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Machine Learning for Brain Imaging Genomics Methods:A Review

Machine Intelligence Research 2023年第1期20卷 57-78页

作者： Mei-Ling Wang Wei Shao Xiao-Ke Hao Dao-Qiang Zhang College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China Key Laboratory of Pattern Analysis and Machine Intelligence Ministry of Industry and Information TechnologyNanjing 211106China School of Artificial Intelligence Hebei University of TechnologyTianjin 300401China

In the past decade,multimodal neuroimaging and genomic techniques have been increasingly *** an interdiscip-linary topic,brain imaging genomics is devoted to evaluating and characterizing genetic variants in individuals that influence phenotyp-ic measures derived from structural and functional brain *** technique is capable of revealing the complex mechanisms by macroscopic intermediates from the genetic level to cognition and psychiatric disorders in *** is well known that machine learn-ing is a powerful tool in the data-driven association studies,which can fully utilize priori knowledge(intercorrelated structure informa-tion among imaging and genetic data)for association *** addition,the association study is able to find the association between risk genes and brain structure or function so that a better mechanistic understanding of behaviors or disordered brain functions is *** this paper,the related background and fundamental work in imaging genomics are first ***,we show the univari-ate learning approaches for association analysis,summarize the main idea and modelling in genetic-imaging association studies based on multivariate machine learning,and present methods for joint association analysis and outcome ***,this paper discusses some prospects for future work.

关键词： Brain imaging genomics machine learning multivariate analysis association analysis outcome prediction

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Artificial intelligence in radiotherapy:a technological review

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Frontiers of Medicine 2020年第4期14卷 431-449页

作者： Ke Sheng Department of Radiation Oncology University of CaliforniaLos AngelesCA 90095USA

Radiation therapy(RT)is widely used to treat *** advances in RT have occurred in the past 30 *** advances,such as three-dimensional image guidance,intensity modulation,and robotics,created challenges and opportunities for the next breakthrough,in which artificial intelligence(AI)will possibly play important *** will replace certain repetitive and labor-intensive tasks and improve the accuracy and consistency of others,particularly those with increased complexity because of technological *** improvement in efficiency and consistency is important to manage the increasing cancer patient burden to the ***,AI may provide new functionalities that facilitate satisfactory *** functionalities include superior images for real-time intervention and adaptive and personalized *** may effectively synthesize and analyze big data for such *** review describes the RT workflow and identifies areas,including imaging,treatment planning,quality assurance,and outcome prediction,that benefit from *** review primarily focuses on deep-learning techniques,although conventional machine-learning techniques are also mentioned.

关键词： artificial intelligence radiation therapy medical imaging treatment planning quality assurance outcome prediction

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Development and Validation of a New Prognostic Model for Predicting Survival outcomes in Patients with Acute-onchronic Liver Failure

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Journal of Clinical and Translational Hepatology 2024年第10期12卷 834-844页

作者： Wende Li Wanshu Liu Yihui Rong Dongze Li Bing Zhu Shaobo Yang Shidong Sun Shaoli You Yu Chen Jun Li Department of Infection and Liver Diseases Peking University International HospitalBeijingChina Department of Hepatology Medicine Fifth Medical Center of Chinese PLA General HospitalBeijingChina Fourth Department of Liver Disease Beijing Youan HospitalCapital Medical UniversityBeijingChina State Key Laboratory for Diagnosis and Treatment of Infectious Diseases Collaborative Innovation Center for Diagnosis and Treatment of Infectious DiseasesZhejiang University School of Medicine First Affiliated HospitalHangzhouZhejiangChina

Background and Aims:Early determination of prognosis in patients with acute-on-chronic liver failure(ACLF)is crucial for optimizing treatment options and liver *** study aimed to identify risk factors associated with ACLF and to develop new prognostic models that accurately predict patient ***:We retrospectively selected 1,952 hospitalized patients diagnosed with ACLF between January 2010 and June *** cohort was used to develop new prognostic scores,which were subsequently validated in external ***:The study included 1,386 ACLF patients and identified six independent predictors of 28-day mortality through multivariate analysis(all p<0.05).The new score,based on a multivariate regression model,demonstrated superior predictive accuracy for both 28-day and 90-day mortalities,with Areas under the ROC curves of 0.863 and 0.853,respectively(all p<0.05).This score can be used to stratify the risk of mortality among ACLF patients with ACLF,showing a significant difference in survival between patients categorized by the cut-off value(log-rank(Mantel-Cox)χ^(2)=487.574 and 606.441,p=0.000).Additionally,the new model exhibited good robustness in two external ***:This study presents a refined prognostic model,the Model for end-stage liver disease-complication score,which accurately predicts short-term mortality in ACLF *** model offers a new perspective and tool for improved clinical decision-making and short-term prognostic assessment in ACLF patients.

关键词： Acute-on-chronic liver failure ACLF outcome prediction Mortality Prognosis Model

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Multi-outcome predictive modelling of anesthesia patients

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The Journal of Biomedical Research 2019年第6期33卷 430-434页

作者： Le Yi Wang George MMcKelvey Hong Wang Department of Electrical and Computer Engineering Wayne State UniversityDetroitMI 48202USA Department of Anesthesiology Detroit Medical CenterDetroitMI 48201USA Department of Anesthesiology West Virginia University HospitalMorgantownWV 26505USA

Conjunctive use of anesthetic agents results in drug interactions which can alter or influence multiple patient outcomes such as anesthesia depth,and cardiorespiratory parameters which can also be altered by patient conditions and surgical *** artificial intelligence technology to continuously gather data of drug infusion and patient outcomes,we can generate reliable computer models individualized for a patient during specific stages of particular surgical *** data can then be used to extend the current anesthesia monitoring functions to include future impact prediction,drug administration planning,and anesthesia decisions.

关键词： anesthesiology monitoring anesthesia depth patient model outcome prediction computer-assisted decision

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