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https://www.readbyqxmd.com/read/29618526/opportunities-and-obstacles-for-deep-learning-in-biology-and-medicine
#1
REVIEW
Travers Ching, Daniel S Himmelstein, Brett K Beaulieu-Jones, Alexandr A Kalinin, Brian T Do, Gregory P Way, Enrico Ferrero, Paul-Michael Agapow, Michael Zietz, Michael M Hoffman, Wei Xie, Gail L Rosen, Benjamin J Lengerich, Johnny Israeli, Jack Lanchantin, Stephen Woloszynek, Anne E Carpenter, Avanti Shrikumar, Jinbo Xu, Evan M Cofer, Christopher A Lavender, Srinivas C Turaga, Amr M Alexandari, Zhiyong Lu, David J Harris, Dave DeCaprio, Yanjun Qi, Anshul Kundaje, Yifan Peng, Laura K Wiley, Marwin H S Segler, Simina M Boca, S Joshua Swamidass, Austin Huang, Anthony Gitter, Casey S Greene
Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges...
April 2018: Journal of the Royal Society, Interface
https://www.readbyqxmd.com/read/29600766/discovering-synergistic-drug-combination-from-a-computational-perspective
#2
Pingjian Ding, Jiawei Luo, Cheng Liang, Qiu Xiao, Buwen Cao, Guanghui Li
Synergistic drug combinations play an important role in the treatment of complex diseases. The identification of effective drug combination is vital to further reduce the side effects and improve therapeutic efficiency. In previous years, in vitro method has been the main route to discover synergistic drug combinations. However, many limitations of time and resource consumption lie within the in vitro method. Therefore, with the rapid development of computational models and the explosive growth of large and phenotypic data, computational methods for discovering synergistic drug combinations are an efficient and promising tool and contribute to precision medicine...
March 30, 2018: Current Topics in Medicinal Chemistry
https://www.readbyqxmd.com/read/29596342/statistical-platform-for-individualized-behavioral-analyses-using-biophysical-micro-movement-spikes
#3
Elizabeth B Torres, Joe Vero, Richa Rai
Wearable biosensors, such as those embedded in smart phones, can provide data to assess neuro-motor control in mobile settings, at homes, schools, workplaces and clinics. However, because most machine learning algorithms currently used to analyze such data require several steps that depend on human heuristics, the analyses become computationally expensive and rather subjective. Further, there is no standardized scale or set of tasks amenable to take advantage of such technology in ways that permit broad dissemination and reproducibility of results...
March 29, 2018: Sensors
https://www.readbyqxmd.com/read/29594204/infrastructure-and-distributed-learning-methodology-for-privacy-preserving-multi-centric-rapid-learning-health-care-eurocat
#4
Timo M Deist, A Jochems, Johan van Soest, Georgi Nalbantov, Cary Oberije, Seán Walsh, Michael Eble, Paul Bulens, Philippe Coucke, Wim Dries, Andre Dekker, Philippe Lambin
Machine learning applications for personalized medicine are highly dependent on access to sufficient data. For personalized radiation oncology, datasets representing the variation in the entire cancer patient population need to be acquired and used to learn prediction models. Ethical and legal boundaries to ensure data privacy hamper collaboration between research institutes. We hypothesize that data sharing is possible without identifiable patient data leaving the radiation clinics and that building machine learning applications on distributed datasets is feasible...
June 2017: Clinical and Translational Radiation Oncology
https://www.readbyqxmd.com/read/29594137/artificial-intelligence-for-the-artificial-kidney-pointers-to-the-future-of-a-personalized-hemodialysis-therapy
#5
REVIEW
Miguel Hueso, Alfredo Vellido, Nuria Montero, Carlo Barbieri, Rosa Ramos, Manuel Angoso, Josep Maria Cruzado, Anders Jonsson
Background: Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve the patient's quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters, and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for the assessment of their adequacy...
February 2018: Kidney Diseases
https://www.readbyqxmd.com/read/29582949/-neuropsychiatry-and-computational-medicine
#6
REVIEW
Shahar Arzy
The classical model of medicine is based on, first, history taking, followed by physical examination, data analysis by the clinician and their further validation using biological tests. Based on this, the clinician may plan the medical treatment. In neuropsychiatry, this model is even more limited as physical examination is based mostly on a patient-doctor conversation, and biological or imaging tests are directed mostly to extract the structural basis for the clinical manifestations. The rapidly developing computational revolution have not yet significantly influenced this model...
March 2018: Harefuah
https://www.readbyqxmd.com/read/29572496/optical-detection-of-degraded-therapeutic-proteins
#7
William F Herrington, Gajendra P Singh, Di Wu, Paul W Barone, William Hancock, Rajeev J Ram
The quality of therapeutic proteins such as hormones, subunit and conjugate vaccines, and antibodies is critical to the safety and efficacy of modern medicine. Identifying malformed proteins at the point-of-care can prevent adverse immune reactions in patients; this is of special concern when there is an insecure supply chain resulting in the delivery of degraded, or even counterfeit, drug product. Identification of degraded protein, for example human growth hormone, is demonstrated by applying automated anomaly detection algorithms...
March 23, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29570167/advanced-morphologic-analysis-for-diagnosing-allograft-rejection-the-case-of-cardiac-transplant-rejection
#8
Eliot G Peyster, Anant Madabhushi, Kenneth B Margulies
Allograft rejection remains a significant concern following all solid organ transplants. While qualitative morphologic analysis with histologic grading of biopsy samples is the main tool employed, for diagnosing allograft rejection, this standard has significant limitations in precision and accuracy that affect patient care. The use of endomyocardial biopsy (EMB) to diagnose cardiac allograft rejection (CAR) illustrates the significant shortcomings of current approaches for diagnosing allograft rejection. Despite disappointing interobserver variability, concerns about discordance with clinical trajectories, attempts at revising the histologic criteria and efforts to establish new diagnostic tools with imaging and gene expression profiling, no method has yet supplanted EMB as the diagnostic gold standard...
March 22, 2018: Transplantation
https://www.readbyqxmd.com/read/29568746/metagenomics-biomarkers-selected-for-prediction-of-three-different-diseases-in-chinese-population
#9
Honglong Wu, Lihua Cai, Dongfang Li, Xinying Wang, Shancen Zhao, Fuhao Zou, Ke Zhou
The dysbiosis of human microbiome has been proven to be associated with the development of many human diseases. Metagenome sequencing emerges as a powerful tool to investigate the effects of microbiome on diseases. Identification of human gut microbiome markers associated with abnormal phenotypes may facilitate feature selection for multiclass classification. Compared with binary classifiers, multiclass classification models deploy more complex discriminative patterns. Here, we developed a pipeline to address the challenging characterization of multilabel samples...
2018: BioMed Research International
https://www.readbyqxmd.com/read/29567042/face-and-content-validity-of-variables-associated-with-the-difficult-to-sedate-child-in-the-paediatric-intensive-care-unit-a-survey-of-paediatric-critical-care-clinicians
#10
Ruth M Lebet, Lisa A Asaro, Athena F Zuppa, Martha A Q Curley
BACKGROUND: Clinicians recognise that some critically ill children are difficult-to-sedate. It may be possible to identify this clinical phenotype for sedation response using statistical modelling techniques adopted from machine learning. This requires identification of a finite number of variables to include in the statistical model. OBJECTIVE: To establish face and content validity for 17 candidate variables identified in the international literature as characteristic of the difficult-to-sedate child phenotype...
March 19, 2018: Australian Critical Care: Official Journal of the Confederation of Australian Critical Care Nurses
https://www.readbyqxmd.com/read/29545756/e-addictology-an-overview-of-new-technologies-for-assessing-and-intervening-in-addictive-behaviors
#11
REVIEW
Florian Ferreri, Alexis Bourla, Stephane Mouchabac, Laurent Karila
Background: New technologies can profoundly change the way we understand psychiatric pathologies and addictive disorders. New concepts are emerging with the development of more accurate means of collecting live data, computerized questionnaires, and the use of passive data. Digital phenotyping , a paradigmatic example, refers to the use of computerized measurement tools to capture the characteristics of different psychiatric disorders. Similarly, machine learning-a form of artificial intelligence-can improve the classification of patients based on patterns that clinicians have not always considered in the past...
2018: Frontiers in Psychiatry
https://www.readbyqxmd.com/read/29538756/machine-learning-for-predictive-analytics-in-medicine-real-opportunity-or-overblown-hype
#12
Cedric Manlhiot
No abstract text is available yet for this article.
March 12, 2018: European Heart Journal Cardiovascular Imaging
https://www.readbyqxmd.com/read/29538103/harnessing-the-power-of-big-data-to-improve-graduate-medical-education-big-idea-or-bust
#13
Vineet M Arora
With the advent of electronic medical records (EMRs) fueling the rise of big data, the use of predictive analytics, machine learning, and artificial intelligence are touted as transformational tools to improve clinical care. While major investments are being made in using big data to transform health care delivery, little effort has been directed toward exploiting big data to improve graduate medical education (GME). Because our current system relies on faculty observations of competence, it is not unreasonable to ask whether big data in the form of clinical EMRs and other novel data sources can answer questions of importance in GME such as when is a resident ready for independent practice...
March 13, 2018: Academic Medicine: Journal of the Association of American Medical Colleges
https://www.readbyqxmd.com/read/29531073/predicting-cancer-outcomes-from-histology-and-genomics-using-convolutional-networks
#14
Pooya Mobadersany, Safoora Yousefi, Mohamed Amgad, David A Gutman, Jill S Barnholtz-Sloan, José E Velázquez Vega, Daniel J Brat, Lee A D Cooper
Cancer histology reflects underlying molecular processes and disease progression and contains rich phenotypic information that is predictive of patient outcomes. In this study, we show a computational approach for learning patient outcomes from digital pathology images using deep learning to combine the power of adaptive machine learning algorithms with traditional survival models. We illustrate how these survival convolutional neural networks (SCNNs) can integrate information from both histology images and genomic biomarkers into a single unified framework to predict time-to-event outcomes and show prediction accuracy that surpasses the current clinical paradigm for predicting the overall survival of patients diagnosed with glioma...
March 12, 2018: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/29527298/response-heterogeneity-challenges-for-personalised-medicine-and-big-data-approaches-in-psychiatry-and-chronic-pain
#15
Agnes Norbury, Ben Seymour
Response rates to available treatments for psychological and chronic pain disorders are poor, and there is a considerable burden of suffering and disability for patients, who often cycle through several rounds of ineffective treatment. As individuals presenting to the clinic with symptoms of these disorders are likely to be heterogeneous, there is considerable interest in the possibility that different constellations of signs could be used to identify subgroups of patients that might preferentially benefit from particular kinds of treatment...
2018: F1000Research
https://www.readbyqxmd.com/read/29500024/behind-the-scenes-a-medical-natural-language-processing-project
#16
Joy T Wu, Franck Dernoncourt, Sebastian Gehrmann, Patrick D Tyler, Edward T Moseley, Eric T Carlson, David W Grant, Yeran Li, Jonathan Welt, Leo Anthony Celi
Advancement of Artificial Intelligence (AI) capabilities in medicine can help address many pressing problems in healthcare. However, AI research endeavors in healthcare may not be clinically relevant, may have unrealistic expectations, or may not be explicit enough about their limitations. A diverse and well-functioning multidisciplinary team (MDT) can help identify appropriate and achievable AI research agendas in healthcare, and advance medical AI technologies by developing AI algorithms as well as addressing the shortage of appropriately labeled datasets for machine learning...
April 2018: International Journal of Medical Informatics
https://www.readbyqxmd.com/read/29497586/advanced-metrics-for-assessing-holistic-care-the-epidaurus-2-project
#17
Frederick O Foote, Herbert Benson, Ann Berger, Brian Berman, James DeLeo, Patricia A Deuster, David J Lary, Marni N Silverman, Esther M Sternberg
In response to the challenge of military traumatic brain injury and posttraumatic stress disorder, the US military developed a wide range of holistic care modalities at the new Walter Reed National Military Medical Center, Bethesda, MD, from 2001 to 2017, guided by civilian expert consultation via the Epidaurus Project. These projects spanned a range from healing buildings to wellness initiatives and healing through nature, spirituality, and the arts. The next challenge was to develop whole-body metrics to guide the use of these therapies in clinical care...
2018: Global Advances in Health and Medicine: Improving Healthcare Outcomes Worldwide
https://www.readbyqxmd.com/read/29497285/review-of-statistical-learning-methods-in-integrated-omics-studies-an-integrated-information-science
#18
REVIEW
Irene Sui Lan Zeng, Thomas Lumley
Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework...
2018: Bioinformatics and Biology Insights
https://www.readbyqxmd.com/read/29492605/machine-learning-for-medical-ultrasound-status-methods-and-future-opportunities
#19
Laura J Brattain, Brian A Telfer, Manish Dhyani, Joseph R Grajo, Anthony E Samir
Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited image quality control. Tremendous opportunities have arisen in the last decade as a result of exponential growth in available computational power coupled with progressive miniaturization of US devices...
February 28, 2018: Abdominal Radiology
https://www.readbyqxmd.com/read/29492098/development-of-models-for-classification-of-action-between-heat-clearing-herbs-and-blood-activating-stasis-resolving-herbs-based-on-theory-of-traditional-chinese-medicine
#20
Zhao Chen, Yanfeng Cao, Shuaibing He, Yanjiang Qiao
Background: Action (" gongxiao " in Chinese) of traditional Chinese medicine (TCM) is the high recapitulation for therapeutic and health-preserving effects under the guidance of TCM theory. TCM-defined herbal properties (" yaoxing " in Chinese) had been used in this research. TCM herbal property (TCM-HP) is the high generalization and summary for actions, both of which come from long-term effective clinical practice in two thousands of years in China. However, the specific relationship between TCM-HP and action of TCM is complex and unclear from a scientific perspective...
2018: Chinese Medicine
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