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https://www.readbyqxmd.com/read/29916031/-artificial-intelligence-in-psychiatry-an-overview
#1
REVIEW
A Meyer-Lindenberg
Artificial intelligence and the underlying methods of machine learning and neuronal networks (NN) have made dramatic progress in recent years and have allowed computers to reach superhuman performance in domains that used to be thought of as uniquely human. In this overview, the underlying methodological developments that made this possible are briefly delineated and then the applications to psychiatry in three domains are discussed: precision medicine and biomarkers, natural language processing and artificial intelligence-based psychotherapeutic interventions...
June 18, 2018: Der Nervenarzt
https://www.readbyqxmd.com/read/29896316/premenopausal-breast-cancer-potential-clinical-utility-of-a-multi-omics-based-machine-learning-approach-for-patient-stratification
#2
Holger Fröhlich, Sabyasachi Patjoshi, Kristina Yeghiazaryan, Christina Kehrer, Walther Kuhn, Olga Golubnitschaja
Background: The breast cancer (BC) epidemic is a multifactorial disease attributed to the early twenty-first century: about two million of new cases and half a million deaths are registered annually worldwide. New trends are emerging now: on the one hand, with respect to the geographical BC prevalence and, on the other hand, with respect to the age distribution. Recent statistics demonstrate that young populations are getting more and more affected by BC in both Eastern and Western countries...
June 2018: EPMA Journal
https://www.readbyqxmd.com/read/29889182/unhealthy-behaviors-prevention-measures-and-neighborhood-cardiovascular-health-a-machine-learning-approach
#3
Yan Li, Shelley H Liu, Li Niu, Bian Liu
This study identifies and ranks predictors of cardiovascular health at the neighborhood level in the United States. We merged the 500 Cities Data and the 2011-2015 American Community Survey to create a new data set that includes sociodemographic characteristics, health behaviors, prevention measures, and cardiovascular health outcomes for more than 28 000 census tracts in the United States. We used random forest to rank predictors of coronary heart disease and stroke. For coronary heart disease, the top 5 ordered predictors were the prevalence of taking medicine for high blood pressure control, binge drinking, being aged 65 years or older, lack of leisure-time physical activity, and obesity...
June 7, 2018: Journal of Public Health Management and Practice: JPHMP
https://www.readbyqxmd.com/read/29882974/neuroimaging-studies-illustrate-the-commonalities-between-ageing-and-brain-diseases
#4
REVIEW
James H Cole
The lack of specificity in neuroimaging studies of neurological and psychiatric diseases suggests that these different diseases have more in common than is generally considered. Potentially, features that are secondary effects of different pathological processes may share common neurobiological underpinnings. Intriguingly, many of these mechanisms are also observed in studies of normal (i.e., non-pathological) brain ageing. Different brain diseases may be causing premature or accelerated ageing to the brain, an idea that is supported by a line of "brain ageing" research that combines neuroimaging data with machine learning analysis...
June 8, 2018: BioEssays: News and Reviews in Molecular, Cellular and Developmental Biology
https://www.readbyqxmd.com/read/29880128/artificial-intelligence-in-cardiology
#5
REVIEW
Kipp W Johnson, Jessica Torres Soto, Benjamin S Glicksberg, Khader Shameer, Riccardo Miotto, Mohsin Ali, Euan Ashley, Joel T Dudley
Artificial intelligence and machine learning are poised to influence nearly every aspect of the human condition, and cardiology is not an exception to this trend. This paper provides a guide for clinicians on relevant aspects of artificial intelligence and machine learning, reviews selected applications of these methods in cardiology to date, and identifies how cardiovascular medicine could incorporate artificial intelligence in the future. In particular, the paper first reviews predictive modeling concepts relevant to cardiology such as feature selection and frequent pitfalls such as improper dichotomization...
June 12, 2018: Journal of the American College of Cardiology
https://www.readbyqxmd.com/read/29860027/patient-similarity-networks-for-precision-medicine
#6
REVIEW
Shraddha Pai, Gary D Bader
Clinical research and practice in the 21st century is poised to be transformed by analysis of computable electronic medical records and population-level genome-scale patient profiles. Genomic data captures genetic and environmental state, providing information about heterogeneity in disease and treatment outcome, but genomic-based clinical risk scores are limited. Achieving the goal of routine precision medicine that takes advantage of this rich genomics data will require computational methods that support heterogeneous data, have excellent predictive performance, and ideally, provide biologically-interpretable results...
May 31, 2018: Journal of Molecular Biology
https://www.readbyqxmd.com/read/29850526/machine-learning-augmented-propensity-score-adjusted-multilevel-mixed-effects-panel-analysis-of-hands-on-cooking-and-nutrition-education-versus-traditional-curriculum-for-medical-students-as-preventive-cardiology-multisite-cohort-study-of-3-248-trainees-over
#7
Dominique J Monlezun, Lyn Dart, Anne Vanbeber, Peggy Smith-Barbaro, Vanessa Costilla, Charlotte Samuel, Carol A Terregino, Emine Ercikan Abali, Beth Dollinger, Nicole Baumgartner, Nicholas Kramer, Alex Seelochan, Sabira Taher, Mark Deutchman, Meredith Evans, Robert B Ellis, Sonia Oyola, Geeta Maker-Clark, Tomi Dreibelbis, Isadore Budnick, David Tran, Nicole DeValle, Rachel Shepard, Erika Chow, Christine Petrin, Alexander Razavi, Casey McGowan, Austin Grant, Mackenzie Bird, Connor Carry, Glynis McGowan, Colleen McCullough, Casey M Berman, Kerri Dotson, Tianhua Niu, Leah Sarris, Timothy S Harlan, On Behalf Of The Chop Co-Investigators
Background: Cardiovascular disease (CVD) annually claims more lives and costs more dollars than any other disease globally amid widening health disparities, despite the known significant reductions in this burden by low cost dietary changes. The world's first medical school-based teaching kitchen therefore launched CHOP-Medical Students as the largest known multisite cohort study of hands-on cooking and nutrition education versus traditional curriculum for medical students. Methods: This analysis provides a novel integration of artificial intelligence-based machine learning (ML) with causal inference statistics...
2018: BioMed Research International
https://www.readbyqxmd.com/read/29806902/groundhog-day-for-medical-artificial-intelligence
#8
Alex John London
Following a boom in investment and overinflated expectations in the 1980s, artificial intelligence entered a period of retrenchment known as the "AI winter." With advances in the field of machine learning and the availability of large datasets for training various types of artificial neural networks, AI is in another cycle of halcyon days. Although medicine is particularly recalcitrant to change, applications of AI in health care have professionals in fields like radiology worried about the future of their careers and have the public tittering about the prospect of soulless machines making life-and-death decisions...
May 2018: Hastings Center Report
https://www.readbyqxmd.com/read/29801942/big-data-in-forensic-science-and-medicine
#9
EDITORIAL
Thomas Lefèvre
In less than a decade, big data in medicine has become quite a phenomenon and many biomedical disciplines got their own tribune on the topic. Perspectives and debates are flourishing while there is a lack for a consensual definition for big data. The 3Vs paradigm is frequently evoked to define the big data principles and stands for Volume, Variety and Velocity. Even according to this paradigm, genuine big data studies are still scarce in medicine and may not meet all expectations. On one hand, techniques usually presented as specific to the big data such as machine learning techniques are supposed to support the ambition of personalized, predictive and preventive medicines...
July 2018: Journal of Forensic and Legal Medicine
https://www.readbyqxmd.com/read/29801696/deep-generative-learning-for-automated-ehr-diagnosis-of-traditional-chinese-medicine
#10
Zhaohui Liang, Jun Liu, Aihua Ou, Honglai Zhang, Ziping Li, Jimmy Xiangji Huang
BACKGROUND: Computer-aided medical decision-making (CAMDM) is the method to utilize massive EMR data as both empirical and evidence support for the decision procedure of healthcare activities. Well-developed information infrastructure, such as hospital information systems and disease surveillance systems, provides abundant data for CAMDM. However, the complexity of EMR data with abstract medical knowledge makes the conventional model incompetent for the analysis. Thus a deep belief networks (DBN) based model is proposed to simulate the information analysis and decision-making procedure in medical practice...
May 4, 2018: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/29791959/sarcopenia-beyond-muscle-atrophy-and-into-the-new-frontiers-of-opportunistic-imaging-precision-medicine-and-machine-learning
#11
Leon Lenchik, Robert D Boutin
As populations continue to age worldwide, the impact of sarcopenia on public health will continue to grow. The clinically relevant and increasingly common diagnosis of sarcopenia is at the confluence of three tectonic shifts in medicine: opportunistic imaging, precision medicine, and machine learning. This review focuses on the state-of-the-art imaging of sarcopenia and provides context for such imaging by discussing the epidemiology, pathophysiology, consequences, and future directions in the field of sarcopenia...
July 2018: Seminars in Musculoskeletal Radiology
https://www.readbyqxmd.com/read/29791132/ptml-model-of-chembl-data-for-dopamine-targets-docking-synthesis-and-assay-of-new-plg-peptidomimetics
#12
Joana Ferreira da Costa, David Silva, Olga Caamaño, José M Brea, Maria Isabel Loza, Cristian R Munteanu, Alejandro Pazos, Xerardo García-Mera, Humbert González-Díaz
Predicting Drug-Protein Interactions (DPIs) for target proteins involved in Dopamine pathways is very important goal in medicinal chemistry. We can tackle this problem using Molecular Docking or Machine Learning (ML) models for one specific protein. Unfortunately, these models fail to account for large and complex Big Data sets of preclinical assays reported in public databases. This includes multiple conditions of assay like different experimental parameters, biological assays, target proteins, cell lines, organism of the target, organism of assay, etc...
May 23, 2018: ACS Chemical Neuroscience
https://www.readbyqxmd.com/read/29790107/the-role-of-pharmacogenomics-in-bipolar-disorder-moving-towards-precision-medicine
#13
REVIEW
Claudia Pisanu, Urs Heilbronner, Alessio Squassina
Bipolar disorder (BD) is a common and disabling psychiatric condition with a severe socioeconomic impact. BD is treated with mood stabilizers, among which lithium represents the first-line treatment. Lithium alone or in combination is effective in 60% of chronically treated patients, but response remains heterogenous and a large number of patients require a change in therapy after several weeks or months. Many studies have so far tried to identify molecular and genetic markers that could help us to predict response to mood stabilizers or the risk for adverse drug reactions...
May 22, 2018: Molecular Diagnosis & Therapy
https://www.readbyqxmd.com/read/29787940/survey-on-deep-learning-for-radiotherapy
#14
REVIEW
Philippe Meyer, Vincent Noblet, Christophe Mazzara, Alex Lallement
More than 50% of cancer patients are treated with radiotherapy, either exclusively or in combination with other methods. The planning and delivery of radiotherapy treatment is a complex process, but can now be greatly facilitated by artificial intelligence technology. Deep learning is the fastest-growing field in artificial intelligence and has been successfully used in recent years in many domains, including medicine. In this article, we first explain the concept of deep learning, addressing it in the broader context of machine learning...
May 17, 2018: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/29784537/artificial-intelligence-a-joint-narrative-on-potential-use-in-pediatric-stem-and-immune-cell-therapies-and-regenerative-medicine
#15
REVIEW
Irena Sniecinski, Jerard Seghatchian
Artificial Intelligence (AI) reflects the intelligence exhibited by machines and software. It is a highly desirable academic field of many current fields of studies. Leading AI researchers describe the field as "the study and design of intelligent agents". McCarthy invented this term in 1955 and defined it as "the science and engineering of making intelligent machines". The central goals of AI research are reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects...
May 9, 2018: Transfusion and Apheresis Science
https://www.readbyqxmd.com/read/29771635/pathogenesis-based-treatments-in-primary-sjogren-s-syndrome-using-artificial-intelligence-and-advanced-machine-learning-techniques-a-systematic-literature-review
#16
Nathan Foulquier, Pascal Redou, Christophe Le Gal, Bénédicte Rouvière, Jacques-Olivier Pers, Alain Saraux
Big data analysis has become a common way to extract information from complex and large datasets among most scientific domains. This approach is now used to study large cohorts of patients in medicine. This work is a review of publications that have used artificial intelligence and advanced machine learning techniques to study physio pathogenesis-based treatments in pSS. A systematic literature review retrieved all articles reporting on the use of advanced statistical analysis applied to the study of systemic autoimmune diseases (SADs) over the last decade...
May 17, 2018: Human Vaccines & Immunotherapeutics
https://www.readbyqxmd.com/read/29769331/temporal-transcriptional-logic-of-dynamic-regulatory-networks-underlying-nitrogen-signaling-and-use-in-plants
#17
Kranthi Varala, Amy Marshall-Colón, Jacopo Cirrone, Matthew D Brooks, Angelo V Pasquino, Sophie Léran, Shipra Mittal, Tara M Rock, Molly B Edwards, Grace J Kim, Sandrine Ruffel, W Richard McCombie, Dennis Shasha, Gloria M Coruzzi
This study exploits time, the relatively unexplored fourth dimension of gene regulatory networks (GRNs), to learn the temporal transcriptional logic underlying dynamic nitrogen (N) signaling in plants. Our "just-in-time" analysis of time-series transcriptome data uncovered a temporal cascade of cis elements underlying dynamic N signaling. To infer transcription factor (TF)-target edges in a GRN, we applied a time-based machine learning method to 2,174 dynamic N-responsive genes. We experimentally determined a network precision cutoff, using TF-regulated genome-wide targets of three TF hubs (CRF4, SNZ, and CDF1), used to "prune" the network to 155 TFs and 608 targets...
May 16, 2018: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/29763706/exploiting-semantic-patterns-over-biomedical-knowledge-graphs-for-predicting-treatment-and-causative-relations
#18
Gokhan Bakal, Preetham Talari, Elijah V Kakani, Ramakanth Kavuluru
BACKGROUND: Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying different causal relations between biomedical entities is also critical to understand biomedical processes. Generally, natural language processing (NLP) and machine learning are used to predict specific relations between any given pair of entities using the distant supervision approach...
May 12, 2018: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29760982/biodynamic-digital-holography-of-chemoresistance-in-a-pre-clinical-trial-of-canine-b-cell-lymphoma
#19
Honggu Choi, Zhe Li, Hao Sun, Dan Merrill, John Turek, Michael Childress, David Nolte
Biodynamic digital holography was used to obtain phenotypic profiles of canine non-Hodgkin B-cell lymphoma biopsies treated with standard-of-care chemotherapy. Biodynamic signatures from the living 3D tissues were extracted using fluctuation spectroscopy from intracellular Doppler light scattering in response to the molecular mechanisms of action of therapeutic drugs that modify a range of internal cellular motions. The standard-of-care to treat B-cell lymphoma in both humans and dogs is a combination CHOP therapy that consists of doxorubicin, prednisolone, cyclophosphamide and vincristine...
May 1, 2018: Biomedical Optics Express
https://www.readbyqxmd.com/read/29756203/the-growing-role-of-machine-learning-and-artificial-intelligence-in-developmental-medicine
#20
Robert J Reynolds, Steven M Day
No abstract text is available yet for this article.
May 13, 2018: Developmental Medicine and Child Neurology
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