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“Machine Learning” “Precision Medicine”

C-A Azencott
Machine learning can have a major societal impact in computational biology applications. In particular, it plays a central role in the development of precision medicine, whereby treatment is tailored to the clinical or genetic features of the patient. However, these advances require collecting and sharing among researchers large amounts of genomic data, which generates much concern about privacy. Researchers, study participants and governing bodies should be aware of the ways in which the privacy of participants might be compromised, as well as of the large body of research on technical solutions to these issues...
September 13, 2018: Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
Len Usvyat, Lorien S Dalrymple, Franklin W Maddux
Consistent with the increase of precision medicine, the care of patients with end-stage kidney disease (ESKD) requiring maintenance dialysis therapy should evolve to become more personalized. Precise and personalized care is nuanced and informed by a number of factors including an individual's needs and preferences, disease progression, and response to and tolerance of treatments. Technology can support the delivery of more precise and personalized care through multiple mechanisms, including more accurate and real-time assessments of key care elements, enhanced treatment monitoring, and remote monitoring of home dialysis therapies...
July 2018: Seminars in Nephrology
Amir M Tahmasebi, Henghui Zhu, Gabriel Mankovich, Peter Prinsen, Prescott Klassen, Sam Pilato, Rob van Ommering, Pritesh Patel, Martin L Gunn, Paul Chang
In today's radiology workflow, free-text reporting is established as the most common medium to capture, store, and communicate clinical information. Radiologists routinely refer to prior radiology reports of a patient to recall critical information for new diagnosis, which is quite tedious, time consuming, and prone to human error. Automatic structuring of report content is desired to facilitate such inquiry of information. In this work, we propose an unsupervised machine learning approach to automatically structure radiology reports by detecting and normalizing anatomical phrases based on the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) ontology...
August 3, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
Amparo Alonso-Betanzos, Verónica Bolón-Canedo
Medicine will experience many changes in the coming years because the so-called "medicine of the future" will be increasingly proactive, featuring four basic elements: predictive, personalized, preventive, and participatory. Drivers for these changes include the digitization of data in medicine and the availability of computational tools that deal with massive volumes of data. Thus, the need to apply machine-learning methods to medicine has increased dramatically in recent years while facing challenges related to an unprecedented large number of clinically relevant features and highly specific diagnostic tests...
2018: Advances in Experimental Medicine and Biology
Shai Rosenberg, Francois Ducray, Agusti Alentorn, Caroline Dehais, Nabila Elarouci, Aurelie Kamoun, Yannick Marie, Marie-Laure Tanguy, Aurélien De Reynies, Karima Mokhtari, Dominique Figarella-Branger, Jean-Yves Delattre, Ahmed Idbaih
BACKGROUND: 1p/19q-codeleted anaplastic gliomas have variable clinical behavior. We have recently shown that the common 9p21.3 allelic loss is an independent prognostic factor in this tumor type. The aim of this study is to identify less frequent genomic copy number variations (CNVs) with clinical importance that may shed light on molecular oncogenesis of this tumor type. MATERIALS AND METHODS: A cohort of 197 patients with anaplastic oligodendroglioma was collected as part of the French POLA network...
July 17, 2018: Oncologist
Jacob Sutton, Ruhi Mahajan, Oguz Akbilgic, Rishikesan Kamaleswaran
Real-time analysis of streaming physiological data to identify earlier abnormal conditions is an important aspect of precision medicine. However, open-source systems supporting this workflow are lacking. In this paper, we present PhysOnline, a pipeline built on the open-source Apache Spark platform to ingest streaming physiological data for online feature extraction and machine learning. We consider scalability factors for horizontal deployment to support growing analysis requirements. We further integrate real-time feature extraction, including pattern recognition methods as well as descriptive statistical components to identify temporal characteristics of waveform signals...
May 2, 2018: IEEE Journal of Biomedical and Health Informatics
Rahul Kumar Sevakula, Vikas Singh, Nishchal K Verma, Chandan Kumar, Yan Cui
The emergence of deep learning has impacted numerous machine learning based applications and research. The reason for its success lies in two main advantages: 1) it provides the ability to learn very complex non-linear relationships between features and 2) it allows one to leverage information from unlabeled data that does not belong to the problem being handled. This paper presents a transfer learning procedure for cancer classification, which uses feature selection and normalization techniques in conjunction with stacked sparse auto-encoders on gene expression data...
April 4, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
R Lee, D Jarchi, R Perera, A Jones, I Cassimjee, A Handa, D A Clifton
Objective: Accurate prediction of abdominal aortic aneurysm (AAA) growth in an individual can allow personalised stratification of surveillance intervals and better inform the timing for surgery. The authors recently described the novel significant association between flow mediated dilatation (FMD) and future AAA growth. The feasibility of predicting future AAA growth was explored in individual patients using a set of benchmark machine learning techniques. Methods: The Oxford Abdominal Aortic Aneurysm Study (OxAAA) prospectively recruited AAA patients undergoing the routine NHS management pathway...
2018: EJVES Short Reports
John Kang, Tiziana Rancati, Sangkyu Lee, Jung Hun Oh, Sarah L Kerns, Jacob G Scott, Russell Schwartz, Seyoung Kim, Barry S Rosenstein
Due to the rapid increase in the availability of patient data, there is significant interest in precision medicine that could facilitate the development of a personalized treatment plan for each patient on an individual basis. Radiation oncology is particularly suited for predictive machine learning (ML) models due to the enormous amount of diagnostic data used as input and therapeutic data generated as output. An emerging field in precision radiation oncology that can take advantage of ML approaches is radiogenomics, which is the study of the impact of genomic variations on the sensitivity of normal and tumor tissue to radiation...
2018: Frontiers in Oncology
Ricardo A Calix, Ravish Gupta, Matrika Gupta, Keyuan Jiang
Health surveillance is an important task to track the happenings related to human health, and one of its areas is pharmacovigilance. Pharmacovigilance tracks and monitors safe use of pharmaceutical products. Pharmacovigilance involves tracking side effects that may be caused by medicines and other health related drugs. Medical professionals have a difficult time collecting this information. It is anticipated that social media could help to collect this data and track side effects. Twitter data can be used for this task given that users post their personal health related experiences on-line...
November 2017: Proceedings
Jaegul Choo, Shixia Liu
Recently, deep learning has been advancing the state of the art in artificial intelligence to a new level, and humans rely on artificial intelligence techniques more than ever. However, even with such unprecedented advancements, the lack of explanation regarding the decisions made by deep learning models and absence of control over their internal processes act as major drawbacks in critical decision-making processes, such as precision medicine and law enforcement. In response, efforts are being made to make deep learning interpretable and controllable by humans...
July 2018: IEEE Computer Graphics and Applications
Duc-Hau Le, Van-Huy Pham
One of the most important problem in personalized medicine research is to precisely predict the drug response for each patient. Due to relationships between drugs, recent machine learning-based methods have solved this problem using multi-task learning models. However, chemical relationships between drugs have not been considered. In addition, using very high dimensions of -omics data (e.g., genetic variant and gene expression) also limits the prediction power. A recent dual-layer network-based method was proposed to overcome these limitations by embedding gene expression features into a cell line similarity network and drug relationships in a chemical structure-based drug similarity network...
June 30, 2018: Journal of Molecular Biology
Stephen T Vernon, Thomas Hansen, Katharine A Kott, Jean Y Yang, John F O'Sullivan, Gemma A Figtree
Identification of the four standard modifiable cardiovascular risk factors (SMuRFs)-diabetes mellitus, hyperlipidaemia, hypertension, and cigarette smoking-has allowed the development of risk scores. These have been used in conjunction with primary and secondary prevention strategies targeting SMuRFs to reduce the burden of CAD. Recent studies show that up to 25% of ACS patients do not have any SMuRFs. Thus, SMuRFs do not explain the entire burden of CAD. There appears to be variation at the individual level rendering some individuals relatively susceptible or resilient to developing atherosclerosis...
June 29, 2018: Microcirculation: the Official Journal of the Microcirculatory Society, Inc
Guilherme Del Fiol, Matthew Michelson, Alfonso Iorio, Chris Cotoi, R Brian Haynes
BACKGROUND: A major barrier to the practice of evidence-based medicine is efficiently finding scientifically sound studies on a given clinical topic. OBJECTIVE: To investigate a deep learning approach to retrieve scientifically sound treatment studies from the biomedical literature. METHODS: We trained a Convolutional Neural Network using a noisy dataset of 403,216 PubMed citations with title and abstract as features. The deep learning model was compared with state-of-the-art search filters, such as PubMed's Clinical Query Broad treatment filter, McMaster's textword search strategy (no Medical Subject Heading, MeSH, terms), and Clinical Query Balanced treatment filter...
June 25, 2018: Journal of Medical Internet Research
G Langs, S Röhrich, J Hofmanninger, F Prayer, J Pan, C Herold, H Prosch
Machine learning is rapidly gaining importance in radiology. It allows for the exploitation of patterns in imaging data and in patient records for a more accurate and precise quantification, diagnosis, and prognosis. Here, we outline the basics of machine learning relevant for radiology, and review the current state of the art, the limitations, and the challenges faced as these techniques become an important building block of precision medicine. Furthermore, we discuss the roles machine learning can play in clinical routine and research and predict how it might change the field of radiology...
June 19, 2018: Der Radiologe
Bo Cao, Raymond Y Cho, Dachun Chen, Meihong Xiu, Li Wang, Jair C Soares, Xiang Yang Zhang
Identifying biomarkers in schizophrenia during the first episode without the confounding effects of treatment has been challenging. Leveraging these biomarkers to establish diagnosis and make individualized predictions of future treatment responses to antipsychotics would be of great value, but there has been limited progress. In this study, by using machine learning algorithms and the functional connections of the superior temporal cortex, we successfully identified the first-episode drug-naive (FEDN) schizophrenia patients (accuracy 78...
June 19, 2018: Molecular Psychiatry
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
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
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 capture genetic and environmental state, providing information on 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 these rich genomics data will require computational methods that support heterogeneous data, have excellent predictive performance, and ideally, provide biologically interpretable results...
June 1, 2018: Journal of Molecular Biology
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
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