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artificial Intelligence operational medicine

Wenhao Xu
Microinjection/micromanipulation is more than 100 years old. It is a technique that is instrumental in biomedical research and healthcare. Its longevity lies in its preciseness in mechanical retrieval, or delivery of biological materials, which in some cases is simply necessary or more effective than other retrieval/delivery means. Microinjection is favored for its straightforwardness in transferring contents from micromolecules to macromolecules and from organelles to cells. Microinjection/micromanipulation has been practiced over the century like an art form...
2019: Methods in Molecular Biology
K Harren, F Dittrich, F Reinecke, M Jäger
In the course of digitalization it is becoming increasingly rare for medical documents to be handwritten. As a result, digitalization has already become an integral part of routine patient care but in contrast to other specialist disciplines, such as radiology or laboratory medicine, orthopedics and trauma surgery are still at the beginning of new technologies. Artificial intelligence is not only used in the form of surgical robots in joint surgery or in the design of individualized implants but also provides valuable decision-making aids through appropriate algorithms for diagnosis and treatment...
December 2018: Der Orthopäde
Michael J Zellweger, Andrew Tsirkin, Vasily Vasilchenko, Michael Failer, Alexander Dressel, Marcus E Kleber, Peter Ruff, Winfried März
Background: Known coronary artery disease (CAD) risk scores (e.g., Framingham) estimate the CAD-related event risk rather than presence/absence of CAD. Artificial intelligence (AI) is rarely used in this context. Aims: This study aims to evaluate the diagnostic power of AI (memetic pattern-based algorithm (MPA)) in CAD and to expand its applicability to a broader patient population. Methods and results: Nine hundred eighty-seven patients of the Ludwigshafen Risk and Cardiovascular Health Study (LURIC) were divided into a training ( n  = 493) and a test population ( n  = 494)...
September 2018: EPMA Journal
Jürg Blaser
Challenges of Digital Medicine Abstract. Digitization is increasingly covering more and more sectors, including medicine. To ensure medical operation 365 × 24 hours, progressively more human and financial resources are necessary. The transformation of patient histories from paper into electronic patient records focused initially on documentation. Today, hospital information systems are increasingly used as a platform for the communication of all professionals involved in the patient process - in Switzerland, however, so far without providing patients direct access to their data...
June 2018: Praxis
Vincent Weidlich, Georg A Weidlich
Artifical Intelligence (AI) was reviewed with a focus on its potential applicability to radiation oncology. The improvement of process efficiencies and the prevention of errors were found to be the most significant contributions of AI to radiation oncology. It was found that the prevention of errors is most effective when data transfer processes were automated and operational decisions were based on logical or learned evaluations by the system. It was concluded that AI could greatly improve the efficiency and accuracy of radiation oncology operations...
April 13, 2018: Curēus
Gustavo A Alonso-Silverio, Fernando Pérez-Escamirosa, Raúl Bruno-Sanchez, José L Ortiz-Simon, Roberto Muñoz-Guerrero, Arturo Minor-Martinez, Antonio Alarcón-Paredes
BACKGROUND: A trainer for online laparoscopic surgical skills assessment based on the performance of experts and nonexperts is presented. The system uses computer vision, augmented reality, and artificial intelligence algorithms, implemented into a Raspberry Pi board with Python programming language. METHODS: Two training tasks were evaluated by the laparoscopic system: transferring and pattern cutting. Computer vision libraries were used to obtain the number of transferred points and simulated pattern cutting trace by means of tracking of the laparoscopic instrument...
August 2018: Surgical Innovation
Khader Shameer, Kipp W Johnson, Benjamin S Glicksberg, Joel T Dudley, Partho P Sengupta
Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform...
July 2018: Heart: Official Journal of the British Cardiac Society
Hongmao Sun, Kimloan Nguyen, Edward Kerns, Zhengyin Yan, Kyeong Ri Yu, Pranav Shah, Ajit Jadhav, Xin Xu
Cell membrane permeability is an important determinant for oral absorption and bioavailability of a drug molecule. An in silico model predicting drug permeability is described, which is built based on a large permeability dataset of 7488 compound entries or 5435 structurally unique molecules measured by the same lab using parallel artificial membrane permeability assay (PAMPA). On the basis of customized molecular descriptors, the support vector regression (SVR) model trained with 4071 compounds with quantitative data is able to predict the remaining 1364 compounds with the qualitative data with an area under the curve of receiver operating characteristic (AUC-ROC) of 0...
February 1, 2017: Bioorganic & Medicinal Chemistry
Aaron M Cohen, Neil R Smalheiser, Marian S McDonagh, Clement Yu, Clive E Adams, John M Davis, Philip S Yu
OBJECTIVE: For many literature review tasks, including systematic review (SR) and other aspects of evidence-based medicine, it is important to know whether an article describes a randomized controlled trial (RCT). Current manual annotation is not complete or flexible enough for the SR process. In this work, highly accurate machine learning predictive models were built that include confidence predictions of whether an article is an RCT. MATERIALS AND METHODS: The LibSVM classifier was used with forward selection of potential feature sets on a large human-related subset of MEDLINE to create a classification model requiring only the citation, abstract, and MeSH terms for each article...
May 2015: Journal of the American Medical Informatics Association: JAMIA
Mathukumalli Vidyasagar
This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction problems are divided into two categories: sparse classification and sparse regression. In classification, the clinical parameter to be predicted is binary, whereas in regression, the parameter is a real number. Well-known methods for both classes of problems are briefly discussed...
2015: Annual Review of Pharmacology and Toxicology
Hui Li, Zhanzhan Zhang
Quantum image recognition is a technology by using quantum algorithm to process the image information. It can obtain better effect than classical algorithm. In this paper, four different quantum algorithms are used in the three stages of palmprint recognition. First, quantum adaptive median filtering algorithm is presented in palmprint filtering processing. Quantum filtering algorithm can get a better filtering result than classical algorithm through the comparison. Next, quantum Fourier transform (QFT) is used to extract pattern features by only one operation due to quantum parallelism...
2014: TheScientificWorldJournal
Mattia Cf Prosperi, Susana Marinho, Angela Simpson, Adnan Custovic, Iain E Buchan
BACKGROUND: There is increasing recognition that asthma and eczema are heterogeneous diseases. We investigated the predictive ability of a spectrum of machine learning methods to disambiguate clinical sub-groups of asthma, wheeze and eczema, using a large heterogeneous set of attributes in an unselected population. The aim was to identify to what extent such heterogeneous information can be combined to reveal specific clinical manifestations. METHODS: The study population comprised a cross-sectional sample of adults, and included representatives of the general population enriched by subjects with asthma...
2014: BMC Medical Genomics
M Simonov, G Delconte
INTRODUCTION: This article is part of the Focus Theme of Methods of Information in Medicine on "New Methodologies for Patients Rehabilitation". BACKGROUND: The article presents the approach in which the rehabilitative exercise prepared by healthcare professional is encoded as formal knowledge and used by humanoid robot to assist patients without involving other care actors. OBJECTIVES: The main objective is the use of humanoids in rehabilitative care...
2015: Methods of Information in Medicine
Quang Nguyen, Hamed Valizadegan, Milos Hauskrecht
OBJECTIVE: Learning of classification models in medicine often relies on data labeled by a human expert. Since labeling of clinical data may be time-consuming, finding ways of alleviating the labeling costs is critical for our ability to automatically learn such models. In this paper we propose a new machine learning approach that is able to learn improved binary classification models more efficiently by refining the binary class information in the training phase with soft labels that reflect how strongly the human expert feels about the original class labels...
May 2014: Journal of the American Medical Informatics Association: JAMIA
Mattia C F Prosperi, Danielle Belgrave, Iain Buchan, Angela Simpson, Adnan Custovic
BACKGROUND: Identifying different patterns of allergens and understanding their predictive ability in relation to asthma and other allergic diseases is crucial for the design of personalized diagnostic tools. METHODS: Allergen-IgE screening using ImmunoCAP ISAC(®) assay was performed at age 11 yrs in children participating a population-based birth cohort. Logistic regression (LR) and nonlinear statistical learning models, including random forests (RF) and Bayesian networks (BN), coupled with feature selection approaches, were used to identify patterns of allergen responses associated with asthma, rhino-conjunctivitis, wheeze, eczema and airway hyper-reactivity (AHR, positive methacholine challenge)...
February 2014: Pediatric Allergy and Immunology
Guy Haskin Fernald, Russ B Altman
Despite recent advances in molecular medicine and rational drug design, many drugs still fail because toxic effects arise at the cellular and tissue level. In order to better understand these effects, cellular assays can generate high-throughput measurements of gene expression changes induced by small molecules. However, our understanding of how the chemical features of small molecules influence gene expression is very limited. Therefore, we investigated the extent to which chemical features of small molecules can reliably be associated with significant changes in gene expression...
October 28, 2013: Journal of Chemical Information and Modeling
L Pignolo, F Riganello, G Dolce, W G Sannita
Ambient Intelligence (AmI) provides extended but unobtrusive sensing and computing devices and ubiquitous networking for human/environment interaction. It is a new paradigm in information technology compliant with the international Integrating Healthcare Enterprise board (IHE) and eHealth HL7 technological standards in the functional integration of biomedical domotics and informatics in hospital and home care. AmI allows real-time automatic recording of biological/medical information and environmental data...
April 2013: Clinical EEG and Neuroscience: Official Journal of the EEG and Clinical Neuroscience Society (ENCS)
Casey C Bennett, Kris Hauser
OBJECTIVE: In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (non-disease-specific) computational/artificial intelligence (AI) framework to address these challenges. This framework serves two potential functions: (1) a simulation environment for exploring various healthcare policies, payment methodologies, etc...
January 2013: Artificial Intelligence in Medicine
Marjorie McShane, Stephen Beale, Sergei Nirenburg, Bruce Jarrell, George Fantry
OBJECTIVE: To use the detection of clinically relevant inconsistencies to support the reasoning capabilities of intelligent agents acting as physicians and tutors in the realm of clinical medicine. METHODS: We are developing a cognitive architecture, OntoAgent, that supports the creation and deployment of intelligent agents capable of simulating human-like abilities. The agents, which have a simulated mind and, if applicable, a simulated body, are intended to operate as members of multi-agent teams featuring both artificial and human agents...
July 2012: Artificial Intelligence in Medicine
Farrokh Alemi, Manabu Torii, Martin J Atherton, David C Pattie, Kenneth L Cox
OBJECTIVE: This article aims to examine whether words listed in reasons for appointments could effectively predict laboratory-verified influenza cases in syndromic surveillance systems. METHODS: Data were collected from the Armed Forces Health Longitudinal Technological Application medical record system. We used 2 algorithms to combine the impact of words within reasons for appointments: Dependent (DBSt) and Independent (IBSt) Bayesian System. We used receiver operating characteristic curves to compare the accuracy of these 2 methods of processing reasons for appointments against current and previous lists of diagnoses used in the Department of Defense's syndromic surveillance system...
March 2012: Medical Decision Making: An International Journal of the Society for Medical Decision Making
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