Artif Intell Med - A machine learning-based approach to prognostic analysis of thoracic transplantations.


{ model(2656) set(1616) predict(1553) }
{ studi(1410) differ(1259) use(1210) }
{ model(2341) predict(2261) use(1141) }
{ studi(1119) effect(1106) posit(819) }
{ clinic(1479) use(1117) guidelin(835) }
{ import(1318) role(1303) understand(862) }
{ cancer(2502) breast(956) screen(824) }
{ risk(3053) factor(974) diseas(938) }
{ learn(2355) train(1041) set(1003) }
{ group(2977) signific(1463) compar(1072) }
{ data(1737) use(1416) pattern(1282) }
{ research(1085) discuss(1038) issu(1018) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ medic(1828) order(1363) alert(1069) }
{ network(2748) neural(1063) input(814) }
{ system(1050) medic(1026) inform(1018) }
{ estim(2440) model(1874) function(577) }
{ detect(2391) sensit(1101) algorithm(908) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ method(1969) cluster(1462) data(1082) }
{ imag(1947) propos(1133) code(1026) }
{ studi(2440) review(1878) systemat(933) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
{ concept(1167) ontolog(924) domain(897) }
{ howev(809) still(633) remain(590) }
{ patient(1821) servic(1111) care(1106) }
{ structur(1116) can(940) graph(676) }
{ can(774) often(719) complex(702) }
{ featur(3375) classif(2383) classifi(1994) }
{ extract(1171) text(1153) clinic(932) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ search(2224) databas(1162) retriev(909) }
{ perform(999) metric(946) measur(919) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ signal(2180) analysi(812) frequenc(800) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ time(1939) patient(1703) rate(768) }
{ analysi(2126) use(1163) compon(1037) }
{ high(1669) rate(1365) level(1280) }
{ result(1111) use(1088) new(759) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ model(3404) distribut(989) bayesian(671) }
{ inform(2794) health(2639) internet(1427) }
{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ imag(2830) propos(1344) filter(1198) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(1557) propos(1049) approach(1037) }
{ data(1714) softwar(1251) tool(1186) }
{ design(1359) user(1324) use(1319) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ data(3963) clinic(1234) research(1004) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ cost(1906) reduc(1198) effect(832) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ implement(1333) system(1263) develop(1122) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }


JECTIVE: The prediction of survival time after organ transplantations and prognosis analysis of different risk groups of transplant patients are not only clinically important but also technically challenging. The current studies, which are mostly linear modeling-based statistical analyses, have focused on small sets of disparate predictive factors where many potentially important variables are neglected in their analyses. Data mining methods, such as machine learning-based approaches, are capable of providing an effective way of overcoming these limitations by utilizing sufficiently large data sets with many predictive factors to identify not only linear associations but also highly complex, non-linear relationships. Therefore, this study is aimed at exploring risk groups of thoracic recipients through machine learning-based methods.METHODS AND MATERIAL: A large, feature-rich, nation-wide thoracic transplantation dataset (obtained from the United Network for Organ Sharing-UNOS) is used to develop predictive models for the survival time estimation. The predictive factors that are most relevant to the survival time identified via, (1) conducting sensitivity analysis on models developed by the machine learning methods, (2) extraction of variables from the published literature, and (3) eliciting variables from the medical experts and other domain specific knowledge bases. A unified set of predictors is then used to develop a Cox regression model and the related prognosis indices. A comparison of clustering algorithm-based and conventional risk grouping techniques is conducted based on the outcome of the Cox regression model in order to identify optimal number of risk groups of thoracic recipients. Finally, the Kaplan-Meier survival analysis is performed to validate the discrimination among the identified various risk groups.RESULTS: The machine learning models performed very effectively in predicting the survival time: the support vector machine model with a radial basis Kernel function produced the best fit with an R(2) value of 0.879, the artificial neural network (multilayer perceptron-MLP-model) came the second with an R(2) value of 0.847, and the M5 algorithm-based regression tree model came last with an R(2) value of 0.785. Following the proposed method, a consolidated set of predictive variables are determined and used to build the Cox survival model. Using the prognosis indices revealed by the Cox survival model along with a k-means clustering algorithm, an optimal number of "three" risk groups is identified. The significance of differences among these risk groups are also validated using the Kaplan-Meier survival analysis.CONCLUSIONS: This study demonstrated that the integrated machine learning method to select the predictor variables is more effective in developing the Cox survival models than the traditional methods commonly found in the literature. The significant distinction among the risk groups of thoracic patients also validates the effectiveness of the methodology proposed herein. We anticipate that this study (and other AI based analytic studies like this one) will lead to more effective analyses of thoracic transplant procedures to better understand the prognosis of thoracic organ recipients. It would potentially lead to new medical and biological advances and more effective allocation policies in the field of organ transplantation.

Resumo Limpo

jectiv predict surviv time organ transplant prognosi analysi differ risk group transplant patient clinic import also technic challeng current studi most linear modelingbas statist analys focus small set dispar predict factor mani potenti import variabl neglect analys data mine method machin learningbas approach capabl provid effect way overcom limit util suffici larg data set mani predict factor identifi linear associ also high complex nonlinear relationship therefor studi aim explor risk group thorac recipi machin learningbas methodsmethod materi larg featurerich nationwid thorac transplant dataset obtain unit network organ sharinguno use develop predict model surviv time estim predict factor relev surviv time identifi via conduct sensit analysi model develop machin learn method extract variabl publish literatur elicit variabl medic expert domain specif knowledg base unifi set predictor use develop cox regress model relat prognosi indic comparison cluster algorithmbas convent risk group techniqu conduct base outcom cox regress model order identifi optim number risk group thorac recipi final kaplanmei surviv analysi perform valid discrimin among identifi various risk groupsresult machin learn model perform effect predict surviv time support vector machin model radial basi kernel function produc best fit r valu artifici neural network multilay perceptronmlpmodel came second r valu m algorithmbas regress tree model came last r valu follow propos method consolid set predict variabl determin use build cox surviv model use prognosi indic reveal cox surviv model along kmean cluster algorithm optim number three risk group identifi signific differ among risk group also valid use kaplanmei surviv analysisconclus studi demonstr integr machin learn method select predictor variabl effect develop cox surviv model tradit method common found literatur signific distinct among risk group thorac patient also valid effect methodolog propos herein anticip studi ai base analyt studi like one will lead effect analys thorac transplant procedur better understand prognosi thorac organ recipi potenti lead new medic biolog advanc effect alloc polici field organ transplant

Resumos Similares

BMC Med Inform Decis Mak - Measuring preferences for analgesic treatment for cancer pain: how do African-Americans and Whites perform on choice-based conjoint (CBC) analysis experiments? ( 0,678261004923487 )
J Biomed Inform - MysiRNA: improving siRNA efficacy prediction using a machine-learning model combining multi-tools and whole stacking energy (G). ( 0,67083066114476 )
BMC Med Inform Decis Mak - Bayesian predictors of very poor health related quality of life and mortality in patients with COPD. ( 0,659853407358987 )
Artif Intell Med - Training artificial neural networks directly on the concordance index for censored data using genetic algorithms. ( 0,639192306230024 )
AMIA Annu Symp Proc - Motivating the additional use of external validity: examining transportability in a model of glioblastoma multiforme. ( 0,633564437343183 )
J Chem Inf Model - Study of chromatographic retention of natural terpenoids by chemoinformatic tools. ( 0,631833446449752 )
J Chem Inf Model - Does rational selection of training and test sets improve the outcome of QSAR modeling? ( 0,627133257057545 )
BMC Med Inform Decis Mak - Concordance and predictive value of two adverse drug event data sets. ( 0,622287965993957 )
AMIA Annu Symp Proc - Effect of data combination on predictive modeling: a study using gene expression data. ( 0,620922644984292 )
Comput Methods Programs Biomed - Kinetic modelling of haemodialysis removal of myoglobin in rhabdomyolysis patients. ( 0,598365352871732 )
Int J Comput Assist Radiol Surg - Assessing performance in brain tumor resection using a novel virtual reality simulator. ( 0,597786571336824 )
J. Med. Internet Res. - A case study of the New York City 2012-2013 influenza season with daily geocoded Twitter data from temporal and spatiotemporal perspectives. ( 0,597208661108041 )
J Chem Inf Model - In silico prediction of total human plasma clearance. ( 0,594623815334418 )
Med Decis Making - Performance of a mathematical model to forecast lives saved from HIV treatment expansion in resource-limited settings. ( 0,579115012569674 )
J Chem Inf Model - iLOGP: a simple, robust, and efficient description of n-octanol/water partition coefficient for drug design using the GB/SA approach. ( 0,577987927894741 )
Comput. Biol. Med. - Artificial neural network modelling of the results of tympanoplasty in chronic suppurative otitis media patients. ( 0,576923274121577 )
J Chem Inf Model - Time-split cross-validation as a method for estimating the goodness of prospective prediction. ( 0,574331651673521 )
J Chem Inf Model - Real external predictivity of QSAR models. Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection. ( 0,574205406863374 )
Med Decis Making - Constructing proper ROCs from ordinal response data using weighted power functions. ( 0,571069748690006 )
J Chem Inf Model - Optimizing predictive performance of CASE Ultra expert system models using the applicability domains of individual toxicity alerts. ( 0,567940636589241 )
J Chem Inf Model - RS-Predictor models augmented with SMARTCyp reactivities: robust metabolic regioselectivity predictions for nine CYP isozymes. ( 0,565608085852597 )
Comput Math Methods Med - Screening for prediabetes using machine learning models. ( 0,563403896583172 )
Int J Health Geogr - Comparative analysis of remotely-sensed data products via ecological niche modeling of avian influenza case occurrences in Middle Eastern poultry. ( 0,561514176778881 )
J Integr Bioinform - Classification of breast cancer subtypes by combining gene expression and DNA methylation data. ( 0,560672573543349 )
Int J Health Geogr - Prediction of high-risk areas for visceral leishmaniasis using socioeconomic indicators and remote sensing data. ( 0,560305392982152 )
Med Biol Eng Comput - Application of the RIMARC algorithm to a large data set of action potentials and clinical parameters for risk prediction of atrial fibrillation. ( 0,558407056981788 )
J. Comput. Biol. - Boolean models can explain bistability in the lac operon. ( 0,558300762607695 )
J Chem Inf Model - Comparative studies on some metrics for external validation of QSPR models. ( 0,556036412554109 )
J Am Med Inform Assoc - Finding falls in ambulatory care clinical documents using statistical text mining. ( 0,555767890023408 )
J Med Syst - Utilization of electronic medical records to build a detection model for surveillance of healthcare-associated urinary tract infections. ( 0,551897647806347 )
Artif Intell Med - NICeSim: an open-source simulator based on machine learning techniques to support medical research on prenatal and perinatal care decision making. ( 0,548521135494027 )
J Chem Inf Model - Predicting pK(a) values of substituted phenols from atomic charges: comparison of different quantum mechanical methods and charge distribution schemes. ( 0,546093574569578 )
J Med Syst - Data mining techniques for assisting the diagnosis of pressure ulcer development in surgical patients. ( 0,545633232968373 )
AMIA Annu Symp Proc - Predicting the dengue incidence in Singapore using univariate time series models. ( 0,544205922912698 )
BMC Med Inform Decis Mak - Regression tree construction by bootstrap: model search for DRG-systems applied to Austrian health-data. ( 0,543395640468735 )
J Chem Inf Model - Applicability domain based on ensemble learning in classification and regression analyses. ( 0,542026076107779 )
J Chem Inf Model - Prediction of linear cationic antimicrobial peptides based on characteristics responsible for their interaction with the membranes. ( 0,540795305993616 )
J Chem Inf Model - Classification of compounds with distinct or overlapping multi-target activities and diverse molecular mechanisms using emerging chemical patterns. ( 0,539724668840577 )
Lifetime Data Anal - Analysis of cure rate survival data under proportional odds model. ( 0,538893783516505 )
J Chem Inf Model - Are bigger data sets better for machine learning? Fusing single-point and dual-event dose response data for Mycobacterium tuberculosis. ( 0,537980532582526 )
Med Decis Making - Developing a tuberculosis transmission model that accounts for changes in population health. ( 0,535887450965596 )
Neural Comput - Input statistics and Hebbian cross-talk effects. ( 0,535406834718759 )
Comput Methods Programs Biomed - A predictive model of longitudinal, patient-specific colonoscopy results. ( 0,535295127162325 )
J Chem Inf Model - Experimental and computational prediction of glass transition temperature of drugs. ( 0,535206028838753 )
J Am Med Inform Assoc - Harvest: an open platform for developing web-based biomedical data discovery and reporting applications. ( 0,535129475007491 )
J Chem Inf Model - Comparison of random forest and Pipeline Pilot Na?ve Bayes in prospective QSAR predictions. ( 0,533980716650031 )
AMIA Annu Symp Proc - A vision of the journey ahead: using public health notifiable condition mapping to illustrate the need to maintain value sets. ( 0,532191150083454 )
IEEE Trans Neural Netw Learn Syst - Hyperparameter Selection for Gaussian Process One-Class Classification. ( 0,531628438605449 )
J Chem Inf Model - Binary classification of aqueous solubility using support vector machines with reduction and recombination feature selection. ( 0,530609955022322 )
BMC Med Inform Decis Mak - A prognostic model for estimating the time to virologic failure in HIV-1 infected patients undergoing a new combination antiretroviral therapy regimen. ( 0,529579139813478 )
Artif Intell Med - Improving predictive models of glaucoma severity by incorporating quality indicators. ( 0,529190896897034 )
J Chem Inf Model - Applicability Domain ANalysis (ADAN): a robust method for assessing the reliability of drug property predictions. ( 0,52623960649636 )
J Chem Inf Model - Beyond the scope of Free-Wilson analysis: building interpretable QSAR models with machine learning algorithms. ( 0,525502743063847 )
AMIA Annu Symp Proc - Advanced proficiency EHR training: effect on physicians' EHR efficiency, EHR satisfaction and job satisfaction. ( 0,524708175703737 )
J Clin Monit Comput - Complex signals bioinformatics: evaluation of heart rate characteristics monitoring as a novel risk marker for neonatal sepsis. ( 0,524504636288064 )
Med Biol Eng Comput - Optimal design of clinical tests for the identification of physiological models of type 1 diabetes in the presence of model mismatch. ( 0,523062050694845 )
J Chem Inf Model - Three useful dimensions for domain applicability in QSAR models using random forest. ( 0,52304676424582 )
Wiley Interdiscip Rev Syst Biol Med - Mechanistic modeling to investigate signaling by oncogenic Ras mutants. ( 0,52090124850154 )
AMIA Annu Symp Proc - Ontology-based federated data access to human studies information. ( 0,516955339447158 )
J Chem Inf Model - Estimation of carcinogenicity using molecular fragments tree. ( 0,516627049685999 )
Spat Spatiotemporal Epidemiol - Spatial modelling of disease using data- and knowledge-driven approaches. ( 0,51571196696196 )
Comput. Aided Surg. - Evaluation of a computational model to predict elbow range of motion. ( 0,51452930625348 )
Med Decis Making - Adaptation of clinical prediction models for application in local settings. ( 0,513540688954637 )
J Chem Inf Model - Ligand and structure-based classification models for prediction of P-glycoprotein inhibitors. ( 0,509896504694947 )
J Biomed Inform - Selection of interdependent genes via dynamic relevance analysis for cancer diagnosis. ( 0,508764550462455 )
Comput Math Methods Med - Multiscale autoregressive identification of neuroelectrophysiological systems. ( 0,508446769978499 )
Comput. Biol. Med. - Cholesteryl ester transfer protein inhibitors in coronary heart disease: Validated comparative QSAR modeling of N, N-disubstituted trifluoro-3-amino-2-propanols. ( 0,50811406448296 )
J Chem Inf Model - GRID-based three-dimensional pharmacophores II: PharmBench, a benchmark data set for evaluating pharmacophore elucidation methods. ( 0,508112761261148 )
BMC Med Inform Decis Mak - An evidential reasoning based model for diagnosis of lymph node metastasis in gastric cancer. ( 0,507595940657236 )
J Chem Inf Model - Rank order entropy: why one metric is not enough. ( 0,506839294435428 )
J Chem Inf Model - Impact of template choice on homology model efficiency in virtual screening. ( 0,505406326686881 )
AMIA Annu Symp Proc - Selecting cases for whom additional tests can improve prognostication. ( 0,505267687988899 )
J Chem Inf Model - Statistical analysis and compound selection of combinatorial libraries for soluble epoxide hydrolase. ( 0,505260939762747 )
J Chem Inf Model - A multiscale simulation system for the prediction of drug-induced cardiotoxicity. ( 0,504851079196522 )
Int J Health Geogr - Incorporating geographical factors with artificial neural networks to predict reference values of erythrocyte sedimentation rate. ( 0,504562372065919 )
BMC Med Inform Decis Mak - Diabetic retinopathy risk prediction for fundus examination using sparse learning: a cross-sectional study. ( 0,503478169531479 )
J Chem Inf Model - Pharmacophore assessment through 3-D QSAR: evaluation of the predictive ability on new derivatives by the application on a series of antitubercular agents. ( 0,502590074202126 )
J. Comput. Biol. - The complexity of the dirichlet model for multiple alignment data. ( 0,501826724615192 )
J Biomed Inform - Development of reusable logic for determination of statin exposure-time from electronic health records. ( 0,501318698762995 )
Comput. Biol. Med. - Quantification of contributions of molecular fragments for eye irritation of organic chemicals using QSAR study. ( 0,499642104454049 )
Lifetime Data Anal - Bayesian inference of the fully specified subdistribution model for survival data with competing risks. ( 0,498285628533138 )
IEEE Trans Image Process - Incremental N-mode SVD for large-scale multilinear generative models. ( 0,498000358310301 )
AMIA Annu Symp Proc - Order sets in computerized physician order entry systems: an analysis of seven sites. ( 0,497282760348223 )
Brief. Bioinformatics - Systematic assessment of imputation performance using the 1000 Genomes reference panels. ( 0,495926787784445 )
J Chem Inf Model - Ligand efficiency-based support vector regression models for predicting bioactivities of ligands to drug target proteins. ( 0,494571013185843 )
J Chem Inf Model - Four-dimensional structure-activity relationship model to predict HIV-1 integrase strand transfer inhibition using LQTA-QSAR methodology. ( 0,494388414616502 )
Comput Methods Programs Biomed - Bayesian bivariate generalized Lindley model for survival data with a cure fraction. ( 0,493810370800942 )
Spat Spatiotemporal Epidemiol - Assessment of land use factors associated with dengue cases in Malaysia using Boosted Regression Trees. ( 0,49368278315542 )
BMC Med Inform Decis Mak - Filtering data from the collaborative initial glaucoma treatment study for improved identification of glaucoma progression. ( 0,492619621744439 )
AMIA Annu Symp Proc - Identifying Deviations from Usual Medical Care using a Statistical Approach. ( 0,492113249357602 )
Comput Math Methods Med - Variable selection in ROC regression. ( 0,492107306849791 )
Comput. Biol. Med. - A knowledge-driven probabilistic framework for the prediction of protein-protein interaction networks. ( 0,49181061796281 )
BMC Med Inform Decis Mak - Improving sensitivity of machine learning methods for automated case identification from free-text electronic medical records. ( 0,491204692562117 )
J Biomed Inform - Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: an application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39). ( 0,489775647327662 )
Int J Health Geogr - Modelling typhoid risk in Dhaka metropolitan area of Bangladesh: the role of socio-economic and environmental factors. ( 0,48896093014515 )
J Chem Inf Model - Prediction of compound potency changes in matched molecular pairs using support vector regression. ( 0,487047813722387 )
J Am Med Inform Assoc - Choosing blindly but wisely: differentially private solicitation of DNA datasets for disease marker discovery. ( 0,486132757072364 )
Appl Clin Inform - Development of an automated, real time surveillance tool for predicting readmissions at a community hospital. ( 0,485901894398854 )
Artif Intell Med - Predicting the need for CT imaging in children with minor head injury using an ensemble of Naive Bayes classifiers. ( 0,485650964975994 )
Comput. Biol. Med. - A prediction model of substrates and non-substrates of breast cancer resistance protein (BCRP) developed by GA-CG-SVM method. ( 0,484694780677783 )