简介：AbstractMachine learning shows enormous potential in facilitating decision-making regarding kidney diseases. With the development of data preservation and processing, as well as the advancement of machine learning algorithms, machine learning is expected to make remarkable breakthroughs in nephrology. Machine learning models have yielded many preliminaries to moderate and several excellent achievements in the fields, including analysis of renal pathological images, diagnosis and prognosis of chronic kidney diseases and acute kidney injury, as well as management of dialysis treatments. However, it is just scratching the surface of the field; at the same time, machine learning and its applications in renal diseases are facing a number of challenges. In this review, we discuss the application status, challenges and future prospects of machine learning in nephrology to help people further understand and improve the capacity for prediction, detection, and care quality in kidney diseases.

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简介：AbstractPurpose:Traumatic brain injury (TBI) generally causes mortality and disability, particularly in children. Machine learning (ML) is a computer algorithm, applied as a clinical prediction tool. The present study aims to assess the predictability of ML for the functional outcomes of pediatric TBI.Methods:A retrospective cohort study was performed targeting children with TBI who were admitted to the trauma center of southern Thailand between January 2009 and July 2020. The patient was excluded if he/she (1) did not undergo a CT scan of the brain, (2) died within the first 24 h, (3) had unavailable complete medical records during admission, or (4) was unable to provide updated outcomes. Clinical and radiologic characteristics were collected such as vital signs, Glasgow coma scale score, and characteristics of intracranial injuries. The functional outcome was assessed using the King's Outcome Scale for Childhood Head Injury, which was thus dichotomized into favourable outcomes and unfavourable outcomes: good recovery and moderate disability were categorized as the former, whereas death, vegetative state, and severe disability were categorized as the latter. The prognostic factors were estimated using traditional binary logistic regression. By data splitting, 70% of data were used for training the ML models and the remaining 30% were used for testing the ML models. The supervised algorithms including support vector machines, neural networks, random forest, logistic regression, naive Bayes and k-nearest neighbor were performed for training of the ML models. Therefore, the ML models were tested for the predictive performances by the testing datasets.Results:There were 828 patients in the cohort. The median age was 72 months (interquartile range 104.7 months, range 2-179 months). Road traffic accident was the most common mechanism of injury, accounting for 68.7%. At hospital discharge, favourable outcomes were achieved in 97.0% of patients, while the mortality rate was 2.2%. Glasgow coma scale score, hypotension, pupillary light reflex, and subarachnoid haemorrhage were associated with TBI outcomes following traditional binary logistic regression; hence, the 4 prognostic factors were used for building ML models and testing performance. The support vector machine model had the best performance for predicting pediatric TBI outcomes: sensitivity 0.95, specificity 0.60, positive predicted value 0.99, negative predictive value 1.0; accuracy 0.94, and area under the receiver operating characteristic curve 0.78.Conclusion:The ML algorithms of the present study have a high sensitivity; therefore they have the potential to be screening tools for predicting functional outcomes and counselling prognosis in general practice of pediatric TBIs.

简介：AbstractIn the past decades, there have been numerous advancements in the field of technology. This has led to many scientific breakthroughs in the field of medical sciences. In this, rapidly transforming world we are having a difficult time and the problem of fatigue is becoming prevalent. So, this study aimed to understand what is fatigue, its repercussions, and techniques to detect it using machine learning (ML) approaches. This paper introduces, discusses methods and recent advancements in the field of fatigue detection. Further, we categorized the methods that can be used to detect fatigue into four diverse groups, that is, mathematical models, rule-based implementation, ML, and deep learning. This study presents, compares, and contrasts various algorithms to find the most promising approach that can be used for the detection of fatigue. Finally, the paper discusses the possible areas for improvement.

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简介：Asthesignificantbranchofintelligentvehiclenetworkingtechnology,theintelligentfatiguedrivingdetectiontechnologyhasbeenintroducedintothepaperinordertorecognizethefatiguestateofthevehicledriverandavoidthetrafficaccident.Thedisadvantagesofthetraditionalfatiguedrivingdetectionmethodhavebeenpointedoutwhenwestudyonthetraditionaleyetrackingtechnologyandtraditionalartificialneuralnetworks.Onthebasisoftheimagetopologicalanalysistechnology,Haarlikefeaturesandextremelearningmachinealgorithm,anewdetectionmethodoftheintelligentfatiguedrivinghasbeenproposedinthepaper.Besides,thedetailedalgorithmandrealizationschemeoftheintelligentfatiguedrivingdetectionhavebeenputforwardaswell.Finally,bycomparingtheresultsofthesimulationexperiments,thenewmethodhasbeenverifiedtohaveabetterrobustness,efficiencyandaccuracyinmonitoringandtrackingthedrivers’fatiguedrivingbyusingthehumaneyetrackingtechnology.

简介：Inthispaper,wepresentanovelSupportVectorMachineactivelearningalgorithmforeffective3Dmodelretrievalusingtheconceptofrelevancefeedback.Theproposedmethodlearnsfromthemostinformativeobjectswhicharemarkedbytheuser,andthencreatesaboundaryseparatingtherelevantmodelsfromirrelevantones.Whatitneedsisonlyasmallnumberof3Dmodelslabelledbytheuser.Itcangrasptheuser'ssemanticknowledgerapidlyandaccurately.Experimentalresultsshowedthattheproposedalgorithmsignificantlyimprovestheretrievaleffectiveness.Comparedwithfourstate-of-the-artqueryrefinementschemesfor3Dmodelretrieval,itprovidessuperiorretrievalperformanceafternomorethantworoundsofrelevance

简介：识别蛋白质的细胞的本地化是的潜水艇在基因产品的功能的注解特别地有用。在这研究，我们使用机器学习和探索数据分析(EDA)技术检验并且描绘在九细胞的分隔空间局部性的人的蛋白质的氨基酸序列。代表人的蛋白质的3,749个蛋白质序列的数据集从SWISS-PROT数据库被提取。特征向量被创造捕获特定的氨基酸顺序特征。相对一台支持向量机器，一个多层的视感控器，和一个天真的Bayes分类器，C4.5决定树算法是越过在可靠地预言蛋白质的细胞的本地化基于他们的氨基酸定序的潜水艇的所有九分隔空间的最历久不渝的表演者(平均Precision=0.88；平均Sensitivity=0.86)。而且，EDA图形在每分隔空间描绘了蛋白质的必要特征。作为例子，在血浆膜上局部性的蛋白质有恐水病的氨基酸的更高的比例；细胞质的蛋白质有中立氨基酸的更高的比例；并且mitochondrial蛋白质有中立氨基酸的更高的比例和极的氨基酸的更低的比例。这些数据证明C4.5分类器和EDA工具能为描绘并且预言人的蛋白质的细胞的本地化基于他们的氨基酸定序的潜水艇是有效的。

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简介：AbstractBackground:Substantial research is underway to develop next-generation interventions that address current malaria control challenges. As there is limited testing in their early development, it is difficult to predefine intervention properties such as efficacy that achieve target health goals, and therefore challenging to prioritize selection of novel candidate interventions. Here, we present a quantitative approach to guide intervention development using mathematical models of malaria dynamics coupled with machine learning. Our analysis identifies requirements of efficacy, coverage, and duration of effect for five novel malaria interventions to achieve targeted reductions in malaria prevalence.Methods:A mathematical model of malaria transmission dynamics is used to simulate deployment and predict potential impact of new malaria interventions by considering operational, health-system, population, and disease characteristics. Our method relies on consultation with product development stakeholders to define the putative space of novel intervention specifications. We couple the disease model with machine learning to search this multi-dimensional space and efficiently identify optimal intervention properties that achieve specified health goals.Results:We apply our approach to five malaria interventions under development. Aiming for malaria prevalence reduction, we identify and quantify key determinants of intervention impact along with their minimal properties required to achieve the desired health goals. While coverage is generally identified as the largest driver of impact, higher efficacy, longer protection duration or multiple deployments per year are needed to increase prevalence reduction. We show that interventions on multiple parasite or vector targets, as well as combinations the new interventions with drug treatment, lead to significant burden reductions and lower efficacy or duration requirements.Conclusions:Our approach uses disease dynamic models and machine learning to support decision-making and resource investment, facilitating development of new malaria interventions. By evaluating the intervention capabilities in relation to the targeted health goal, our analysis allows prioritization of interventions and of their specifications from an early stage in development, and subsequent investments to be channeled cost-effectively towards impact maximization. This study highlights the role of mathematical models to support intervention development. Although we focus on five malaria interventions, the analysis is generalizable to other new malaria interventions.

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简介：High-frequencystocktrendpredictionusingmachinelearnershasraisedsubstantialinterestinliterature.Nevertheless,thereisnogoldstandardtoselecttheinputsforthelearners.Thispaperinvestigatestheapproachofadaptiveinputselection(AIS)forthetrendpredictionofhigh-frequencystockindexpriceandcomparesitwiththecommonlyuseddeterministicinputsetting(DIS)approach.TheDISapproachisimplementedthroughcomputationoftechnicalindicatorvaluesondeterministicperiodparameters.TheAISapproachselectsthemostsuitableindicatorsandtheirparametersforthetime-varyingdatasetusingfeatureselectionmethods.Twostate-of-the-artmachinelearners,supportvectormachine(SVM)andartificialneuralnetwork(ANN),areadoptedaslearningmodels.AccuracyandF-measureofSVMandANNmodelswithboththeapproachesarecomputedbasedonthehigh-frequencydataofCSI300index.TheresultssuggestthattheAISapproachusingt-statistics,informationgainandROCmethodscanachievebetterpredictionperformancethantheDISapproach.Also,theinvestmentperformanceevaluationshowsthattheAISapproachwiththesamethreefeatureselectionmethodsprovidessignificantlyhigherreturnsthantheDISapproach.

简介：AbstractBackground:Fever is the most common chief complaint of emergency patients. Early identification of patients at an increasing risk of death may avert adverse outcomes. The aim of this study was to establish an early prediction model of fatal adverse prognosis of fever patients by extracting key indicators using big data technology.Methods:A retrospective study of patients’ data was conducted using the Emergency Rescue Database of Chinese People’s Liberation Army General Hospital. Patients were divided into the fatal adverse prognosis group and the good prognosis group. The commonly used clinical indicators were compared. Recursive feature elimination method was used to determine the optimal number of the included variables. In the training model, logistic regression, random forest, adaboost, and bagging were selected. We also collected the emergency room data from December 2018 to December 2019 with the same inclusion and exclusion criterion. The performance of the model was evaluated by accuracy, F1-score, precision, sensitivity, and the areas under receiver operator characteristic curves (ROC-AUC).Results:The accuracy of logistic regression, decision tree, adaboost and bagging was 0.951, 0.928, 0.924, and 0.924, F1-scores were 0.938, 0.933, 0.930, and 0.930, the precision was 0.943, 0.938, 0.937, and 0.937, ROC-AUC were 0.808, 0.738, 0.736, and 0.885, respectively. ROC-AUC of ten-fold cross-validation in logistic and bagging models were 0.80 and 0.87, respectively. The top six coefficients and odds ratio (OR) values of the variables in the logistic regression were cardiac troponin T (CTnT) (coefficient = 0.346, OR = 1.413), temperature (T) (coefficient = 0.235, OR = 1.265), respiratory rate (RR) (coefficient= –0.206, OR = 0.814), serum kalium (K) (coefficient = 0.137, OR = 1.146), pulse oxygen saturation (SPO2) (coefficient = –0.101, OR = 0.904), and albumin (ALB) (coefficient = –0.043, OR = 0.958). The weights of the top six variables in the bagging model were: CTnT, RR, lactate dehydrogenase, serum amylase, heart rate, and systolic blood pressure.Conclusions:The main clinical indicators of concern included CTnT, RR, SPO2, T, ALB, and K. The bagging model and logistic regression model had better diagnostic performance comprehesively. Those may be conducive to the early identification of critical patients with fever by physicians.

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简介：AbstractBackground:Grayscale image attributes of computed tomography (CT) of pulmonary scans contain valuable information relating to patients with respiratory ailments. These attributes are used to evaluate the severity of lung conditions of patients confirmed to be with and without COVID-19.Method:Five hundred thirteen CT images relating to 57 patients (49 with COVID-19; 8 free of COVID-19) were collected at Namazi Medical Centre (Shiraz, Iran) in 2020 and 2021. Five visual scores (VS: 0, 1, 2, 3, or 4) are clinically assigned to these images with the score increasing with the severity of COVID-19-related lung conditions. Eleven deep learning and machine learning techniques (DL/ML) are used to distinguish the VS class based on 12 grayscale image attributes.Results:The convolutional neural network achieves 96.49% VS accuracy (18 errors from 513 images) successfully distinguishing VS Classes 0 and 1, outperforming clinicians’ visual inspections. An algorithmic score (AS), involving just five grayscale image attributes, is developed independently of clinicians’ assessments (99.81% AS accuracy; 1 error from 513 images).Conclusion:Grayscale CT image attributes can be successfully used to distinguish the severity of COVID-19 lung damage. The AS technique developed provides a suitable basis for an automated system using ML/DL methods and 12 image attributes.

简介：AbstractBackground:Liver fibrosis (LF) continues to develop and eventually progresses to cirrhosis. However, LF and early-stage cirrhosis (ESC) can be reversed in some cases, while advanced cirrhosis is almost impossible to cure. Advances in quantitative imaging techniques have made it possible to replace the gold standard biopsy method with non-invasive imaging, such as radiomics. Therefore, the purpose of this study is to develop a radiomics model to identify LF and ESC.Methods:Patients with LF (n = 108) and ESC (n = 116) were enrolled in this study. As a control, patients with healthy livers were involved in the study (n = 145). Diffusion-weighted imaging (DWI) data sets with three b-values (0, 400, and 800 s/mm2) of enrolled cases were collected in this study. Then, radiomics features were extracted from manually delineated volumes of interest. Two modeling strategies were performed after univariate analysis and feature selection. Finally, an optimal model was determined by the receiver operating characteristic area under the curve (AUC).Results:The optimal models were built in plan 1. For model 1 in plan 1, the AUCs of the training and validation cohorts were 0.973 (95% confidence interval [CI] 0.946-1.000) and 0.948 (95% CI 0.903-0.993), respectively. For model 2 in plan 1, the AUCs of the training and validation cohorts were 0.944, 95% CI 0.905 to 0.983, and 0.968, 95% CI 0.940 to 0.996, respectively.Conclusions:Radiomics analysis of DWI images allows for accurate identification of LF and ESC, and the non-invasive biomarkers extracted from the functional DWI images can serve as a better alternative to biopsy.

简介：AbstractBackground:Being able to predict with confidence the early onset of type 2 diabetes from a suite of signs and symptoms (features) displayed by potential sufferers is desirable to commence treatment promptly. Late or inconclusive diagnosis can result in more serious health consequences for sufferers and higher costs for health care services in the long run.Methods:A novel integrated methodology is proposed involving correlation, statistical analysis, machine learning, multi-K-fold cross-validation, and confusion matrices to provide a reliable classification of diabetes-positive and -negative individuals from a substantial suite of features. The method also identifies the relative influence of each feature on the diabetes diagnosis and highlights the most important ones. Ten statistical and machine learning methods are utilized to conduct the analysis.Results:A published data set involving 520 individuals (Sylthet Diabetes Hospital, Bangladesh) is modeled revealing that a support vector classifier generates the most accurate early-onset type 2 diabetes status predictions with just 11 misclassifications (2.1% error). Polydipsia and polyuria are among the most influential features, whereas obesity and age are assigned low weights by the prediction models.Conclusion:The proposed methodology can rapidly predict early-onset type 2 diabetes with high confidence while providing valuable insight into the key influential features involved in such predictions.

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简介：Biomimetics(orbionics)istheengineeringdisciplinethatconstructsartificialsystemsusingbiologicalprinciples.Theidealfinalresultinbiomimeticsistocreatealivingmachine.Butwhatarethedesirableandnon-desirablepropertiesofbiomimeticproduct?Wherecannaturalprototypesbefound?Howcantechnicalsolutionsbetransferredfromnaturetotechnology?CanweuselivingnaturelikeLEGObricksforconstructionourmachines?Howcanbiologyhelpus?Whatisalivingmachine?Inbiomimeticpracticeonlysome"part"(organ,partoforgan,tissue)oftheobservedwholeorganismisutilized.Apossibletemplateforfuturesuper-organismextensionforbiomimeticmethodsmightbedrawnfromexperimentsinholisticecologicalagriculture(ecologicaldesign,permaculture,ecologicalengineering,etc.).ThenecessarytranslationoftheserulestopracticalactioncanbeachievedwiththeRussianTheoryofInventiveProblemSolving(TRIZ),specificallyadjustedtobiology.Thus,permaculture,reinforcedbyaTRIZconceptualframework,mightprovidethebasisforSuper-OrganismicBionics,whichishypothesizedasnecessaryforeffectiveecologicalengineering.Thishypothesisissupportedbyacasestudy-thedesignofasustainableartificialnaturereserveforwildpollinatorsasalivingmachine.

简介：TheTerabyteAnalysisMachineProjectisDevelopinghardwareandsoftwaretoanalyzeTerabytescaledatasets.TheDistanceMachineframeworkprovidesfacilitiestoflexiblyinterfaceapplicationspecificindexingandpartitioningalgorthmstolargescientificdatabases.