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https://www.readbyqxmd.com/read/28918412/prediction-of-early-unplanned-intensive-care-unit-readmission-in-a-uk-tertiary-care-hospital-a-cross-sectional-machine-learning-approach
#1
Thomas Desautels, Ritankar Das, Jacob Calvert, Monica Trivedi, Charlotte Summers, David J Wales, Ari Ercole
OBJECTIVES: Unplanned readmissions to the intensive care unit (ICU) are highly undesirable, increasing variance in care, making resource planning difficult and potentially increasing length of stay and mortality in some settings. Identifying patients who are likely to suffer unplanned ICU readmission could reduce the frequency of this adverse event. SETTING: A single academic, tertiary care hospital in the UK. PARTICIPANTS: A set of 3326 ICU episodes collected between October 2014 and August 2016...
September 15, 2017: BMJ Open
https://www.readbyqxmd.com/read/28915930/predicting-activities-of-daily-living-for-cancer-patients-using-an-ontology-guided-machine-learning-methodology
#2
Hua Min, Hedyeh Mobahi, Katherine Irvin, Sanja Avramovic, Janusz Wojtusiak
BACKGROUND: Bio-ontologies are becoming increasingly important in knowledge representation and in the machine learning (ML) fields. This paper presents a ML approach that incorporates bio-ontologies and its application to the SEER-MHOS dataset to discover patterns of patient characteristics that impact the ability to perform activities of daily living (ADLs). Bio-ontologies are used to provide computable knowledge for ML methods to "understand" biomedical data. RESULTS: This retrospective study included 723 cancer patients from the SEER-MHOS dataset...
September 16, 2017: Journal of Biomedical Semantics
https://www.readbyqxmd.com/read/28899551/relationship-between-neuronal-network-architecture-and-naming-performance-in-temporal-lobe-epilepsy-a-connectome-based-approach-using-machine-learning
#3
REVIEW
B C Munsell, G Wu, J Fridriksson, K Thayer, N Mofrad, N Desisto, D Shen, L Bonilha
Impaired confrontation naming is a common symptom of temporal lobe epilepsy (TLE). The neurobiological mechanisms underlying this impairment are poorly understood but may indicate a structural disorganization of broadly distributed neuronal networks that support naming ability. Importantly, naming is frequently impaired in other neurological disorders and by contrasting the neuronal structures supporting naming in TLE with other diseases, it will become possible to elucidate the common systems supporting naming...
September 9, 2017: Brain and Language
https://www.readbyqxmd.com/read/28888332/machine-learning-based-electronic-triage-more-accurately-differentiates-patients-with-respect-to%C3%A2-clinical-outcomes-compared-with-the-emergency-severity-index
#4
Scott Levin, Matthew Toerper, Eric Hamrock, Jeremiah S Hinson, Sean Barnes, Heather Gardner, Andrea Dugas, Bob Linton, Tom Kirsch, Gabor Kelen
STUDY OBJECTIVE: Standards for emergency department (ED) triage in the United States rely heavily on subjective assessment and are limited in their ability to risk-stratify patients. This study seeks to evaluate an electronic triage system (e-triage) based on machine learning that predicts likelihood of acute outcomes enabling improved patient differentiation. METHODS: A multisite, retrospective, cross-sectional study of 172,726 ED visits from urban and community EDs was conducted...
September 6, 2017: Annals of Emergency Medicine
https://www.readbyqxmd.com/read/28887351/using-predictive-analytics-and-big-data-to-optimize-pharmaceutical-outcomes
#5
Inmaculada Hernandez, Yuting Zhang
PURPOSE: The steps involved, the resources needed, and the challenges associated with applying predictive analytics in healthcare are described, with a review of successful applications of predictive analytics in implementing population health management interventions that target medication-related patient outcomes. SUMMARY: In healthcare, the term big data typically refers to large quantities of electronic health record, administrative claims, and clinical trial data as well as data collected from smartphone applications, wearable devices, social media, and personal genomics services; predictive analytics refers to innovative methods of analysis developed to overcome challenges associated with big data, including a variety of statistical techniques ranging from predictive modeling to machine learning to data mining...
September 15, 2017: American Journal of Health-system Pharmacy: AJHP
https://www.readbyqxmd.com/read/28883645/combining-biomarkers-with-emr-data-to-identify-patients-in-different-phases-of-sepsis
#6
Ishan Taneja, Bobby Reddy, Gregory Damhorst, Sihai Dave Zhao, Umer Hassan, Zachary Price, Tor Jensen, Tanmay Ghonge, Manish Patel, Samuel Wachspress, Jake Winter, Michael Rappleye, Gillian Smith, Ryan Healey, Muhammad Ajmal, Muhammad Khan, Jay Patel, Harsh Rawal, Raiya Sarwar, Sumeet Soni, Syed Anwaruddin, Benjamin Davis, James Kumar, Karen White, Rashid Bashir, Ruoqing Zhu
Sepsis is a leading cause of death and is the most expensive condition to treat in U.S. hospitals. Despite targeted efforts to automate earlier detection of sepsis, current techniques rely exclusively on using either standard clinical data or novel biomarker measurements. In this study, we apply machine learning techniques to assess the predictive power of combining multiple biomarker measurements from a single blood sample with electronic medical record data (EMR) for the identification of patients in the early to peak phase of sepsis in a large community hospital setting...
September 7, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28883203/machine-learning-models-of-post-intubation-hypoxia-during-general-anesthesia
#7
Philipp Sippl, Thomas Ganslandt, Hans-Ulrich Prokosch, Tino Muenster, Dennis Toddenroth
Fine-meshed perioperative measurements are offering enormous potential for automatically investigating clinical complications during general anesthesia. In this study, we employed multiple machine learning methods to model perioperative hypoxia and compare their respective capabilities. After exporting and visualizing 620 series of perioperative vital signs, we had ten anesthesiologists annotate the subjective presence and severity of temporary post-intubation oxygen desaturation. We then applied specific clustering and prediction methods on the acquired annotations, and evaluated their performance in comparison to the inter-rater agreement between experts...
2017: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/28880900/phase-separation-of-the-plasma-membrane-in-human-red-blood-cells-as-a-potential-tool-for-diagnosis-and-progression-monitoring-of-type-1-diabetes-mellitus
#8
Giuseppe Maulucci, Ermanno Cordelli, Alessandro Rizzi, Francesca De Leva, Massimiliano Papi, Gabriele Ciasca, Daniela Samengo, Giovambattista Pani, Dario Pitocco, Paolo Soda, Giovanni Ghirlanda, Giulio Iannello, Marco De Spirito
Glycosylation, oxidation and other post-translational modifications of membrane and transmembrane proteins can alter lipid density, packing and interactions, and are considered an important factor that affects fluidity variation in membranes. Red blood cells (RBC) membrane physical state, showing pronounced alterations in Type 1 diabetes mellitus (T1DM), could be the ideal candidate for monitoring the disease progression and the effects of therapies. On these grounds, the measurement of RBC membrane fluidity alterations can furnish a more sensitive index in T1DM diagnosis and disease progression than Glycosylated hemoglobin (HbA1c), which reflects only the information related to glycosylation processes...
2017: PloS One
https://www.readbyqxmd.com/read/28876574/holding-the-torch-up-high-a-medical-historical-evaluation-of-surgical-advances-during-the-great-war-1914-1918-in-memory-of-those-that-served-and-fell
#9
G Scharf
"How wide and varied is the experience of the battlefield and how fertile the blood of warriors in raising good surgeons" Sir Clifford Allbutt (1898). With these sentiments of the medical lessons learned in war and conflict, with the background of the poem of "In Flanders Field", written by a doctor who had South African War connections, reasons (the Somme and third Ypres battles) will be given that this was indeed a "GREAT WAR" as the world history, weapons, strategy, tactics and wounding patterns had changed dramatically...
September 2017: South African Journal of Surgery. Suid-Afrikaanse Tydskrif Vir Chirurgie
https://www.readbyqxmd.com/read/28870022/playing-to-our-human-strengths-to-prepare-medical-students-for-the-future
#10
Julie Chen
We are living in an age where artificial intelligence and astounding technological advances are bringing truly remarkable change to healthcare. Medical knowledge and skills which form the core responsibility of doctors such as making diagnoses may increasingly be delivered by robots. Machines are gradually acquiring human abilities such as deep learning and empathy. What, then is the role of doctors in future healthcare? And what direction should medical schools be taking to prepare their graduates? This article will give an overview of the evolving technological landscape of healthcare and examine the issues undergraduate medical education may have to address...
September 2017: Korean Journal of Medical Education
https://www.readbyqxmd.com/read/28867810/longitudinal-study-based-dementia-prediction-for-public-health
#11
HeeChel Kim, Hong-Woo Chun, Seonho Kim, Byoung-Youl Coh, Oh-Jin Kwon, Yeong-Ho Moon
The issue of public health in Korea has attracted significant attention given the aging of the country's population, which has created many types of social problems. The approach proposed in this article aims to address dementia, one of the most significant symptoms of aging and a public health care issue in Korea. The Korean National Health Insurance Service Senior Cohort Database contains personal medical data of every citizen in Korea. There are many different medical history patterns between individuals with dementia and normal controls...
August 30, 2017: International Journal of Environmental Research and Public Health
https://www.readbyqxmd.com/read/28866570/phenolines-phenotype-comparison-visualizations-for-disease-subtyping-via-topic-models
#12
Michael Glueck, Mahdi Pakdaman Naeini, Finale Doshi-Velez, Fanny Chevalier, Azam Khan, Daniel Wigdor, Michael Brudno
PhenoLines is a visual analysis tool for the interpretation of disease subtypes, derived from the application of topic models to clinical data. Topic models enable one to mine cross-sectional patient comorbidity data (e.g., electronic health records) and construct disease subtypes-each with its own temporally evolving prevalence and co-occurrence of phenotypes-without requiring aligned longitudinal phenotype data for all patients. However, the dimensionality of topic models makes interpretation challenging, and de facto analyses provide little intuition regarding phenotype relevance or phenotype interrelationships...
August 29, 2017: IEEE Transactions on Visualization and Computer Graphics
https://www.readbyqxmd.com/read/28866567/clustervision-visual-supervision-of-unsupervised-clustering
#13
Bum Chul Kwon, Ben Eysenbach, Janu Verma, Kenney Ng, Christopher deFilippi, Walter F Stewart, Adam Perer
Clustering, the process of grouping together similar items into distinct partitions, is a common type of unsupervised machine learning that can be useful for summarizing and aggregating complex multi-dimensional data. However, data can be clustered in many ways, and there exist a large body of algorithms designed to reveal different patterns. While having access to a wide variety of algorithms is helpful, in practice, it is quite difficult for data scientists to choose and parameterize algorithms to get the clustering results relevant for their dataset and analytical tasks...
August 29, 2017: IEEE Transactions on Visualization and Computer Graphics
https://www.readbyqxmd.com/read/28861720/accurate-identification-of-fatty-liver-disease-in-data-warehouse-utilizing-natural-language-processing
#14
Joseph S Redman, Yamini Natarajan, Jason K Hou, Jingqi Wang, Muzammil Hanif, Hua Feng, Jennifer R Kramer, Roxanne Desiderio, Hua Xu, Hashem B El-Serag, Fasiha Kanwal
INTRODUCTION: Natural language processing is a powerful technique of machine learning capable of maximizing data extraction from complex electronic medical records. METHODS: We utilized this technique to develop algorithms capable of "reading" full-text radiology reports to accurately identify the presence of fatty liver disease. Abdominal ultrasound, computerized tomography, and magnetic resonance imaging reports were retrieved from the Veterans Affairs Corporate Data Warehouse from a random national sample of 652 patients...
August 31, 2017: Digestive Diseases and Sciences
https://www.readbyqxmd.com/read/28861708/breast-cancer-cell-nuclei-classification-in-histopathology-images-using-deep-neural-networks
#15
REVIEW
Yangqin Feng, Lei Zhang, Zhang Yi
PURPOSE: Cell nuclei classification in breast cancer histopathology images plays an important role in effective diagnose since breast cancer can often be characterized by its expression in cell nuclei. However, due to the small and variant sizes of cell nuclei, and heavy noise in histopathology images, traditional machine learning methods cannot achieve desirable recognition accuracy. To address this challenge, this paper aims to present a novel deep neural network which performs representation learning and cell nuclei recognition in an end-to-end manner...
August 31, 2017: International Journal of Computer Assisted Radiology and Surgery
https://www.readbyqxmd.com/read/28860874/can-machine-learning-complement-traditional-medical-device-surveillance-a-case-study-of-dual-chamber-implantable-cardioverter-defibrillators
#16
Joseph S Ross, Jonathan Bates, Craig S Parzynski, Joseph G Akar, Jeptha P Curtis, Nihar R Desai, James V Freeman, Ginger M Gamble, Richard Kuntz, Shu-Xia Li, Danica Marinac-Dabic, Frederick A Masoudi, Sharon-Lise T Normand, Isuru Ranasinghe, Richard E Shaw, Harlan M Krumholz
BACKGROUND: Machine learning methods may complement traditional analytic methods for medical device surveillance. METHODS AND RESULTS: Using data from the National Cardiovascular Data Registry for implantable cardioverter-defibrillators (ICDs) linked to Medicare administrative claims for longitudinal follow-up, we applied three statistical approaches to safety-signal detection for commonly used dual-chamber ICDs that used two propensity score (PS) models: one specified by subject-matter experts (PS-SME), and the other one by machine learning-based selection (PS-ML)...
2017: Medical Devices: Evidence and Research
https://www.readbyqxmd.com/read/28859346/data-science-priorities-for-a-university-hospital-based-institute-of-infectious-diseases-a-viewpoint
#17
Alain-Jacques Valleron
Automation of laboratory tests, bioinformatic analysis of biological sequences, and professional data management are used routinely in a modern university hospital-based infectious diseases institute. This dates back to at least the 1980s. However, the scientific methods of this 21st century are changing with the increased power and speed of computers, with the "big data" revolution having already happened in genomics and environment, and eventually arriving in medical informatics. The research will be increasingly "data driven," and the powerful machine learning methods whose efficiency is demonstrated in daily life will also revolutionize medical research...
August 15, 2017: Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America
https://www.readbyqxmd.com/read/28843999/predicting-clinical-symptoms-of-attention-deficit-hyperactivity-disorder-based-on-temporal-patterns-between-and-within-intrinsic-connectivity-networks
#18
Xun-Heng Wang, Yun Jiao, Lihua Li
Attention deficit hyperactivity disorder (ADHD) is a common brain disorder with high prevalence in school-age children. Previously developed machine learning-based methods have discriminated patients with ADHD from normal controls by providing label information of the disease for individuals. Inattention and impulsivity are the two most significant clinical symptoms of ADHD. However, predicting clinical symptoms (i.e., inattention and impulsivity) is a challenging task based on neuroimaging data. The goal of this study is twofold: to build predictive models for clinical symptoms of ADHD based on resting-state fMRI and to mine brain networks for predictive patterns of inattention and impulsivity...
August 24, 2017: Neuroscience
https://www.readbyqxmd.com/read/28842613/accuracy-of-deep-learning-a-machine-learning-technology-using-ultra-wide-field-fundus-ophthalmoscopy-for-detecting-rhegmatogenous-retinal-detachment
#19
Hideharu Ohsugi, Hitoshi Tabuchi, Hiroki Enno, Naofumi Ishitobi
Rhegmatogenous retinal detachment (RRD) is a serious condition that can lead to blindness; however, it is highly treatable with timely and appropriate treatment. Thus, early diagnosis and treatment of RRD is crucial. In this study, we applied deep learning, a machine-learning technology, to detect RRD using ultra-wide-field fundus images and investigated its performance. In total, 411 images (329 for training and 82 for grading) from 407 RRD patients and 420 images (336 for training and 84 for grading) from 238 non-RRD patients were used in this study...
August 25, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28838801/optimization-of-infobutton-design-and-implementation-a-systematic-review
#20
REVIEW
Miguel Teixeira, David A Cook, Bret S E Heale, Guilherme Del Fiol
OBJECTIVE: Infobuttons are clinical decision tools embedded in the electronic health record that attempt to link clinical data with context sensitive knowledge resources. We systematically reviewed technical approaches that contribute to improved infobutton design, implementation and functionality. METHODS: We searched databases including MEDLINE, EMBASE, and the Cochrane Library database from inception to March 1, 2016 for studies describing the use of infobuttons...
August 21, 2017: Journal of Biomedical Informatics
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