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https://www.readbyqxmd.com/read/28812013/intelligent-techniques-using-molecular-data-analysis-in-leukaemia-an-opportunity-for-personalized-medicine-support-system
#1
REVIEW
Haneen Banjar, David Adelson, Fred Brown, Naeem Chaudhri
The use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient's genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis...
2017: BioMed Research International
https://www.readbyqxmd.com/read/28803840/development-and-validation-of-risk-equations-for-complications-of-type-2-diabetes-recode-using-individual-participant-data-from-randomised-trials
#2
Sanjay Basu, Jeremy B Sussman, Seth A Berkowitz, Rodney A Hayward, John S Yudkin
BACKGROUND: In view of substantial mis-estimation of risks of diabetes complications using existing equations, we sought to develop updated Risk Equations for Complications Of type 2 Diabetes (RECODe). METHODS: To develop and validate these risk equations, we used data from the Action to Control Cardiovascular Risk in Diabetes study (ACCORD, n=9635; 2001-09) and validated the equations for microvascular events using data from the Diabetes Prevention Program Outcomes Study (DPPOS, n=1018; 1996-2001), and for cardiovascular events using data from the Action for Health in Diabetes (Look AHEAD, n=4760; 2001-12)...
August 10, 2017: Lancet Diabetes & Endocrinology
https://www.readbyqxmd.com/read/28801817/contour-segmentation-of-the-intima-media-and-adventitia-layers-in-intracoronary-oct-images-application-to-fully-automatic-detection-of-healthy-wall-regions
#3
Guillaume Zahnd, Ayla Hoogendoorn, Nicolas Combaret, Antonios Karanasos, Emilie Péry, Laurent Sarry, Pascal Motreff, Wiro Niessen, Evelyn Regar, Gijs van Soest, Frank Gijsen, Theo van Walsum
PURPOSE: Quantitative and automatic analysis of intracoronary optical coherence tomography images is useful and time-saving to assess cardiovascular risk in the clinical arena. METHODS: First, the interfaces of the intima, media, and adventitia layers are segmented, by means of an original front propagation scheme, running in a 4D multi-parametric space, to simultaneously extract three non-crossing contours in the initial cross-sectional image. Second, information resulting from the tentative contours is exploited by a machine learning approach to identify healthy and diseased regions of the arterial wall...
August 11, 2017: International Journal of Computer Assisted Radiology and Surgery
https://www.readbyqxmd.com/read/28791144/machine-learning-landscapes-and-predictions-for-patient-outcomes
#4
Ritankar Das, David J Wales
The theory and computational tools developed to interpret and explore energy landscapes in molecular science are applied to the landscapes defined by local minima for neural networks. These machine learning landscapes correspond to fits of training data, where the inputs are vital signs and laboratory measurements for a database of patients, and the objective is to predict a clinical outcome. In this contribution, we test the predictions obtained by fitting to single measurements, and then to combinations of between 2 and 10 different patient medical data items...
July 2017: Royal Society Open Science
https://www.readbyqxmd.com/read/28790433/a-neural-marker-of-obsessive-compulsive-disorder-from-whole-brain-functional-connectivity
#5
Yu Takagi, Yuki Sakai, Giuseppe Lisi, Noriaki Yahata, Yoshinari Abe, Seiji Nishida, Takashi Nakamae, Jun Morimoto, Mitsuo Kawato, Jin Narumoto, Saori C Tanaka
Obsessive-compulsive disorder (OCD) is a common psychiatric disorder with a lifetime prevalence of 2-3%. Recently, brain activity in the resting state is gathering attention for exploring altered functional connectivity in psychiatric disorders. Although previous resting-state functional magnetic resonance imaging studies investigated the neurobiological abnormalities of patients with OCD, there are concerns that should be addressed. One concern is the validity of the hypothesis employed. Most studies used seed-based analysis of the fronto-striatal circuit, despite the potential for abnormalities in other regions...
August 8, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28774329/feature-selection-through-validation-and-un-censoring-of-endovascular-repair-survival-data-for-predicting-the-risk-of-re-intervention
#6
Omneya Attallah, Alan Karthikesalingam, Peter J E Holt, Matthew M Thompson, Rob Sayers, Matthew J Bown, Eddie C Choke, Xianghong Ma
BACKGROUND: Feature selection (FS) process is essential in the medical area as it reduces the effort and time needed for physicians to measure unnecessary features. Choosing useful variables is a difficult task with the presence of censoring which is the unique characteristic in survival analysis. Most survival FS methods depend on Cox's proportional hazard model; however, machine learning techniques (MLT) are preferred but not commonly used due to censoring. Techniques that have been proposed to adopt MLT to perform FS with survival data cannot be used with the high level of censoring...
August 3, 2017: BMC Medical Informatics and Decision Making
https://www.readbyqxmd.com/read/28774262/parallel-multiple-instance-learning-for-extremely-large-histopathology-image-analysis
#7
Yan Xu, Yeshu Li, Zhengyang Shen, Ziwei Wu, Teng Gao, Yubo Fan, Maode Lai, Eric I-Chao Chang
BACKGROUND: Histopathology images are critical for medical diagnosis, e.g., cancer and its treatment. A standard histopathology slice can be easily scanned at a high resolution of, say, 200,000×200,000 pixels. These high resolution images can make most existing imaging processing tools infeasible or less effective when operated on a single machine with limited memory, disk space and computing power. RESULTS: In this paper, we propose an algorithm tackling this new emerging "big data" problem utilizing parallel computing on High-Performance-Computing (HPC) clusters...
August 3, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28767689/crisprpred-a-flexible-and-efficient-tool-for-sgrnas-on-target-activity-prediction-in-crispr-cas9-systems
#8
Md Khaledur Rahman, M Sohel Rahman
The CRISPR/Cas9-sgRNA system has recently become a popular tool for genome editing and a very hot topic in the field of medical research. In this system, Cas9 protein is directed to a desired location for gene engineering and cleaves target DNA sequence which is complementary to a 20-nucleotide guide sequence found within the sgRNA. A lot of experimental efforts, ranging from in vivo selection to in silico modeling, have been made for efficient designing of sgRNAs in CRISPR/Cas9 system. In this article, we present a novel tool, called CRISPRpred, for efficient in silico prediction of sgRNAs on-target activity which is based on the applications of Support Vector Machine (SVM) model...
2017: PloS One
https://www.readbyqxmd.com/read/28764872/integration-of-data-mining-classification-techniques-and-ensemble-learning-to-identify-risk-factors-and-diagnose-ovarian-cancer-recurrence
#9
Chih-Jen Tseng, Chi-Jie Lu, Chi-Chang Chang, Gin-Den Chen, Chalong Cheewakriangkrai
Ovarian cancer is the second leading cause of deaths among gynecologic cancers in the world. Approximately 90% of women with ovarian cancer reported having symptoms long before a diagnosis was made. Literature shows that recurrence should be predicted with regard to their personal risk factors and the clinical symptoms of this devastating cancer. In this study, ensemble learning and five data mining approaches, including support vector machine (SVM), C5.0, extreme learning machine (ELM), multivariate adaptive regression splines (MARS), and random forest (RF), were integrated to rank the importance of risk factors and diagnose the recurrence of ovarian cancer...
May 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28762098/preprocessing-prediction-of-advanced-algorithms-for-medical-imaging
#10
Bella Fadida-Specktor
Advanced medical imaging algorithms (such as bone removal, vessel segmentation, or a lung nodule detection) can provide extremely valuable information to the radiologists, but they might sometimes be very time consuming. Being able to run the algorithms in advance can be a possible solution. However, we do not know which algorithm to run on a given dataset before it is actually used. It is possible to manually insert matching rules for preprocessing algorithms, but it requires high maintenance and does not work well in practice...
July 31, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28760726/triaging-patient-complaints-monte-carlo-cross-validation-of-six-machine-learning-classifiers
#11
Adel Elmessiry, William O Cooper, Thomas F Catron, Jan Karrass, Zhe Zhang, Munindar P Singh
BACKGROUND: Unsolicited patient complaints can be a useful service recovery tool for health care organizations. Some patient complaints contain information that may necessitate further action on the part of the health care organization and/or the health care professional. Current approaches depend on the manual processing of patient complaints, which can be costly, slow, and challenging in terms of scalability. OBJECTIVE: The aim of this study was to evaluate automatic patient triage, which can potentially improve response time and provide much-needed scale, thereby enhancing opportunities to encourage physicians to self-regulate...
July 31, 2017: JMIR Medical Informatics
https://www.readbyqxmd.com/read/28750904/a-comparison-of-rule-based-and-machine-learning-approaches-for-classifying-patient-portal-messages
#12
Robert M Cronin, Daniel Fabbri, Joshua C Denny, S Trent Rosenbloom, Gretchen Purcell Jackson
OBJECTIVE: Secure messaging through patient portals is an increasingly popular way that consumers interact with healthcare providers. The increasing burden of secure messaging can affect clinic staffing and workflows. Manual management of portal messages is costly and time consuming. Automated classification of portal messages could potentially expedite message triage and delivery of care. MATERIALS AND METHODS: We developed automated patient portal message classifiers with rule-based and machine learning techniques using bag of words and natural language processing (NLP) approaches...
September 2017: International Journal of Medical Informatics
https://www.readbyqxmd.com/read/28739578/applying-deep-neural-networks-to-unstructured-text-notes-in-electronic-medical-records-for-phenotyping-youth-depression
#13
Joseph Geraci, Pamela Wilansky, Vincenzo de Luca, Anvesh Roy, James L Kennedy, John Strauss
BACKGROUND: We report a study of machine learning applied to the phenotyping of psychiatric diagnosis for research recruitment in youth depression, conducted with 861 labelled electronic medical records (EMRs) documents. A model was built that could accurately identify individuals who were suitable candidates for a study on youth depression. OBJECTIVE: Our objective was a model to identify individuals who meet inclusion criteria as well as unsuitable patients who would require exclusion...
July 24, 2017: Evidence-based Mental Health
https://www.readbyqxmd.com/read/28738059/predicting-diabetes-mellitus-using-smote-and-ensemble-machine-learning-approach-the-henry-ford-exercise-testing-fit-project
#14
Manal Alghamdi, Mouaz Al-Mallah, Steven Keteyian, Clinton Brawner, Jonathan Ehrman, Sherif Sakr
Machine learning is becoming a popular and important approach in the field of medical research. In this study, we investigate the relative performance of various machine learning methods such as Decision Tree, Naïve Bayes, Logistic Regression, Logistic Model Tree and Random Forests for predicting incident diabetes using medical records of cardiorespiratory fitness. In addition, we apply different techniques to uncover potential predictors of diabetes. This FIT project study used data of 32,555 patients who are free of any known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 5-year follow-up...
2017: PloS One
https://www.readbyqxmd.com/read/28736771/on-interestingness-measures-for-mining-statistically-significant-and-novel-clinical-associations-from-emrs
#15
Orhan Abar, Richard J Charnigo, Abner Rayapati, Ramakanth Kavuluru
Association rule mining has received significant attention from both the data mining and machine learning communities. While data mining researchers focus more on designing efficient algorithms to mine rules from large datasets, the learning community has explored applications of rule mining to classification. A major problem with rule mining algorithms is the explosion of rules even for moderate sized datasets making it very difficult for end users to identify both statistically significant and potentially novel rules that could lead to interesting new insights and hypotheses...
October 2016: ACM-BCB: ACM Conference on Bioinformatics, Computational Biology and Biomedicine
https://www.readbyqxmd.com/read/28736474/phenotype-analysis-of-early-risk-factors-from-electronic-medical-records-improves-image-derived-diagnostic-classifiers-for-optic-nerve-pathology
#16
Shikha Chaganti, Kunal P Nabar, Katrina M Nelson, Louise A Mawn, Bennett A Landman
We examine imaging and electronic medical records (EMR) of 588 subjects over five major disease groups that affect optic nerve function. An objective evaluation of the role of imaging and EMR data in diagnosis of these conditions would improve understanding of these diseases and help in early intervention. We developed an automated image-processing pipeline that identifies the orbital structures within the human eyes from computed tomography (CT) scans, calculates structural size, and performs volume measurements...
February 11, 2017: Proceedings of SPIE
https://www.readbyqxmd.com/read/28730995/decoding-human-mental-states-by-whole-head-eeg-fnirs-during-category-fluency-task-performance
#17
Ahmet Omurtag, Haleh Aghajani, Hasan Onur Keles
Concurrent scalp electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), which we refer to as EEG+fNIRS, promises greater accuracy than the individual modalities while remaining nearly as convenient as EEG. We sought to quantify the hybrid system's ability to decode mental states and compare it with its unimodal components. Approach. We recorded from healthy volunteers taking the category fluency test and applied machine learning techniques to the data. Main results...
July 21, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28716018/prediction-of-extubation-readiness-in-extremely-preterm-infants-by-the-automated-analysis-of-cardiorespiratory-behavior-study-protocol
#18
Wissam Shalish, Lara J Kanbar, Smita Rao, Carlos A Robles-Rubio, Lajos Kovacs, Sanjay Chawla, Martin Keszler, Doina Precup, Karen Brown, Robert E Kearney, Guilherme M Sant'Anna
BACKGROUND: Extremely preterm infants (≤ 28 weeks gestation) commonly require endotracheal intubation and mechanical ventilation (MV) to maintain adequate oxygenation and gas exchange. Given that MV is independently associated with important adverse outcomes, efforts should be made to limit its duration. However, current methods for determining extubation readiness are inaccurate and a significant number of infants fail extubation and require reintubation, an intervention that may be associated with increased morbidities...
July 17, 2017: BMC Pediatrics
https://www.readbyqxmd.com/read/28713293/predicting-future-high-cost-schizophrenia-patients-using-high-dimensional-administrative-data
#19
Yajuan Wang, Vijay Iyengar, Jianying Hu, David Kho, Erin Falconer, John P Docherty, Gigi Y Yuen
BACKGROUND: The burden of serious and persistent mental illness such as schizophrenia is substantial and requires health-care organizations to have adequate risk adjustment models to effectively allocate their resources to managing patients who are at the greatest risk. Currently available models underestimate health-care costs for those with mental or behavioral health conditions. OBJECTIVES: The study aimed to develop and evaluate predictive models for identification of future high-cost schizophrenia patients using advanced supervised machine learning methods...
2017: Frontiers in Psychiatry
https://www.readbyqxmd.com/read/28711679/reproducibility-of-studies-on-text-mining-for-citation-screening-in-systematic-reviews-evaluation-and-checklist
#20
Babatunde Kazeem Olorisade, Pearl Brereton, Peter Andras
CONTEXT: Independent validation of published scientific results through study replication is a pre-condition for accepting the validity of such results. In computation research, full replication is often unrealistic for independent results validation, therefore, study reproduction has been justified as the minimum acceptable standard to evaluate the validity of scientific claims. The application of text mining techniques to citation screening in the context of systematic literature reviews is a relatively young and growing computational field with high relevance for software engineering, medical research and other fields...
July 12, 2017: Journal of Biomedical Informatics
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