keyword
MENU ▼
Read by QxMD icon Read
search

machine learning medicine

keyword
https://www.readbyqxmd.com/read/28285459/a-critical-review-for-developing-accurate-and-dynamic-predictive-models-using-machine-learning-methods-in-medicine-and-health-care
#1
Hamdan O Alanazi, Abdul Hanan Abdullah, Kashif Naseer Qureshi
Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients' diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed...
April 2017: Journal of Medical Systems
https://www.readbyqxmd.com/read/28272367/how-open-data-shapes-in-silico-transporter-modeling
#2
REVIEW
Floriane Montanari, Barbara Zdrazil
Chemical compound bioactivity and related data are nowadays easily available from open data sources and the open medicinal chemistry literature for many transmembrane proteins. Computational ligand-based modeling of transporters has therefore experienced a shift from local (quantitative) models to more global, qualitative, predictive models. As the size and heterogeneity of the data set rises, careful data curation becomes even more important. This includes, for example, not only a tailored cutoff setting for the generation of binary classes, but also the proper assessment of the applicability domain...
March 7, 2017: Molecules: a Journal of Synthetic Chemistry and Natural Product Chemistry
https://www.readbyqxmd.com/read/28268820/towards-sophisticated-learning-from-ehrs-increasing-prediction-specificity-and-accuracy-using-clinically-meaningful-risk-criteria
#3
Ieva Vasiljeva, Ognjen Arandjelovic
Computer based analysis of Electronic Health Records (EHRs) has the potential to provide major novel insights of benefit both to specific individuals in the context of personalized medicine, as well as on the level of population-wide health care and policy. The present paper introduces a novel algorithm that uses machine learning for the discovery of longitudinal patterns in the diagnoses of diseases. Two key technical novelties are introduced: one in the form of a novel learning paradigm which enables greater learning specificity, and another in the form of a risk driven identification of confounding diagnoses...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28263937/identifying-drug-drug-interactions-by-data-mining-a-pilot-study-of-warfarin-associated-drug-interactions
#4
Peter Wæde Hansen, Line Clemmensen, Thomas S G Sehested, Emil Loldrup Fosbøl, Christian Torp-Pedersen, Lars Køber, Gunnar H Gislason, Charlotte Andersson
BACKGROUND: Knowledge about drug-drug interactions commonly arises from preclinical trials, from adverse drug reports, or based on knowledge of mechanisms of action. Our aim was to investigate whether drug-drug interactions were discoverable without prior hypotheses using data mining. We focused on warfarin-drug interactions as the prototype. METHODS AND RESULTS: We analyzed altered prothrombin time (measured as international normalized ratio [INR]) after initiation of a novel prescription in previously INR-stable warfarin-treated patients with nonvalvular atrial fibrillation...
November 2016: Circulation. Cardiovascular Quality and Outcomes
https://www.readbyqxmd.com/read/28259405/novel-approaches-to-assess-the-quality-of-fertility-data-stored-in-dairy-herd-management-software
#5
K Hermans, W Waegeman, G Opsomer, B Van Ranst, J De Koster, M Van Eetvelde, M Hostens
Scientific journals and popular press magazines are littered with articles in which the authors use data from dairy herd management software. Almost none of such papers include data cleaning and data quality assessment in their study design despite this being a very critical step during data mining. This paper presents 2 novel data cleaning methods that permit identification of animals with good and bad data quality. The first method is a deterministic or rule-based data cleaning method. Reproduction and mutation or life-changing events such as birth and death were converted to a symbolic (alphabetical letter) representation and split into triplets (3-letter code)...
March 2, 2017: Journal of Dairy Science
https://www.readbyqxmd.com/read/28257413/uncovering-precision-phenotype-biomarker-associations-in-traumatic-brain-injury-using-topological-data-analysis
#6
Jessica L Nielson, Shelly R Cooper, John K Yue, Marco D Sorani, Tomoo Inoue, Esther L Yuh, Pratik Mukherjee, Tanya C Petrossian, Jesse Paquette, Pek Y Lum, Gunnar E Carlsson, Mary J Vassar, Hester F Lingsma, Wayne A Gordon, Alex B Valadka, David O Okonkwo, Geoffrey T Manley, Adam R Ferguson
BACKGROUND: Traumatic brain injury (TBI) is a complex disorder that is traditionally stratified based on clinical signs and symptoms. Recent imaging and molecular biomarker innovations provide unprecedented opportunities for improved TBI precision medicine, incorporating patho-anatomical and molecular mechanisms. Complete integration of these diverse data for TBI diagnosis and patient stratification remains an unmet challenge. METHODS AND FINDINGS: The Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot multicenter study enrolled 586 acute TBI patients and collected diverse common data elements (TBI-CDEs) across the study population, including imaging, genetics, and clinical outcomes...
2017: PloS One
https://www.readbyqxmd.com/read/28256072/perceived-social-support-in-adults-with-autism-spectrum-disorder-and-attention-deficit-hyperactivity-disorder
#7
Sonia Alvarez-Fernandez, Hallie R Brown, Yihong Zhao, Jessica A Raithel, Somer L Bishop, Sarah B Kern, Catherine Lord, Eva Petkova, Adriana Di Martino
Perceived social support (PSS) has been related to physical and mental well-being in typically developing individuals, but systematic characterizations of PSS in autism spectrum disorder (ASD) are limited. We compared self-report ratings of the multidimensional scale of PSS (MSPSS) among age- and IQ-matched groups of adults (18-58 years) with cognitively high-functioning ASD (N = 41), or attention-deficit/hyperactivity disorder (ADHD; N = 69), and neurotypical controls (NC; N = 69). Accompanying group comparisons, we used machine learning random forest (RF) analyses to explore predictors among a range of psychopathological and socio-emotional variables...
March 3, 2017: Autism Research: Official Journal of the International Society for Autism Research
https://www.readbyqxmd.com/read/28241858/machine-learning-identifies-a-compact-gene-set-for-monitoring-the-circadian-clock-in-human-blood
#8
Jacob J Hughey
BACKGROUND: The circadian clock and the daily rhythms it produces are crucial for human health, but are often disrupted by the modern environment. At the same time, circadian rhythms may influence the efficacy and toxicity of therapeutics and the metabolic response to food intake. Developing treatments for circadian dysfunction, as well as optimizing the daily timing of treatments for other health conditions, will require a simple and accurate method to monitor the molecular state of the circadian clock...
February 28, 2017: Genome Medicine
https://www.readbyqxmd.com/read/28227016/towards-sophisticated-learning-from-ehrs-increasing-prediction-specificity-and-accuracy-using-clinically-meaningful-risk-criteria
#9
Ieva Vasiljeva, Ognjen Arandjelovic, Ieva Vasiljeva, Ognjen Arandjelovic, Ieva Vasiljeva, Ognjen Arandjelovic
Computer based analysis of Electronic Health Records (EHRs) has the potential to provide major novel insights of benefit both to specific individuals in the context of personalized medicine, as well as on the level of population-wide health care and policy. The present paper introduces a novel algorithm that uses machine learning for the discovery of longitudinal patterns in the diagnoses of diseases. Two key technical novelties are introduced: one in the form of a novel learning paradigm which enables greater learning specificity, and another in the form of a risk driven identification of confounding diagnoses...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28222333/immunoprofiling-as-a-predictor-of-patient-s-response-to-cancer-therapy-promises-and-challenges
#10
REVIEW
Daniel Bethmann, Zipei Feng, Bernard A Fox
Immune cell infiltration is common to many tumors and has been recognized by pathologists for more than 100 years. The application of digital imaging and objective assessment software allowed a concise determination of the type and quantity of immune cells and their location relative to the tumor and, in the case of colon cancer, characterized overall survival better than AJCC TNM staging. Subsequently, expression of PD-L1, by 50% or more tumor cells, identified NSCLC patients with double the response rate to anti-PD-1...
February 18, 2017: Current Opinion in Immunology
https://www.readbyqxmd.com/read/28176850/application-of-machine-learning-models-to-predict-tacrolimus-stable-dose-in-renal-transplant-recipients
#11
Jie Tang, Rong Liu, Yue-Li Zhang, Mou-Ze Liu, Yong-Fang Hu, Ming-Jie Shao, Li-Jun Zhu, Hua-Wen Xin, Gui-Wen Feng, Wen-Jun Shang, Xiang-Guang Meng, Li-Rong Zhang, Ying-Zi Ming, Wei Zhang
Tacrolimus has a narrow therapeutic window and considerable variability in clinical use. Our goal was to compare the performance of multiple linear regression (MLR) and eight machine learning techniques in pharmacogenetic algorithm-based prediction of tacrolimus stable dose (TSD) in a large Chinese cohort. A total of 1,045 renal transplant patients were recruited, 80% of which were randomly selected as the "derivation cohort" to develop dose-prediction algorithm, while the remaining 20% constituted the "validation cohort" to test the final selected algorithm...
February 8, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28166733/a-machine-learning-classifier-trained-on-cancer-transcriptomes-detects-nf1-inactivation-signal-in-glioblastoma
#12
Gregory P Way, Robert J Allaway, Stephanie J Bouley, Camilo E Fadul, Yolanda Sanchez, Casey S Greene
BACKGROUND: We have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 (NF1) tumor suppressor gene. However, recognizing tumors that have inactivation of the NF1 tumor suppressor function is challenging because the loss may occur via mechanisms that do not involve mutation of the genomic locus. Degradation of the NF1 protein, independent of NF1 mutation status, phenocopies inactivating mutations to drive tumors in human glioma cell lines. NF1 inactivation may alter the transcriptional landscape of a tumor and allow a machine learning classifier to detect which tumors will benefit from synthetic lethal molecules...
February 6, 2017: BMC Genomics
https://www.readbyqxmd.com/read/28154050/beyond-prediction-using-big-data-for-policy-problems
#13
REVIEW
Susan Athey
Machine-learning prediction methods have been extremely productive in applications ranging from medicine to allocating fire and health inspectors in cities. However, there are a number of gaps between making a prediction and making a decision, and underlying assumptions need to be understood in order to optimize data-driven decision-making.
February 3, 2017: Science
https://www.readbyqxmd.com/read/28153954/hot-topics-will-machine-learning-change-medicine
#14
Michael Forsting
No abstract text is available yet for this article.
February 2, 2017: Journal of Nuclear Medicine: Official Publication, Society of Nuclear Medicine
https://www.readbyqxmd.com/read/28137222/machine-learning-and-molecular-dynamics-based-insights-into-mode-of-actions-of-insulin-degrading-enzyme-modulators
#15
Salma Jamal, Sukriti Goyal, Asheesh Shanker, Abhinav Grover
BACKGROUND: Alzheimer's disease (AD) is one of the most common lethal neurodegenerative disorders having impact on the lives of millions of people worldwide. The disease lacks effective treatment options and the unavailability of the drugs to cure the disease necessitates the development of effectual anti-Alzheimer drugs. Several mechanisms have been reported underlying the association of the two disorders, diabetes and dementia, one among which is the insulin-degrading enzyme (IDE) which is known to degrade insulin as well beta-amyloid peptides...
January 30, 2017: Combinatorial Chemistry & High Throughput Screening
https://www.readbyqxmd.com/read/28128933/computational-sensing-using-low-cost-and-mobile-plasmonic-readers-designed-by-machine-learning
#16
Zachary S Ballard, Daniel Shir, Aashish Bhardwaj, Sarah Bazargan, Shyama Sathianathan, Aydogan Ozcan
Plasmonic sensors have been used for a wide range of biological and chemical sensing applications. Emerging nanofabrication techniques have enabled these sensors to be cost-effectively mass manufactured onto various types of substrates. To accompany these advances, major improvements in sensor read-out devices must also be achieved to fully realize the broad impact of plasmonic nanosensors. Here, we propose a machine learning framework which can be used to design low-cost and mobile multispectral plasmonic readers that do not use traditionally employed bulky and expensive stabilized light sources or high-resolution spectrometers...
February 28, 2017: ACS Nano
https://www.readbyqxmd.com/read/28128301/discovering-novel-phenotypes-with-automatically-inferred-dynamic-models-a-partial-melanocyte-conversion-in-xenopus
#17
Daniel Lobo, Maria Lobikin, Michael Levin
Progress in regenerative medicine requires reverse-engineering cellular control networks to infer perturbations with desired systems-level outcomes. Such dynamic models allow phenotypic predictions for novel perturbations to be rapidly assessed in silico. Here, we analyzed a Xenopus model of conversion of melanocytes to a metastatic-like phenotype only previously observed in an all-or-none manner. Prior in vivo genetic and pharmacological experiments showed that individual animals either fully convert or remain normal, at some characteristic frequency after a given perturbation...
January 27, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28127429/machine-learning-and-systems-genomics-approaches-for-multi-omics-data
#18
REVIEW
Eugene Lin, Hsien-Yuan Lane
In light of recent advances in biomedical computing, big data science, and precision medicine, there is a mammoth demand for establishing algorithms in machine learning and systems genomics (MLSG), together with multi-omics data, to weigh probable phenotype-genotype relationships. Software frameworks in MLSG are extensively employed to analyze hundreds of thousands of multi-omics data by high-throughput technologies. In this study, we reviewed the MLSG software frameworks and future directions with respect to multi-omics data analysis and integration...
2017: Biomarker Research
https://www.readbyqxmd.com/read/28126242/artificial-intelligence-in-medicine
#19
Pavel Hamet, Johanne Tremblay
Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech word robota, meaning biosynthetic machines used as forced labor. In this field, Leonardo Da Vinci's lasting heritage is today's burgeoning use of robotic-assisted surgery, named after him, for complex urologic and gynecologic procedures. Da Vinci's sketchbooks of robots helped set the stage for this innovation...
April 2017: Metabolism: Clinical and Experimental
https://www.readbyqxmd.com/read/28125523/machine-learning-based-classification-of-38-years-of-spine-related-literature-into-100-research-topics
#20
David C Sing, Lionel N Metz, Stefan Dudli
STUDY DESIGN: Retrospective review. OBJECTIVE: To identify the top 100 spine research topics. SUMMARY OF BACKGROUND DATA: Recent advances in "machine learning," or computers learning without explicit instructions, have yielded broad technological advances. Topic modeling algorithms can be applied to large volumes of text to discover quantifiable themes and trends. METHODS: Abstracts were extracted from the National Library of Medicine PubMed database from five prominent peer-reviewed spine journals (European Spine Journal [ESJ], The Spine Journal [SpineJ], Spine, Journal of Spinal Disorders and Techniques [JSDT], Journal of Neurosurgery: Spine [JNS])...
January 25, 2017: Spine
keyword
keyword
120558
1
2
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"