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https://www.readbyqxmd.com/read/29156506/lower-gastrointestinal-bleeding-in-patients-with-coronary-artery-disease-on-antithrombotics-and-subsequent-mortality-risk
#1
Parita Patel, Neha Nigam, Neil Sengupta
BACKGROUND: Lower gastrointestinal bleeding (LGIB) is a common complication for patients with coronary artery disease (CAD) due to use of antithrombotic medications. Limited data exists describing which patients are at increased risk for mortality. AIM: To 1) determine whether patients on dual antiplatelet (DAPT) therapy or triple therapy are at higher risk of 90-day and 6-month mortality compared to patients on aspirin (ASA) alone and 2) evaluate risk factors for mortality in patients with CAD on antithrombotics hospitalized with LGIB...
November 20, 2017: Journal of Gastroenterology and Hepatology
https://www.readbyqxmd.com/read/29134342/an-introduction-and-overview-of-machine-learning-in-neurosurgical-care
#2
REVIEW
Joeky T Senders, Mark M Zaki, Aditya V Karhade, Bliss Chang, William B Gormley, Marike L Broekman, Timothy R Smith, Omar Arnaout
BACKGROUND: Machine learning (ML) is a branch of artificial intelligence that allows computers to learn from large complex datasets without being explicitly programmed. Although ML is already widely manifest in our daily lives in various forms, the considerable potential of ML has yet to find its way into mainstream medical research and day-to-day clinical care. The complex diagnostic and therapeutic modalities used in neurosurgery provide a vast amount of data that is ideally suited for ML models...
November 13, 2017: Acta Neurochirurgica
https://www.readbyqxmd.com/read/29132626/early-hospital-mortality-prediction-of-intensive-care-unit-patients-using-an-ensemble-learning-approach
#3
Aya Awad, Mohamed Bader-El-Den, James McNicholas, Jim Briggs
BACKGROUND: Mortality prediction of hospitalized patients is an important problem. Over the past few decades, several severity scoring systems and machine learning mortality prediction models have been developed for predicting hospital mortality. By contrast, early mortality prediction for intensive care unit patients remains an open challenge. Most research has focused on severity of illness scoring systems or data mining (DM) models designed for risk estimation at least 24 or 48h after ICU admission...
December 2017: International Journal of Medical Informatics
https://www.readbyqxmd.com/read/29131760/deep-learning-a-primer-for-radiologists
#4
Gabriel Chartrand, Phillip M Cheng, Eugene Vorontsov, Michal Drozdzal, Simon Turcotte, Christopher J Pal, Samuel Kadoury, An Tang
Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapping from raw inputs to desired outputs (eg, image classes). Unlike traditional machine learning methods, which require hand-engineered feature extraction from inputs, deep learning methods learn these features directly from data. With the advent of large datasets and increased computing power, these methods can produce models with exceptional performance...
November 2017: Radiographics: a Review Publication of the Radiological Society of North America, Inc
https://www.readbyqxmd.com/read/29127902/a-survey-of-machine-learning-applications-in-hiv-clinical-research-and-care
#5
REVIEW
Kuteesa R Bisaso, Godwin T Anguzu, Susan A Karungi, Agnes Kiragga, Barbara Castelnuovo
A wealth of genetic, demographic, clinical and biomarker data is collected from routine clinical care of HIV patients and exists in the form of medical records available among the medical care and research communities. Machine learning (ML) methods have the ability to identify and discover patterns in complex datasets and predict future outcomes of HIV treatment. We survey published studies that make use of ML techniques in HIV clinical research and care. An advanced search relevant to the use of ML in HIV research was conducted in the PubMed biomedical database...
November 8, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/29127485/oct-based-deep-learning-algorithm-for-the-evaluation-of-treatment-indication-with-anti-vascular-endothelial-growth-factor-medications
#6
Philipp Prahs, Viola Radeck, Christian Mayer, Yordan Cvetkov, Nadezhda Cvetkova, Horst Helbig, David Märker
PURPOSE: Intravitreal injections with anti-vascular endothelial growth factor (anti-VEGF) medications have become the standard of care for their respective indications. Optical coherence tomography (OCT) scans of the central retina provide detailed anatomical data and are widely used by clinicians in the decision-making process of anti-VEGF indication. In recent years, significant progress has been made in artificial intelligence and computer vision research. We trained a deep convolutional artificial neural network to predict treatment indication based on central retinal OCT scans without human intervention...
November 10, 2017: Graefe's Archive for Clinical and Experimental Ophthalmology
https://www.readbyqxmd.com/read/29126825/artificial-intelligence-in-medical-practice-the-question-to-the-answer
#7
REVIEW
D Douglas Miller, Eric W Brown
Computer science advances and ultra-fast computing speeds find artificial intelligence (AI) broadly benefitting modern society - forecasting weather, recognizing faces, detecting fraud, and deciphering genomics. AI's future role in medical practice remains an unanswered question. Machines (computers) learn to detect patterns not decipherable using biostatistics by processing massive datasets (big data) through layered mathematical models (algorithms). Correcting algorithm mistakes (training) adds to AI predictive model confidence...
November 7, 2017: American Journal of Medicine
https://www.readbyqxmd.com/read/29114182/application-of-deep-learning-in-automated-analysis-of-molecular-images-in-cancer-a-survey
#8
REVIEW
Yong Xue, Shihui Chen, Jing Qin, Yong Liu, Bingsheng Huang, Hanwei Chen
Molecular imaging enables the visualization and quantitative analysis of the alterations of biological procedures at molecular and/or cellular level, which is of great significance for early detection of cancer. In recent years, deep leaning has been widely used in medical imaging analysis, as it overcomes the limitations of visual assessment and traditional machine learning techniques by extracting hierarchical features with powerful representation capability. Research on cancer molecular images using deep learning techniques is also increasing dynamically...
2017: Contrast Media & Molecular Imaging
https://www.readbyqxmd.com/read/29109070/artificial-intelligence-learning-semantics-via-external-resources-for-classifying-diagnosis-codes-in-discharge-notes
#9
Chin Lin, Chia-Jung Hsu, Yu-Sheng Lou, Shih-Jen Yeh, Chia-Cheng Lee, Sui-Lung Su, Hsiang-Cheng Chen
BACKGROUND: Automated disease code classification using free-text medical information is important for public health surveillance. However, traditional natural language processing (NLP) pipelines are limited, so we propose a method combining word embedding with a convolutional neural network (CNN). OBJECTIVE: Our objective was to compare the performance of traditional pipelines (NLP plus supervised machine learning models) with that of word embedding combined with a CNN in conducting a classification task identifying International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis codes in discharge notes...
November 6, 2017: Journal of Medical Internet Research
https://www.readbyqxmd.com/read/29105694/-handle-with-care-about-the-potential-unintended-consequences-of-oracular-artificial-intelligence-systems-in-medicine
#10
Federico Cabitza, Camilla Alderighi, Raffaele Rasoini, Gian Franco Gensini
Decisional support systems based on machine learning (ML) in medicine are gaining a growing interest as some recent articles have highlighted the high diagnostic accuracy exhibited by these systems in specific medical contexts. However, it is implausible that any potential advantage can be obtained without some potential drawbacks. In light of the current gaps in medical research about the side effects of the application of these new AI systems in medical practice, in this article we summarize the main unexpected consequences that may result from the widespread application of "oracular" systems, that is highly accurate systems that cannot give reasonable explanations of their advice as those endowed with predictive models developed with ML techniques usually are...
October 2017: Recenti Progressi in Medicina
https://www.readbyqxmd.com/read/29103378/estimating-local-costs-associated-with-clostridium-difficile-infection-using-machine-learning-and-electronic-medical-records
#11
Theodore R Pak, Kieran I Chacko, Timothy O'Donnell, Shirish S Huprikar, Harm van Bakel, Andrew Kasarskis, Erick R Scott
BACKGROUND Reported per-patient costs of Clostridium difficile infection (CDI) vary by 2 orders of magnitude among different hospitals, implying that infection control officers need precise, local analyses to guide rational decision making between interventions. OBJECTIVE We sought to comprehensively estimate changes in length of stay (LOS) attributable to CDI at a single urban tertiary-care facility using only data automatically extractable from the electronic medical record (EMR). METHODS We performed a retrospective cohort study of 171,938 visits spanning a 7-year period...
November 6, 2017: Infection Control and Hospital Epidemiology
https://www.readbyqxmd.com/read/29102036/coronary-computed-tomographic-angiography-derived-fractional-flow-reserve-for-therapeutic-decision-making
#12
Christian Tesche, Rozemarijn Vliegenthart, Taylor M Duguay, Carlo N De Cecco, Moritz H Albrecht, Domenico De Santis, Marcel C Langenbach, Akos Varga-Szemes, Brian E Jacobs, David Jochheim, Moritz Baquet, Richard R Bayer, Sheldon E Litwin, Ellen Hoffmann, Daniel H Steinberg, U Joseph Schoepf
This study investigated the performance of coronary computed tomography angiography (cCTA) with cCTA-derived fractional flow reserve (CT-FFR) compared with invasive coronary angiography (ICA) with fractional flow reserve (FFR) for therapeutic decision making in patients with suspected coronary artery disease (CAD). Seventy-four patients (62 ± 11 years, 62% men) with at least 1 coronary stenosis of ≥50% on clinically indicated dual-source cCTA, who had subsequently undergone ICA with FFR measurement, were retrospectively evaluated...
September 19, 2017: American Journal of Cardiology
https://www.readbyqxmd.com/read/29095704/medical-education-must-move-from-the-information-age-to-the-age-of-artificial-intelligence
#13
Steven A Wartman, C Donald Combs
Changes to the medical profession require medical education reforms that will enable physicians to more effectively enter contemporary practice. Proposals for such reforms abound. Common themes include renewed emphasis on communication, teamwork, risk-management, and patient safety. These reforms are important but insufficient. They do not adequately address the most fundamental change--the practice of medicine is rapidly transitioning from the information age to the age of artificial intelligence. Employers need physicians who: work at the top of their license, have knowledge spanning the health professions and care continuum, effectively leverage data platforms, focus on analyzing outcomes and improving performance, and communicate the meaning of the probabilities generated by massive amounts of data to patients given their unique human complexities...
November 1, 2017: Academic Medicine: Journal of the Association of American Medical Colleges
https://www.readbyqxmd.com/read/29092082/using-machine-learning-for-sequence-level-automated-mri-protocol-selection-in-neuroradiology
#14
Andrew D Brown, Thomas R Marotta
Incorrect imaging protocol selection can lead to important clinical findings being missed, contributing to both wasted health care resources and patient harm. We present a machine learning method for analyzing the unstructured text of clinical indications and patient demographics from magnetic resonance imaging (MRI) orders to automatically protocol MRI procedures at the sequence level. We compared 3 machine learning models - support vector machine, gradient boosting machine, and random forest - to a baseline model that predicted the most common protocol for all observations in our test set...
October 27, 2017: Journal of the American Medical Informatics Association: JAMIA
https://www.readbyqxmd.com/read/29079959/integrating-natural-language-processing-and-machine-learning-algorithms-to-categorize-oncologic-response-in-radiology-reports
#15
Po-Hao Chen, Hanna Zafar, Maya Galperin-Aizenberg, Tessa Cook
A significant volume of medical data remains unstructured. Natural language processing (NLP) and machine learning (ML) techniques have shown to successfully extract insights from radiology reports. However, the codependent effects of NLP and ML in this context have not been well-studied. Between April 1, 2015 and November 1, 2016, 9418 cross-sectional abdomen/pelvis CT and MR examinations containing our internal structured reporting element for cancer were separated into four categories: Progression, Stable Disease, Improvement, or No Cancer...
October 27, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29076332/neural-network-based-coronary-heart-disease-risk-prediction-using-feature-correlation-analysis
#16
Jae Kwon Kim, Sanggil Kang
Background: Of the machine learning techniques used in predicting coronary heart disease (CHD), neural network (NN) is popularly used to improve performance accuracy. Objective: Even though NN-based systems provide meaningful results based on clinical experiments, medical experts are not satisfied with their predictive performances because NN is trained in a “black-box” style. Method: We sought to devise an NN-based prediction of CHD risk using feature correlation analysis (NN-FCA) using two stages...
2017: Journal of Healthcare Engineering
https://www.readbyqxmd.com/read/29075939/web-enabled-distributed-health-care-framework-for-automated-malaria-parasite-classification-an-e-health-approach
#17
Maitreya Maity, Dhiraj Dhane, Tushar Mungle, A K Maiti, Chandan Chakraborty
Web-enabled e-healthcare system or computer assisted disease diagnosis has a potential to improve the quality and service of conventional healthcare delivery approach. The article describes the design and development of a web-based distributed healthcare management system for medical information and quantitative evaluation of microscopic images using machine learning approach for malaria. In the proposed study, all the health-care centres are connected in a distributed computer network. Each peripheral centre manages its' own health-care service independently and communicates with the central server for remote assistance...
October 26, 2017: Journal of Medical Systems
https://www.readbyqxmd.com/read/29073909/genotype-driven-identification-of-a-molecular-network-predictive-of-advanced-coronary-calcium-in-clinseq%C3%A2-and-framingham-heart-study-cohorts
#18
Cihan Oguz, Shurjo K Sen, Adam R Davis, Yi-Ping Fu, Christopher J O'Donnell, Gary H Gibbons
BACKGROUND: One goal of personalized medicine is leveraging the emerging tools of data science to guide medical decision-making. Achieving this using disparate data sources is most daunting for polygenic traits. To this end, we employed random forests (RFs) and neural networks (NNs) for predictive modeling of coronary artery calcium (CAC), which is an intermediate endo-phenotype of coronary artery disease (CAD). METHODS: Model inputs were derived from advanced cases in the ClinSeq®; discovery cohort (n=16) and the FHS replication cohort (n=36) from 89 (th) -99 (th) CAC score percentile range, and age-matched controls (ClinSeq®; n=16, FHS n=36) with no detectable CAC (all subjects were Caucasian males)...
October 26, 2017: BMC Systems Biology
https://www.readbyqxmd.com/read/29073279/open-source-machine-learning-algorithms-for-the-prediction-of-optimal-cancer-drug-therapies
#19
Cai Huang, Roman Mezencev, John F McDonald, Fredrik Vannberg
Precision medicine is a rapidly growing area of modern medical science and open source machine-learning codes promise to be a critical component for the successful development of standardized and automated analysis of patient data. One important goal of precision cancer medicine is the accurate prediction of optimal drug therapies from the genomic profiles of individual patient tumors. We introduce here an open source software platform that employs a highly versatile support vector machine (SVM) algorithm combined with a standard recursive feature elimination (RFE) approach to predict personalized drug responses from gene expression profiles...
2017: PloS One
https://www.readbyqxmd.com/read/29065583/neural-network-based-coronary-heart-disease-risk-prediction-using-feature-correlation-analysis
#20
Jae Kwon Kim, Sanggil Kang
BACKGROUND: Of the machine learning techniques used in predicting coronary heart disease (CHD), neural network (NN) is popularly used to improve performance accuracy. OBJECTIVE: Even though NN-based systems provide meaningful results based on clinical experiments, medical experts are not satisfied with their predictive performances because NN is trained in a "black-box" style. METHOD: We sought to devise an NN-based prediction of CHD risk using feature correlation analysis (NN-FCA) using two stages...
2017: Journal of Healthcare Engineering
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