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"Natural Language Processing"

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https://www.readbyqxmd.com/read/29135365/deep-learning-to-classify-radiology-free-text-reports
#1
Matthew C Chen, Robyn L Ball, Lingyao Yang, Nathaniel Moradzadeh, Brian E Chapman, David B Larson, Curtis P Langlotz, Timothy J Amrhein, Matthew P Lungren
Purpose To evaluate the performance of a deep learning convolutional neural network (CNN) model compared with a traditional natural language processing (NLP) model in extracting pulmonary embolism (PE) findings from thoracic computed tomography (CT) reports from two institutions. Materials and Methods Contrast material-enhanced CT examinations of the chest performed between January 1, 1998, and January 1, 2016, were selected. Annotations by two human radiologists were made for three categories: the presence, chronicity, and location of PE...
November 13, 2017: Radiology
https://www.readbyqxmd.com/read/29131760/deep-learning-a-primer-for-radiologists
#2
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/29126825/artificial-intelligence-in-medical-practice-the-question-to-the-answer
#3
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/29121053/adept-a-semantically-enriched-pipeline-for-extracting-adverse-drug-events-from-free-text-electronic-health-records
#4
Ehtesham Iqbal, Robbie Mallah, Daniel Rhodes, Honghan Wu, Alvin Romero, Nynn Chang, Olubanke Dzahini, Chandra Pandey, Matthew Broadbent, Robert Stewart, Richard J B Dobson, Zina M Ibrahim
Adverse drug events (ADEs) are unintended responses to medical treatment. They can greatly affect a patient's quality of life and present a substantial burden on healthcare. Although Electronic health records (EHRs) document a wealth of information relating to ADEs, they are frequently stored in the unstructured or semi-structured free-text narrative requiring Natural Language Processing (NLP) techniques to mine the relevant information. Here we present a rule-based ADE detection and classification pipeline built and tested on a large Psychiatric corpus comprising 264k patients using the de-identified EHRs of four UK-based psychiatric hospitals...
2017: PloS One
https://www.readbyqxmd.com/read/29112003/measuring-processes-of-care-in-palliative-surgery-a-novel-approach-using-natural-language-processing
#5
Elizabeth J Lilley, Charlotta Lindvall, Keith D Lillemoe, James A Tulsky, Daniel C Wiener, Zara Cooper
: Palliative surgical procedures are often performed for patients with limited survival. Quality measures for processes of care at the end of life are appropriate in palliative surgery, but have not been applied in this patient population. In this paper, the authors propose 4 quality measures for end-of-life care in a palliative surgery, and then demonstrate the utility of natural language processing for implementing these measures.
November 3, 2017: Annals of Surgery
https://www.readbyqxmd.com/read/29109070/artificial-intelligence-learning-semantics-via-external-resources-for-classifying-diagnosis-codes-in-discharge-notes
#6
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/29104964/a-machine-learning-algorithm-for-identifying-atopic-dermatitis-in-adults-from-electronic-health-records
#7
Erin Gustafson, Jennifer Pacheco, Firas Wehbe, Jonathan Silverberg, William Thompson
The current work aims to identify patients with atopic dermatitis for inclusion in genome-wide association studies (GWAS). Here we describe a machine learning-based phenotype algorithm. Using the electronic health record (EHR), we combined coded information with information extracted from encounter notes as features in a lasso logistic regression. Our algorithm achieves high positive predictive value (PPV) and sensitivity, improving on previous algorithms with low sensitivity. These results demonstrate the utility of natural language processing (NLP) and machine learning for EHR-based phenotyping...
August 2017: IEEE International Conference on Healthcare Informatics IEEE International Conference on Healthcare Informatics
https://www.readbyqxmd.com/read/29096203/modular-representation-of-layered-neural-networks
#8
Chihiro Watanabe, Kaoru Hiramatsu, Kunio Kashino
Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge from the inference provided by a layered neural network, since its internal representation has many nonlinear and complex parameters embedded in hierarchical layers. Therefore, it becomes important to establish a new methodology by which layered neural networks can be understood...
October 12, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29094145/population-based-analysis-of-histologically-confirmed-melanocytic-proliferations-using-natural-language-processing
#9
Jason P Lott, Denise M Boudreau, Ray L Barnhill, Martin A Weinstock, Eleanor Knopp, Michael W Piepkorn, David E Elder, Steven R Knezevich, Andrew Baer, Anna N A Tosteson, Joann G Elmore
Importance: Population-based information on the distribution of histologic diagnoses associated with skin biopsies is unknown. Electronic medical records (EMRs) enable automated extraction of pathology report data to improve our epidemiologic understanding of skin biopsy outcomes, specifically those of melanocytic origin. Objective: To determine population-based frequencies and distribution of histologically confirmed melanocytic lesions. Design, Setting, and Participants: A natural language processing (NLP)-based analysis of EMR pathology reports of adult patients who underwent skin biopsies at a large integrated health care delivery system in the US Pacific Northwest from January 1, 2007, through December 31, 2012...
November 1, 2017: JAMA Dermatology
https://www.readbyqxmd.com/read/29092954/deepphe-a-natural-language-processing-system-for-extracting-cancer-phenotypes-from-clinical-records
#10
Guergana K Savova, Eugene Tseytlin, Sean Finan, Melissa Castine, Timothy Miller, Olga Medvedeva, David Harris, Harry Hochheiser, Chen Lin, Girish Chavan, Rebecca S Jacobson
Precise phenotype information is needed to understand the effects of genetic and epigenetic changes on tumor behavior and responsiveness. Extraction and representation of cancer phenotypes is currently mostly performed manually, making it difficult to correlate phenotypic data to genomic data. In addition, genomic data are being produced at an increasingly faster pace, exacerbating the problem. The DeepPhe software enables automated extraction of detailed phenotype information from electronic medical records of cancer patients...
November 1, 2017: Cancer Research
https://www.readbyqxmd.com/read/29089288/ranking-medical-terms-to-support-expansion-of-lay-language-resources-for-patient-comprehension-of-electronic-health-record-notes-adapted-distant-supervision-approach
#11
Jinying Chen, Abhyuday N Jagannatha, Samah J Fodeh, Hong Yu
BACKGROUND: Medical terms are a major obstacle for patients to comprehend their electronic health record (EHR) notes. Clinical natural language processing (NLP) systems that link EHR terms to lay terms or definitions allow patients to easily access helpful information when reading through their EHR notes, and have shown to improve patient EHR comprehension. However, high-quality lay language resources for EHR terms are very limited in the public domain. Because expanding and curating such a resource is a costly process, it is beneficial and even necessary to identify terms important for patient EHR comprehension first...
October 31, 2017: JMIR Medical Informatics
https://www.readbyqxmd.com/read/29084368/electronic-health-record-phenotypes-for-precision-medicine-perspectives-and-caveats-from-treatment-of-breast-cancer-at-a-single-institution
#12
Matthew K Breitenstein, Hongfang Liu, Kara N Maxwell, Jyotishman Pathak, Rui Zhang
Precision medicine is at the forefront of biomedical research. Cancer registries provide rich perspectives and electronic health record(EHR)s are commonly utilized to gather additional clinical data elements needed for translational research. However, manual annotation is resource-intense and not readily scalable. Informatics-based phenotyping presents an ideal solution, but perspectives obtained can be impacted by both data source and algorithm selection. We derived breast cancer(BC) receptor status phenotypes from structured and unstructured EHR data using rule-based algorithms, including natural language processing(NLP)...
October 30, 2017: Clinical and Translational Science
https://www.readbyqxmd.com/read/29084046/using-clinical-notes-and-natural-language-processing-for-automated-hiv-risk-assessment
#13
Daniel J Feller, Jason Zucker, Michael T Yin, Peter Gordon, Noémie Elhadad
OBJECTIVE: Universal HIV screening programs are costly, labor-intensive, and often fail to identify high-risk individuals. Automated risk assessment methods that leverage longitudinal electronic health records (EHRs) could catalyze targeted screening programs. While social and behavioral determinants of health are typically captured in narrative documentation, previous analyses have considered only structured EHR fields. We examined whether natural language processing (NLP) would improve predictive models of HIV diagnosis...
October 27, 2017: Journal of Acquired Immune Deficiency Syndromes: JAIDS
https://www.readbyqxmd.com/read/29079959/integrating-natural-language-processing-and-machine-learning-algorithms-to-categorize-oncologic-response-in-radiology-reports
#14
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/29070036/comparing-clinician-descriptions-of-frailty-and-geriatric-syndromes-using-electronic-health-records-a-retrospective-cohort-study
#15
Laura J Anzaldi, Ashwini Davison, Cynthia M Boyd, Bruce Leff, Hadi Kharrazi
BACKGROUND: Geriatric syndromes, including frailty, are common in older adults and associated with adverse outcomes. We compared patients described in clinical notes as "frail" to other older adults with respect to geriatric syndrome burden and healthcare utilization. METHODS: We conducted a retrospective cohort study on 18,341 Medicare Advantage enrollees aged 65+ (members of a large nonprofit medical group in Massachusetts), analyzing up to three years of administrative claims and structured and unstructured electronic health record (EHR) data...
October 25, 2017: BMC Geriatrics
https://www.readbyqxmd.com/read/29066897/an-annotated-corpus-with-nanomedicine-and-pharmacokinetic-parameters
#16
Nastassja A Lewinski, Ivan Jimenez, Bridget T McInnes
A vast amount of data on nanomedicines is being generated and published, and natural language processing (NLP) approaches can automate the extraction of unstructured text-based data. Annotated corpora are a key resource for NLP and information extraction methods which employ machine learning. Although corpora are available for pharmaceuticals, resources for nanomedicines and nanotechnology are still limited. To foster nanotechnology text mining (NanoNLP) efforts, we have constructed a corpus of annotated drug product inserts taken from the US Food and Drug Administration's Drugs@FDA online database...
2017: International Journal of Nanomedicine
https://www.readbyqxmd.com/read/29063570/best-paper-selection
#17
(no author information available yet)
Althoff, T, Clark K, Leskovec, J. Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health. Trans Assoc Comput Linguist 2016(4):463-76 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5361062/ Kilicoglu, H, Demner-Fushman, D. Bio-SCoRes: A Smorgasbord Architecture for Coreference Resolution in Biomedical Text. PLoS One. 2016 Mar 2;11(3):e0148538 http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0148538 Morid, MA, Fiszman, M, Raja, K, Jonnalagadda, SR, Del Fiol, G...
August 2017: Yearbook of Medical Informatics
https://www.readbyqxmd.com/read/29063569/making-sense-of-big-textual-data-for-health-care-findings-from-the-section-on-clinical-natural-language-processing
#18
A Névéol, P Zweigenbaum
Objectives: To summarize recent research and present a selection of the best papers published in 2016 in the field of clinical Natural Language Processing (NLP). Method: A survey of the literature was performed by the two section editors of the IMIA Yearbook NLP section. Bibliographic databases were searched for papers with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. Papers were automatically ranked and then manually reviewed based on titles and abstracts. A shortlist of candidate best papers was first selected by the section editors before being peer-reviewed by independent external reviewers...
August 2017: Yearbook of Medical Informatics
https://www.readbyqxmd.com/read/29063568/capturing-the-patient-s-perspective-a-review-of-advances-in-natural-language-processing-of-health-related-text
#19
G Gonzalez-Hernandez, A Sarker, K O'Connor, G Savova
Background: Natural Language Processing (NLP) methods are increasingly being utilized to mine knowledge from unstructured health-related texts. Recent advances in noisy text processing techniques are enabling researchers and medical domain experts to go beyond the information encapsulated in published texts (e.g., clinical trials and systematic reviews) and structured questionnaires, and obtain perspectives from other unstructured sources such as Electronic Health Records (EHRs) and social media posts. Objectives: To review the recently published literature discussing the application of NLP techniques for mining health-related information from EHRs and social media posts...
August 2017: Yearbook of Medical Informatics
https://www.readbyqxmd.com/read/29060556/comparing-deep-neural-network-and-other-machine-learning-algorithms-for-stroke-prediction-in-a-large-scale-population-based-electronic-medical-claims-database
#20
Chen-Ying Hung, Wei-Chen Chen, Po-Tsun Lai, Ching-Heng Lin, Chi-Chun Lee
Electronic medical claims (EMCs) can be used to accurately predict the occurrence of a variety of diseases, which can contribute to precise medical interventions. While there is a growing interest in the application of machine learning (ML) techniques to address clinical problems, the use of deep-learning in healthcare have just gained attention recently. Deep learning, such as deep neural network (DNN), has achieved impressive results in the areas of speech recognition, computer vision, and natural language processing in recent years...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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