/finding-important-terms-for-patients-in-their-electronic-health-records-a-learning-to-rank-approach-using-expert-annotations
#6
Jinying Chen, Jiaping Zheng, Hong Yu
BACKGROUND: Many health organizations allow patients to access their own electronic health record (EHR) notes through online patient portals as a way to enhance patient-centered care. However, EHR notes are typically long and contain abundant medical jargon that can be difficult for patients to understand. In addition, many medical terms in patients' notes are not directly related to their health care needs. One way to help patients better comprehend their own notes is to reduce information overload and help them focus on medical terms that matter most to them...
November 30, 2016: JMIR Medical Informatics
https://www.readbyqxmd.com/read/27901055/mediboost-a-patient-stratification-tool-for-interpretable-decision-making-in-the-era-of-precision-medicine
#7
Gilmer Valdes, José Marcio Luna, Eric Eaton, Charles B Simone, Lyle H Ungar, Timothy D Solberg
Machine learning algorithms that are both interpretable and accurate are essential in applications such as medicine where errors can have a dire consequence. Unfortunately, there is currently a tradeoff between accuracy and interpretability among state-of-the-art methods. Decision trees are interpretable and are therefore used extensively throughout medicine for stratifying patients. Current decision tree algorithms, however, are consistently outperformed in accuracy by other, less-interpretable machine learning models, such as ensemble methods...
November 30, 2016: Scientific Reports
https://www.readbyqxmd.com/read/27898976/development-and-validation-of-a-deep-learning-algorithm-for-detection-of-diabetic-retinopathy-in-retinal-fundus-photographs
#8
Varun Gulshan, Lily Peng, Marc Coram, Martin C Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros, Ramasamy Kim, Rajiv Raman, Philip C Nelson, Jessica L Mega, Dale R Webster
Importance: Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation. Objective: To apply deep learning to create an algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs...
November 29, 2016: JAMA: the Journal of the American Medical Association
https://www.readbyqxmd.com/read/27896982/predictive-modeling-of-hospital-readmission-rates-using-electronic-medical-record-wide-machine-learning-a-case-study-using-mount-sinai-heart-failure-cohort
#9
Khader Shameer, Kipp W Johnson, Alexandre Yahi, Riccardo Miotto, L I Li, Doran Ricks, Jebakumar Jebakaran, Patricia Kovatch, Partho P Sengupta, Sengupta Gelijns, Alan Moskovitz, Bruce Darrow, David L David, Andrew Kasarskis, Nicholas P Tatonetti, Sean Pinney, Joel T Dudley
Reduction of preventable hospital readmissions that result from chronic or acute conditions like stroke, heart failure, myocardial infarction and pneumonia remains a significant challenge for improving the outcomes and decreasing the cost of healthcare delivery in the United States. Patient readmission rates are relatively high for conditions like heart failure (HF) despite the implementation of high-quality healthcare delivery operation guidelines created by regulatory authorities. Multiple predictive models are currently available to evaluate potential 30-day readmission rates of patients...
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27888170/a-predictive-model-for-medical-events-based-on-contextual-embedding-of-temporal-sequences
#10
Wael Farhan, Zhimu Wang, Yingxiang Huang, Shuang Wang, Fei Wang, Xiaoqian Jiang
BACKGROUND: Medical concepts are inherently ambiguous and error-prone due to human fallibility, which makes it hard for them to be fully used by classical machine learning methods (eg, for tasks like early stage disease prediction). OBJECTIVE: Our work was to create a new machine-friendly representation that resembles the semantics of medical concepts. We then developed a sequential predictive model for medical events based on this new representation. METHODS: We developed novel contextual embedding techniques to combine different medical events (eg, diagnoses, prescriptions, and labs tests)...
November 25, 2016: JMIR Medical Informatics
https://www.readbyqxmd.com/read/27885364/bidirectional-rnn-for-medical-event-detection-in-electronic-health-records
#11
Abhyuday N Jagannatha, Hong Yu
Sequence labeling for extraction of medical events and their attributes from unstructured text in Electronic Health Record (EHR) notes is a key step towards semantic understanding of EHRs. It has important applications in health informatics including pharmacovigilance and drug surveillance. The state of the art supervised machine learning models in this domain are based on Conditional Random Fields (CRFs) with features calculated from fixed context windows. In this application, we explored recurrent neural network frameworks and show that they significantly out-performed the CRF models...
June 2016: Proceedings of the Conference
https://www.readbyqxmd.com/read/27876690/-mommy-blogs-and-the-vaccination-exemption-narrative-results-from-a-machine-learning-approach-for-story-aggregation-on-parenting-social-media-sites
#12
Timothy R Tangherlini, Vwani Roychowdhury, Beth Glenn, Catherine M Crespi, Roja Bandari, Akshay Wadia, Misagh Falahi, Ehsan Ebrahimzadeh, Roshan Bastani
BACKGROUND: Social media offer an unprecedented opportunity to explore how people talk about health care at a very large scale. Numerous studies have shown the importance of websites with user forums for people seeking information related to health. Parents turn to some of these sites, colloquially referred to as "mommy blogs," to share concerns about children's health care, including vaccination. Although substantial work has considered the role of social media, particularly Twitter, in discussions of vaccination and other health care-related issues, there has been little work on describing the underlying structure of these discussions and the role of persuasive storytelling, particularly on sites with no limits on post length...
November 22, 2016: JMIR Public Health and Surveillance
https://www.readbyqxmd.com/read/27873411/training-physicians-for-the-real-world-of-medicine-administration-based-learning
#13
Jason Rosenstock, Garrett M Sparks
Tired of outdated teaching formats like case-based learning (CBL), problem-based learning (PBL) and team-based learning (TBL)? We wanted something fresh for our medical school, something that would prepare our graduates for the modern practice of medicine, something that would satisfy regulatory agencies and our deans. After doing an extensive needs assessment, which we ignored, we decided to replace basic science in our curriculum with something more practical: administration-based learning (ABL). We taught students how to fix fax machines, how to deal with angry team members, and how to maximise revenue in private practice - lessons that were well received and were more consistent with what physicians really need to learn to be effective practitioners...
December 2016: Medical Education
https://www.readbyqxmd.com/read/27849742/62-machine-learning-can-pre-identify-instability-risk-for-a-medical-emergency-team-call
#14
Marilyn Hravnak, Lujie Chen, Artur Dubrawski, Gilles Clermont, Michael Pinsky
No abstract text is available yet for this article.
December 2016: Critical Care Medicine
https://www.readbyqxmd.com/read/27848006/clinical-fracture-risk-evaluated-by-hierarchical-agglomerative-clustering
#15
C Kruse, P Eiken, P Vestergaard
: Clustering analysis can identify subgroups of patients based on similarities of traits. From data on 10,775 subjects, we document nine patient clusters of different fracture risks. Differences emerged after age 60 and treatment compliance differed by hip and lumbar spine bone mineral density profiles. INTRODUCTION: The purposes of this study were to establish and quantify patient clusters of high, average and low fracture risk using an unsupervised machine learning algorithm...
November 16, 2016: Osteoporosis International
https://www.readbyqxmd.com/read/27833115/transcriptome-assists-prognosis-of-disease-severity-in-respiratory-syncytial-virus-infected-infants
#16
Victor L Jong, Inge M L Ahout, Henk-Jan van den Ham, Jop Jans, Fatiha Zaaraoui-Boutahar, Aldert Zomer, Elles Simonetti, Maarten A Bijl, H Kim Brand, Wilfred F J van IJcken, Marien I de Jonge, Pieter L Fraaij, Ronald de Groot, Albert D M E Osterhaus, Marinus J Eijkemans, Gerben Ferwerda, Arno C Andeweg
Respiratory syncytial virus (RSV) causes infections that range from common cold to severe lower respiratory tract infection requiring high-level medical care. Prediction of the course of disease in individual patients remains challenging at the first visit to the pediatric wards and RSV infections may rapidly progress to severe disease. In this study we investigate whether there exists a genomic signature that can accurately predict the course of RSV. We used early blood microarray transcriptome profiles from 39 hospitalized infants that were followed until recovery and of which the level of disease severity was determined retrospectively...
November 11, 2016: Scientific Reports
https://www.readbyqxmd.com/read/27826755/using-machine-learning-to-parse-breast-pathology-reports
#17
Adam Yala, Regina Barzilay, Laura Salama, Molly Griffin, Grace Sollender, Aditya Bardia, Constance Lehman, Julliette M Buckley, Suzanne B Coopey, Fernanda Polubriaginof, Judy E Garber, Barbara L Smith, Michele A Gadd, Michelle C Specht, Thomas M Gudewicz, Anthony J Guidi, Alphonse Taghian, Kevin S Hughes
PURPOSE: Extracting information from electronic medical record is a time-consuming and expensive process when done manually. Rule-based and machine learning techniques are two approaches to solving this problem. In this study, we trained a machine learning model on pathology reports to extract pertinent tumor characteristics, which enabled us to create a large database of attribute searchable pathology reports. This database can be used to identify cohorts of patients with characteristics of interest...
November 8, 2016: Breast Cancer Research and Treatment
https://www.readbyqxmd.com/read/27813310/identifying-axial-spondyloarthritis-in-electronic-medical-records-of-united-states-veterans
#18
Jessica A Walsh, Yijun Shao, Jianwei Leng, Tao He, Chia-Chen Teng, Doug Redd, Qing Treitler Zeng, Zachary Burningham, Daniel O Clegg, Brian C Sauer
OBJECTIVE: Large database research in axial spondyloarthritis (AxSpA) is limited by a lack of methods for identifying most types of AxSpA. Our objective was to develop methods for identifying AxSpA concepts in the free text of documents from electronic medical records. METHODS: Veterans with documents in the national Veteran Health Administration Corporate Data Warehouse between January 1, 2005 and June 30, 2015 were included. Methods were developed for exploring, selecting, and extracting meaningful terms that were likely to represent AxSpA concepts...
November 3, 2016: Arthritis Care & Research
https://www.readbyqxmd.com/read/27813129/a-machine-learning-approach-to-identifying-the-thought-markers-of-suicidal-subjects-a-prospective-multicenter-trial
#19
John P Pestian, Michael Sorter, Brian Connolly, Kevin Bretonnel Cohen, Cheryl McCullumsmith, Jeffry T Gee, Louis-Philippe Morency, Stefan Scherer, Lesley Rohlfs
Death by suicide demonstrates profound personal suffering and societal failure. While basic sciences provide the opportunity to understand biological markers related to suicide, computer science provides opportunities to understand suicide thought markers. In this novel prospective, multimodal, multicenter, mixed demographic study, we used machine learning to measure and fuse two classes of suicidal thought markers: verbal and nonverbal. Machine learning algorithms were used with the subjects' words and vocal characteristics to classify 379 subjects recruited from two academic medical centers and a rural community hospital into one of three groups: suicidal, mentally ill but not suicidal, or controls...
November 3, 2016: Suicide & Life-threatening Behavior
https://www.readbyqxmd.com/read/27798643/predictors-of-post-operative-mycetoma-recurrence-using-machine-learning-algorithms-the-mycetoma-research-center-experience
#20
Ali Wadal, Tusneem Ahmed Elhassan, Hajer Ahmed Zein, Manar Elsheikh Abdel-Rahman, Ahmed Hassan Fahal
Post-operative recurrence in mycetoma after adequate medical and surgical treatment is common and a serious problem. It has health, socio-economic and psychological detrimental effects on patients and families. It is with this in mind, we set out to determine the predictors of post-operative recurrence in mycetoma. The study included 1013 patients with Madurella mycetomatis causing eumycetoma who underwent surgical excision at the Mycetoma Research Centre, Khartoum, Sudan in the period 1991-2015. The clinical records of these patients were reviewed and relevant information was collected using a pre-designed data collection sheet...
October 2016: PLoS Neglected Tropical Diseases
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