References
Nils, B. H. & Sabine, S. The morals of instrumentality learning-based objective determination support: an investigation done the lens of professionalisation theory. BMC Med. Ethics 22, 112 (2021).
Sanchez-Pinto, L. N., Luo, Y. & Churpek, M. M. Big information and information subject successful captious care. Chest 154, 1239–1248 (2018).
Miotto, R., Wang, F., Wang, S., Jiang, X. & Dudley, J. T. Deep learning for healthcare: Review, opportunities, and challenges. Brief Bioinform. 19, 1236–1246 (2017).
Timothy, J. W. D. et al. Machine learning of threedimensional close ventricular question enables result prediction successful pulmonary hypertension: A cardiac MR imaging study. Radiology 283, 381–390 (2017).
Gigi, F. S., Gregory, R. H., Bradley, J. N. & Jun, D. Predicting bosom crab hazard utilizing idiosyncratic wellness information and instrumentality learning models. PLoS One 14, e0226765 (2019).
Michael, F., Justin, B. E.-T. & Elizabeth, S. Clinical and nationalist wellness implications of 2019 endocrine nine guidelines for diagnosis of diabetes successful older adults. Diabetes Care 43, 1456–1461 (2020).
Gambhir, S. S., Ge, T. J., Vermesh, O. & Spitler, R. Toward achieving precision health. Sci. Transl. Med. 10, eaao3612 (2018).
Schüssler-Fiorenza Rose, S. M. et al. A longitudinal large information attack for precision health. Nat. Med. 25, 792–804 (2019).
Feero, W. G. Introducing “genomics and precision health”. JAMA 317, 1842–1843 (2017).
Kellogg, R. A., Dunn, J. & Snyder, M. P. Personal omics for precision health. Circulation Res. 122, 1169–1171 (2018).
Thapa, C. & Camtepe, S. Precision wellness data: Requirements, challenges and existing techniques for information information and privacy. Comput. Biol. Med. 129, 104130 (2021).
Pranata, S. et al. Precision wellness attraction elements, definitions, and strategies for patients with diabetes: A lit review. Int. J. Environ. Res. Public Health 18, 6535 (2021).
Shih Cheng, H., Anuj, P., Saeed, S., Imon, B. & Matthew, P. L. Fusion of aesculapian imaging and physics wellness records utilizing heavy learning: A systematic reappraisal and implementation guidelines. npj Digital Med. 3, 136 (2020).
Weixian, H., Kaiwen, T., Jinlong, H., Ziye, Z. & Shoubin, D. A reappraisal of fusion methods for omics and imaging data. In IEEE/ACM Trans Comput Biol Bioinform (IEEE, 2022).
Federico, C. A reappraisal of information fusion techniques. Scientific World J. 2013, 704504 (2013).
Alan, N. S., Christopher, L. B. & Franklin, E. W. Revisions to the JDL information fusion model. Sens. Fusion.: Architectures, Algorithms, Appl. III 3719, 430 (1999). steadfast = SPIE.
Erik, M.-M.-R., Antonio, A. A., Ramon, F. B. & Enrique, G.-C. Improved accuracy successful predicting the champion sensor fusion architecture for aggregate domains. Sensors 21, 7007 (2021).
Ahmad, F. S., Luo, Y., Wehbe, R. M., Thomas, J. D. & Shah, S. J. Advances successful instrumentality learning approaches to bosom nonaccomplishment with preserved ejection fraction. Heart Fail Clin. 18, 287–300 (2022).
Erik, B. et al. Machine learning/artificial quality for sensor information fusion-opportunities and challenges. In IEEE Aerospace and Electronic Systems Magazines Vol. 36, 80–93 (IEEE, 2021).
Li, Y., Wu, X., Yang, P., Jiang, G. & Luo, Y. Machine learning applications successful diagnosis, treatment, and prognosis of lung cancer. Preprint astatine https://arxiv.org/abs/2203.02794 (2022).
Kohane, I. S. et al. What each scholar should cognize astir studies utilizing physics wellness grounds information but whitethorn beryllium acrophobic to ask. J. Med. Internet Res. 23, e22219 (2021).
Andres, C. et al. Machine-learning Prognostic Models from the 2014–16 Ebola Outbreak: Data-harmonization challenges, validation strategies, and mHealth applications. EClinicalMedicine 11, 54–64 (2019).
Afshin, J., Jean Pierre, P. & Johanne, M.-P. Machine-learning-based patient-specific prediction models for genu osteoarthritis. Nat. Rev. Rheumatol. 15, 49–60 (2019).
Luo, Y., Ahmad, F. S. & Shah, S. J. Tensor factorization for precision medicine successful bosom nonaccomplishment with preserved ejection fraction. J. Cardiovasc. Transl. Res. 10, 305–312 (2017).
Luo, Y., Wang, F. & Szolovits, P. Tensor factorization toward precision medicine. Briefings Bioinform. 18, 511–514 (2016).
Rasmussen L. et al. Considerations for improving the portability of physics wellness record-based phenotype algorithms. In Proceedings of 2019 AMIA Annual Symposium 2019 (2019).
Zhong, Y. et al. Characterizing plan patterns of EHR-driven phenotype extraction algorithms. In 2018 IEEE International Conference connected Bioinformatics and Biomedicine (BIBM) 1143–1146 (IEEE, 2018).
Rasmussen, L. V. et al. Solutions for unexpected challenges encountered erstwhile integrating probe genomics results into the EHR. ACI Open 4, e132–e5 (2020).
Shang, N. et al. Making enactment disposable for physics phenotype implementation: Lessons learned from the eMERGE network. J. Biomed. Inf. 99, 103293 (2019).
Dana, L., Tulay, A. & Christian, J. Multimodal information fusion: An overview of methods, challenges, and prospects. Proc. IEEE 103, 1449–1477 (2015).
Wei, C., Yungui, H., Brendan, B. & Simon, L. The inferior of including pathology reports successful improving the computational recognition of patients. J. Pathol. Inform. 7, 46 (2016).
Yubraj, G., Ramesh Kumar, L. & Goo Rak, K. Prediction and classification of Alzheimer’s illness based connected combined features from apolipoprotein-E genotype, cerebrospinal fluid, MR, and FDG-PET imaging biomarkers. Front. Comput. Neurosci. 13, 72 (2019).
Xia An, B., Xi, H., Hao, W. & Yang, W. Multimodal information investigation of Alzheimer’s illness based connected clustering evolutionary random forest. IEEE J. Biomed. Health Inform. 24, 2973–2983 (2020).
Ariana, A. et al. Non-negative matrix factorization of multimodal MRI, fMRI and phenotypic information reveals differential changes successful default mode subnetworks successful ADHD. Neuroimage 102, 207–219 (2014).
Chao, G., Luo, Y. & Ding, W. Recent advances successful supervised magnitude reduction: A survey. Mach. Learn. Knowl. Extraction 1, 341–358 (2019).
Pillai, P. S. L. T. Y. Fusing heterogeneous information for Alzheimer’s illness classification. Stud. Health Technol. Inf. 216, 731–735 (2015).
Tao, Z., Kim Han, T., Xiaofeng, Z. & Dinggang, S. Effective diagnostic learning and fusion of multimodality information utilizing stage-wise heavy neural web for dementia diagnosis. Hum. Brain Mapp. 40, 1001–1016 (2019).
Robi, P. Ensemble based systems successful determination making. IEEE Circuits Syst. Mag. 6, 21–44 (2006).
Francesco, C. & Marwa, M. Multimodal temporal instrumentality learning for bipolar upset and slump recognition. Pattern Analysis Appl. 25, 493–504 (2021).
Durrant-Whyte, H. F. Sensor models and multisensor integration. Int. J. Robot. Res. 7, 97–113 (1988).
Xu, Z. et al. Identification of predictive sub-phenotypes of acute kidney wounded utilizing structured and unstructured physics wellness grounds information with representation networks. J. Biomed. Inform. 102, 103361 (2019).
Dongdong, Z., Changchang, Y., Jucheng, Z., Xiaohui, Y. & Ping, Z. Combining structured and unstructured information for predictive models: A heavy learning approach. BMC Med. Inform. Decis. Mak. 20, 280 (2020).
Shaker, E.-S., Tamer, A., Islam, S. M. R. & Kyung Sup, K. Multimodal multitask heavy learning exemplary for Alzheimer’s illness progression detection based connected clip bid data. Neurocomputing 412, 197–215 (2020).
Haiyang, Y., Li, K. & Feng Qiang, X. Multimodal temporal-clinical enactment web for mortality prediction. J. Biomed. Semantics 12, 3 (2021).
Zizhao, Z., Pingjun, C., Manish, S. & Lin, Y. TandemNet: Distilling cognition from aesculapian images utilizing diagnostic reports arsenic optional semantic references. International Conference connected Medical Image Computing and Computer-Assisted Intervention, 10435, 320–328 (Springer, Cham, 2017).
Xiaosong, W., Yifan, P., Le, L., Zhiyong, L. & Ronald, M. S. TieNet: Text-Image embedding web for communal thorax illness classification and reporting successful thorax X-rays. In Proceedings of the IEEE Computer Society Conference connected Computer Vision and Pattern Recognition 9049–9058 (IEEE Computer Society, 2018).
Syed Arbaaz, Q., Sriparna, S., Mohammed, H., Gael, D. & Erik, C. Multitask practice learning for multimodal estimation of slump level. IEEE Intell. Syst. 34, 45–52 (2019).
Jordan, Y., William, Y. & Philipp, T. Multimodal tegument lesion classification utilizing heavy learning. Exp. Dermatol. 27, 1261–1267 (2018).
Kai, Z et al. MLMDA: A instrumentality learning attack to foretell and validate MicroRNA-disease associations by integrating of heterogenous accusation sources. J. Transl. Med. 17, 260 (2019).
Sivan, K. et al. Predicting hazard for Alcohol Use Disorder utilizing longitudinal information with multimodal biomarkers and household history: A instrumentality learning study. Mol. Psychiatry 26, 1133–1141 (2021).
Mara Ten, K et al. MRI predictors of amyloid pathology: Results from the EMIF-AD Multimodal Biomarker Discovery study. Alzheimer’s Res. Ther. 10, 100 (2018).
Isamu, H. et al. Radiogenomics predicts the look of microRNA-1246 successful the serum of esophageal crab patients. Sci. Rep. 10, 2532 (2020).
Jesus, J. C., Jianhua, Y. & Daniel, J. M. Enhancing representation analytic tools by fusing quantitative physiological values with representation features. J. Digit Imaging 25, 550–557 (2012).
Kevin Bretonnel, C. et al. Methodological issues successful predicting pediatric epilepsy country candidates done earthy connection processing and instrumentality learning. Biomed. Inform. Insights 8, BII.S38308 (2016).
Weiming, L. et al. Predicting Alzheimer’s illness conversion from mild cognitive impairment utilizing an utmost learning machine-based grading method with multimodal data. Front. Aging Neurosci. 12, 77 (2020).
Micah, C. et al. Predicting rehospitalization wrong 2 years of archetypal diligent admittance for a large depressive episode: A multimodal instrumentality learning approach. Transl. Psychiatry 9, 285 (2019).
Jongin, K. & Boreom, L. Identification of Alzheimer’s illness and mild cognitive impairment utilizing multimodal sparse hierarchical utmost learning machine. Hum. Brain Mapp. 39, 3728–3741 (2018).
Hélène De, C. et al. Wearable monitoring and interpretable instrumentality learning tin objectively way progression successful patients during cardiac rehabilitation. Sensors (Switz.) 20, 1–15 (2020).
Tamer, A., Shaker, E.-S. & Jose, M. A. Robust hybrid heavy learning models for Alzheimer’s progression detection. Knowledge-Based Syst. 213, 106688 (2021).
Jeungchan, L. et al. Machine learning-based prediction of objective symptom utilizing multimodal neuroimaging and autonomic metrics. Pain 160, 550–560 (2019).
Uttam, K., Goo Rak, K. & Horacio, R.-G. An businesslike operation among sMRI, CSF, cognitive score, and APOE ϵ 4 biomarkers for classification of AD and MCI utilizing utmost learning machine. Comput. Intell. Neurosci. 2020, 8015156 (2020).
Bo, C., Mingxia, L., Heung, I. S., Dinggang, S. & Daoqiang, Z. Multimodal manifold-regularized transportation learning for MCI conversion prediction. Brain Imaging Behav. 9, 913–926 (2015).
Kevin, H., Ulrike, L., Markus, M. & Katja, B-B. Separating generalized anxiousness upset from large slump utilizing clinical, hormonal, and structural MRI data: A multimodal instrumentality learning study. Brain Behavior 7, e00633 (2017).
Fayao, L., Luping, Z., Chunhua, S. & Jianping, Y. Multiple kernel learning successful the primal for multimodal alzheimer’s illness classification. IEEE J. Biomed. Health Inform. 18, 984–990 (2014).
Diego, C.-B. et al. Robust ensemble classification methodology for I123-Ioflupane SPECT images and aggregate heterogeneous biomarkers successful the diagnosis of Parkinson’s disease. Front. Neuroinform. 12, 53 (2018).
Yi, Z. et al. Predicting adverse cause reactions of combined medicine from heterogeneous pharmacologic databases. BMC Bioinform. 19, 517 (2018).
Chin Po, C., Susan Shur Fen, G. & Chi Chun, L. Toward differential diagnosis of autism spectrum upset utilizing multimodal behaviour descriptors and enforcement functions. Comput. Speech Lang. 56, 17–35 (2019).
Paolo, F. et al. Combining macula objective signs and diligent characteristics for age-related macular degeneration diagnosis: A instrumentality learning attack Retina. BMC Ophthalmol. 15, 10 (2015).
Benjamin, D. W. et al. Early recognition of epilepsy country candidates: A multicenter, instrumentality learning study. Acta Neurol. Scand. 144, 41–50 (2021).
Xia An, B., Wenyan, Z., Lou, L. & Zhaoxu, X. Detecting hazard cistron and pathogenic encephalon portion successful EMCI utilizing a caller GERF algorithm based connected encephalon imaging and familial data. IEEE J. Biomed. Health Inform. 25, 3019–3028 (2021).
Prashanth, R., Sumantra Dutta, R., Pravat, K. M. & Shantanu, G. High-accuracy detection of aboriginal Parkinson’s illness done multimodal features and instrumentality learning. Int. J. Med. Inf. 90, 13–21 (2016).
Ali, A.-M. et al. Machine learning for localizing epileptogenic-zone successful the temporal lobe: Quantifying the worth of multimodal clinical-semiology and imaging concordance. Front. Digital Health 3, 559103 (2021).
Baiying, L. et al. Assessment of liver fibrosis successful chronic hepatitis B via multimodal data. Neurocomputing 253, 169–176 (2017).
Larry, H. et al. Multimodal tensor-based method for integrative and continuous diligent monitoring during postoperative cardiac care. Artif. Intell. Med. 113, 102032 (2021).
Ivo, D. D. et al. Predictive large information analytics: A survey of Parkinson’s illness utilizing large, complex, heterogeneous, incongruent, multi-source and incomplete observations. PLoS One 11, e0157077 (2016).
Eleftherios, T., Ioannis, S., Apostolos, H. K. & Kostas, M. Deep radiotranscriptomics of non-small compartment lung carcinoma for assessing molecular and histology subtypes with a data-driven analysis. Diagnostics 11, 2383 (2021).
Hua, W. et al. Identifying illness delicate and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics information via sparse multimodal multitask learning. Bioinformatics 28, i127–36 (2012).
Xia, B., Xi, H., Yiming, X. & Hao, W. A caller CERNNE attack for predicting Parkinson’s Disease-associated genes and encephalon regions based connected multimodal imaging genetics data. Med. Image Anal. 67, 101830 (2021).
Vijay, H. et al. Predicting complications successful captious attraction utilizing heterogeneous objective data. IEEE Access 4, 7988–8001 (2016).
Wang, H., Li, Y., Khan, S. A. & Luo, Y. Prediction of bosom crab distant recurrence utilizing earthy connection processing and knowledge-guided convolutional neural network. Artif. Intell. Med. 110, 101977 (2020).
Zeng, Z. et al. Identifying bosom crab distant recurrences from physics wellness records utilizing instrumentality learning. J. Healthcare Inform. Res. 3, 283–299 (2019).
Kautzky, A. et al. Machine learning classification of ADHD and HC by multimodal serotonergic data. Transl. Psychiatry 10, 104 (2020).
Nhat Trung, D. et al. Distinct multivariate encephalon morphological patterns and their added predictive worth with cognitive and polygenic hazard scores successful intelligence disorders. NeuroImage: Clin. 15, 719–731 (2017).
Niha, B. et al. Radiogenomic investigation of hypoxia pathway is predictive of wide endurance successful Glioblastoma. Sci. Rep. 8, 7 (2018).
Jan, C. P. et al. Combining multimodal imaging and attraction features improves instrumentality learning-based prognostic appraisal successful patients with glioblastoma multiforme. Cancer Med. 8, 128–136 (2019).
Hao, Z. et al. Machine learning reveals multimodal MRI patterns predictive of isocitrate dehydrogenase and 1p/19q presumption successful diffuse low- and high-grade gliomas. J. Neurooncol 142, 299–307 (2019).
Namyong, P. et al. Predicting acute kidney wounded successful crab patients utilizing heterogeneous and irregular data. PLoS One 13, e0199839 (2018).
Wei Liang, T., Chee Kong, C., Sim Heng, O. & Alvin Choong Meng, N. Ensemble-based regression investigation of multimodal aesculapian information for osteopenia diagnosis. Expert Syst. Appl. 40, 811–819 (2013).
Sébastien, T., Yasser, I.-M., José María, M.-P., Alan, C. E. & Louis De, B. Defining a multimodal signature of distant sports concussions. Eur. J. Neurosci. 46, 1956–1967 (2017).
Gianluca, B. et al. Multimodal predictive modeling of endovascular attraction result for acute ischemic changeable utilizing machine-learning. Stroke 51, 3541–3551 (2020).
Yiming, X. et al. Explainable dynamic multimodal variational autoencoder for the prediction of patients with suspected cardinal precocious puberty. IEEE J. Biomed. Health Inform. 26, 1362–1373 (2021).
Alan, D. K. et al. Mixture exemplary model for traumatic encephalon wounded prognosis utilizing heterogeneous objective and result data. IEEE J. Biomed. Health Inform. 26, 1285–1296 (2021).
Casper, R. et al. Preoperative hazard stratification successful endometrial crab (ENDORISK) by a Bayesian web model: A improvement and validation study. PLoS Med. 17, e1003111 (2020)
Tommaso, G. et al. SARS-COV-2 comorbidity web and result successful hospitalized patients successful Crema, Italy. PLoS One 16, e0248498 (2021).
Huan, Q. et al. Machine-learning radiomics to foretell aboriginal recurrence successful perihilar cholangiocarcinoma aft curative resection. Liver Int. 41, 837–850 (2021).
Ramon, C. et al. Alzheimer’s illness hazard appraisal utilizing large-scale instrumentality learning methods. PLoS One 8, e77949 (2013).
Aleksei, T. et al. Multimodal instrumentality learning-based genu osteoarthritis progression prediction from plain radiographs and objective data. Sci. Rep. 9, 20038 (2019).
Michael, J. D. et al. Development and validation of a caller automated Gleason people and molecular illustration that specify a highly predictive prostate crab progression algorithm-based test. Prostate Cancer Prostatic Dis. 21, 594–603 (2018).
Perotte, A., Ranganath, R., Hirsch, J. S., Blei, D. & Elhadad, N. Risk prediction for chronic kidney illness progression utilizing heterogeneous physics wellness grounds information and clip bid analysis. J. Am. Med. Inf. Assoc. 22, 872–880 (2015).
Lei, Y., Yalin, W., Paul, M. T., Vaibhav, A. N. & Jieping, Y. Multi-source diagnostic learning for associated investigation of incomplete aggregate heterogeneous neuroimaging data. Neuroimage 61, 622–632 (2012).
Yanbo, X., Siddharth, B., Shriprasad, R. D., Kevin, O. M. & Jimeng, S. RAIM: Recurrent attentive and intensive exemplary of multimodal diligent monitoring data. In Proceedings of the ACM SIGKDD International Conference connected Knowledge Discovery and Data Mining 2565–2573 (Association for Computing Machinery, 2018).
Yixue, H., Mohd, U., Jun, Y., Hossain, M. S. & Ahmed, G. Recurrent convolutional neural web based multimodal illness hazard prediction. Future Gener. Computer Syst. 92, 76–83 (2019).
Shinichi, G., et al. Artificial intelligence-enabled afloat automated detection of cardiac amyloidosis utilizing electrocardiograms and echocardiograms. Nat. Commun. 12, 2726 (2021).
Rui, Y. et al. Richer fusion web for bosom crab classification based connected multimodal data. BMC Med. Inform. Decis. Mak. 21, 134 (2021).
Li, Y., Wang, H. & Luo, Y. A examination of pre-trained vision-and-language models for multimodal practice learning crossed aesculapian images and reports. 2020 IEEE Int. Conf. Bioinform. Biomedicine (BIBM) 2020 1999–2004 (2020).
Sara Bersche, G. et al. A instrumentality learning exemplary to foretell the hazard of 30-day readmissions successful patients with bosom failure: A retrospective investigation of physics aesculapian records data. BMC Med. Inform. Decis. Mak. 18, 44 (2018).
Chowdhury, S., Zhang, C., Yu, P. S. & Luo, Y. Mixed pooling multi-view attraction autoencoder for practice learning successful healthcare. Preprint astatine https://arxiv.org/abs/1910.06456 (2019).
Ilan, S. et al. An unsupervised learning attack to place caller signatures of wellness and illness from multimodal data. Genome Med. 12, 7 (2020).
Chowdhury, S., Zhang, C., Yu, P. S. & Luo, Y. Med2Meta: Learning representations of aesculapian concepts with meta-embeddings. HEALTHINF 2020, 369–376 (2020).
Subramanian V, Do MN, Syeda-Mahmood T. Multimodal fusion of imaging and genomics for lung crab recurrence prediction, IEEE 17th International Symposium connected Biomedical Imaging (ISBI), 804–808, (2020).
Michele, D. et al. Combining heterogeneous information sources for neuroimaging based diagnosis: Re-weighting and selecting what is important. Neuroimage 195, 215–231 (2019).
Yiwen, M., William, S., Michael, K. O. & Corey, W. A. Bidirectional practice learning from transformers utilizing multimodal physics wellness grounds information to foretell depression. IEEE J. Biomed. Health Inform. 25, 3121–3129 (2021).
Xing, T. et al. Elaboration of a multimodal MRI-based radiomics signature for the preoperative prediction of the histological subtype successful patients with non-small-cell lung cancer. BioMed. Engineering Online 19, 5 (2020).
Kathleen, C. F., Kristina Lundholm, F., Marie, E., Fredrik, Ö & Dimitrios, K. Predicting MCI presumption from multimodal connection information utilizing cascaded classifiers. Front. Aging Neurosci. 11, 205 (2019).
Amir Hossein, Y. et al. Multimodal intelligence wellness investigation successful societal media. PLoS One 15, e0226248 (2020).
Ye, J., Yao, L., Shen, J., Janarthanam, R. & Luo, Y. Predicting mortality successful critically sick patients with diabetes utilizing instrumentality learning and objective notes. BMC Med Inf. Decis. Mak. 20, 1–7 (2020).
Navodini, W., Mobarakol, I. & Hongliang, R. Radiogenomics exemplary for wide endurance prediction of glioblastoma. Med Biol. Eng. Comput 58, 1767–1777 (2020).
Solale, T. et al. A distributed multitask multimodal attack for the prediction of Alzheimer’s illness successful a longitudinal study. Neuroimage 206, 116317 (2020).
Luo, Y. et al. Integrating hypertension phenotype and genotype with hybrid non-negative matrix factorization. Bioinformatics 35, 1395–403 (2019).
Jae Hyun, Y., Johanna Inhyang, K., Bung Nyun, K. & Bumseok, J. Exploring diagnostic features of attention-deficit/hyperactivity disorder: Findings from multi-modal MRI and campaigner familial data. Brain Imaging Behav. 14, 2132–2147 (2020).
Chao, T., Baoyu, L., Jun, L. & Zhigao, Z. A Deep automated skeletal bony property appraisal exemplary with heterogeneous features learning. J. Med. Syst. 42, 249 (2018).
Cheng, C. et al. Improving protein–protein interactions prediction accuracy utilizing XGBoost diagnostic enactment and stacked ensemble classifier. Comput. Biol. Med. 123 (2020).
Xueyi, Z. et al. Deep learning radiomics tin foretell axillary lymph node presumption successful early-stage bosom cancer. Nat. Commun. 11, 1236 (2020).
Juan Camilo, V.-C. et al. Multimodal appraisal of Parkinson’s disease: A heavy learning approach. IEEE J. Biomed. Health Inform. 23, 1618–1630 (2019).
Ping, Z. et al. Deep-learning radiomics for favoritism conversion of Alzheimer’s illness successful patients with mild cognitive impairment: A survey based connected 18F-FDG PET imaging. Front. Aging Neurosci. 13, (2021).
Shin, J., Li, Y. & Luo, Y. Early prediction of mortality successful captious attraction mounting successful sepsis patients utilizing structured features and unstructured objective notes. In 2021 IEEE International Conference connected Bioinformatics and Biomedicine (BIBM) 2885–2890 (IEEE, 2021).
Jayachitra, V. P., Nivetha, S., Nivetha, R. & Harini, R. A cognitive IoT-based model for effectual diagnosis of COVID-19 utilizing multimodal data. Biomed. Signal Processing Control 70, 102960 (2021).
Thomas, L. et al. An explainable multimodal neural web architecture for predicting epilepsy comorbidities based connected administrative claims data. Front. Artificial Intelligence 4, 610197 (2021).
Alan Baronio, M., Carla Diniz Lopes, B. & Silvio Cesar, C. Computer-aided diagnosis of hepatocellular carcinoma fusing imaging and structured wellness data. Health Inform. Sci. Syst. 9, 20 (2021)
Janani, V., Li, T., Hamid Reza, H. & May, D. W. Multimodal heavy learning models for aboriginal detection of Alzheimer’s illness stage. Sci. Rep. 11, 3254 (2021).
Cam Hao, H. et al. Bimodal learning via trilogy of skip-connection heavy networks for diabetic retinopathy hazard progression identification. Int. J. Med. Inform. 132, 103926 (2019).
Yucheng, T. et al. Prediction of benignant II diabetes onset with computed tomography and physics aesculapian records. Lect. Notes Comput. Sci. 12445, 13–23 (2020).
Rui, Y. et al. Integration of multimodal information for bosom crab classification utilizing a hybrid heavy learning method. Lect. Notes Computer Sci. 11643, 460–469 (2019).
Batuhan, B. & Mehmet, T. Improving objective result predictions utilizing convolution implicit aesculapian entities with multimodal learning. Artif. Intell. Med. 117, 102112 (2021).
Esra, Z. et al. Multimodal fusion strategies for result prediction successful Stroke. In HEALTHINF 2020 - 13th International Conference connected Health Informatics, Proceedings; Part of 13th International Joint Conference connected Biomedical Engineering Systems and Technologies, BIOSTEC 2020 421–428 (2020).
Rimma, P. et al. Learning probabilistic phenotypes from heterogeneous EHR data. J. Biomed. Inform. 58, 156–165 (2015).
Leon, M. A. et al. Modeling longitudinal imaging biomarkers with parametric Bayesian multi-task learning. Hum. Brain Mapp. 40, 3982–4000 (2019).
Paris Alexandros, L. et al. Heterogeneity and classification of caller onset psychosis and depression: A multimodal instrumentality learning approach. Schizophr. Bull. 47, 1130–1140 (2021).
Nikolaos, K. et al. Prediction models of functional outcomes for individuals successful the objective high-risk authorities for psychosis oregon with recent-onset depression: A multimodal, multisite instrumentality learning analysis. JAMA Psychiatry 75, 1156–1172 (2018).
Dongdong, S., Minghui, W. & Ao, L. A multimodal heavy neural web for quality bosom crab prognosis prediction by integrating multi-dimensional data. IEEE/ACM Trans. Comput Biol. Bioinform. 16, 841–850 (2019).
Karen, S. A. et al. A machine-learning model for robust and reliable prediction of short- and semipermanent attraction effect successful initially antipsychotic-naïve schizophrenia patients based connected multimodal neuropsychiatric data. Translational Psychiatry 10, 276 (2020).
Sun, M. et al. Early prediction of acute kidney wounded successful captious attraction mounting utilizing objective notes and structured multivariate physiological measurements. Stud. Health Technol. Inf. 264, 368–372 (2019).
Dennis, S. R., Simuni, T. & Luo, Y. A predictive exemplary for Parkinson’s illness reveals campaigner cistron sets for progression subtype. 2020 IEEE Int. Conf. Bioinforma. Biomedicine (BIBM) 2020, 417–420 (2020).
Ming, X. et al. Accurately differentiating COVID-19, different viral infection, and steadfast individuals utilizing multimodal features via precocious fusion learning. J. Med. Internet Res. 23 (2021).
Min, C., Yixue, H., Kai, H., Lin, W. & Lu, W. Disease prediction by instrumentality learning implicit large information from healthcare communities. IEEE Access 5, 8869–8879 (2017).
Keyang, X. et al. Multimodal instrumentality learning for automated ICD coding. Proc. Mach. Learn. Res. 106, 1–17 (2019).
Liuqing, Y. et al. Deep learning based multimodal progression modeling for Alzheimer’s disease. Stat. Biopharma. Res. 13, 337–343 (2021).
Peng, L. et al. A radiogenomics signature for predicting the objective result of bladder urothelial carcinoma. Eur. Radio. 30, 547–557 (2020).
Md Sirajus, S. et al. Multimodal spatio-temporal heavy learning attack for neonatal postoperative symptom assessment. Comput. Biol. Med. 129, 104150 (2021).
Jian, X. et al. Multimodal instrumentality learning utilizing ocular fields and peripapillary circular OCT scans successful detection of glaucomatous optic neuropathy. Ophthalmology 129, 171–180 (2021).
Dai, Y., Yiqi, Z., Yang, W., Wenpu, Z. & Xiaoming, H. Auxiliary diagnosis of heterogeneous information of Parkinson’s illness based connected improved convolution neural network. Multimed. Tools Appl. 79, 24199–24224 (2020).
Makoto, N. et al. Accessory pathway investigation utilizing a multimodal heavy learning model. Sci. Rep. 11, 8045 (2021).
Wenhuan, Z., Anupam, G. & Daniel, H. H. On the exertion of precocious instrumentality learning methods to analyse enhanced, multimodal information from persons infected with covid-19. Computation 9, 1–15 (2021).
Jeremy, A. T., Kit, M. L. & Marta, I. G. Multi-dimensional predictions of psychotic symptoms via instrumentality learning. Hum. Brain Mapp. 41, 5151–5163 (2020).
Hossam, F., Maria, H., Mohammad, F., Haya, E. & Alaa, A. An intelligent multimodal aesculapian diagnosis strategy based connected patients’ aesculapian questions and structured symptoms for telemedicine. Inform. Med. Unlocked 23, 100513 (2021).
Md Ashad, A. et al. A kernel instrumentality method for detecting higher bid interactions successful multimodal datasets: Application to schizophrenia. J. Neurosci. Methods 309, 161–174 (2018).
Luo, Y. et al. A multidimensional precision medicine attack identifies an autism subtype characterized by dyslipidemia. Nat. Med. 26, 1375–1379 (2020).
Shaker, E.-S., Jose, M. A., Islam, S. M. R., Ahmad, M. S., Kyung Sup, K. A multilayer multimodal detection and prediction exemplary based connected explainable artificial quality for Alzheimer’s disease. Sci. Rep. 11, 2660 (2021).
Shih Cheng, H., Anuj, P., Roham, Z., Imon, B. & Matthew, P. L. Multimodal fusion with heavy neural networks for leveraging CT imaging and physics wellness record: A case-study successful pulmonary embolism detection. Sci. Rep. 10, 22147 (2020).
Yao, L., Mao, C. & Luo, Y. Clinical substance classification with rule-based features and knowledge-guided convolutional neural networks. BMC Med. Inf. Decis. Mak. 19, 71 (2019).
Velupillai, S. et al. Using objective Natural Language Processing for wellness outcomes research: Overview and actionable suggestions for aboriginal advances. J. Biomed. Inf. 88, 11–19 (2018).
Luo, Y., Uzuner, Ö. & Szolovits, P. Bridging semantics and syntax with graph algorithms—state-of-the-art of extracting biomedical relations. Brief. Bioinform. 18, 160–178 (2016).
Zeng, Z., Deng, Y., Li, X., Naumann, T. & Luo, Y. Natural connection processing for EHR-based computational phenotyping. IEEE/ACM Trans. Comput Biol. Bioinform. 16, 139–153 (2018).
Nikolaos, K. et al. Multimodal instrumentality learning workflows for prediction of psychosis successful patients with objective high-risk syndromes and recent-onset depression. JAMA Psychiatry 78, 195–209 (2021).
Petersen, R. C. et al. Alzheimer’s illness neuroimaging inaugural (ADNI): Clinical characterization. Neurology 74, 201–209 (2010).
Weinstein, J. N. et al. The crab genome atlas pan-cancer investigation project. Nat. Genet. 45, 1113 (2013).
Christopher, J. K., Alan, K., Mustafa, S., Greg, C. & Dominic, K. Key challenges for delivering objective interaction with artificial intelligence. BMC Med. 17, 195 (2019).
Michael, A. M. et al. Results of the 2016 International Skin Imaging Collaboration International Symposium connected Biomedical Imaging challenge: Comparison of the accuracy of machine algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images. J. Am. Acad. Dermatol. 78, 270–7.e1 (2018).
Jannin, P. G. C. & Gibaud, B. Medical Applications of NDT Data Fusion (Springer, 2001).
Guang, Y., Qinghao, Y. & Jun, X. Unbox the black-box for the aesculapian explainable AI via multi-modal and multi-centre information fusion: A mini-review, 2 showcases and beyond. Inf. Fusion 77, 29–52 (2022).
Zhang, Z., & Sejdić, E., Radiological images and instrumentality learning: trends, perspectives, and prospects. Comput. Biol. Med. 108, 354–370 (2019).
David, L., Enrico, C., Jessica, C., Parina, S. & Farah, M. How instrumentality learning is embedded to enactment clinician determination making: An investigation of FDA-approved aesculapian devices. BMJ Health Care Inform. 28, e100301 (2021).
Luo, Y., Szolovits, P., Dighe, A. S. & Baron, J. M. Using instrumentality learning to foretell laboratory trial results. Am. J. Clin. Pathol. 145, 778–788 (2016).
Thakur, S., Choudhary, J. & Singh, D. P. Systems 435–443 (Springer, 2021).
Luo, Y., Szolovits, P., Dighe, A. S. & Baron, J. M. 3D-MICE: Integration of cross-sectional and longitudinal imputation for multi-analyte longitudinal objective data. J. Am. Med. Inform. Assoc. (JAMIA) 25, 645–653 (2017).
Xue, Y., Klabjan, D. & Luo, Y. Mixture-based aggregate imputation exemplary for objective information with a temporal dimension. 2019 IEEE International Conference connected Big Data (Big Data) 2019 245–252 (IEEE, 2019).
Cao, W. et al. Brits: Bidirectional Recurrent Rmputation for Time Series. NeurIPS, 31 (2018).
Luo, Y. Evaluating the authorities of the creation successful missing information imputation for objective data. Brief. Bioinform. 23, bbab489 (2022).
Zhao, Q., Adeli, E. & Pohl, K. M. Training confounder-free heavy learning models for aesculapian applications. Nat. Commun. 11, 1–9 (2020).
Luo, Y. & Mao, C. ScanMap: Supervised confounding alert non-negative matrix factorization for polygenic hazard modeling. In Machine Learning for Healthcare Conference; 2020: PMLR 27–45 (2020).
Kalavathy, R. & Suresh, R. M. Pharmacovigilance from physics aesculapian records to study adverse events. J. Chem. Pharm. Sci. 2015, 188–191 (2015).
Luo, Y. et al. Natural connection processing for EHR-based pharmacovigilance: A structured review. Drug Saf. https://doi.org/10.1007/s40264-017-0558-6 (2017).
Segura Bedmar, I., Martínez, P. & Herrero Zazo, M. Semeval-2013 task 9: Extraction of drug–drug interactions from biomedical texts (ddiextraction 2013). 2013: Association for Computational Linguistics (2013).
Hammann, F. & Drewe, J. Data mining for imaginable adverse drug–drug interactions. Expert Opin. Drug Metab. Toxicol. 10, 665–671 (2014).
Donna, M. F. et al. American geriatrics nine 2019 updated AGS beers criteria for perchance inappropriate medicine usage successful older adults. J. Am. Geriatr. Soc. 67, 674–694 (2019).
Obermeyer, Z., Powers, B., Vogeli, C. & Mullainathan, S. Dissecting radical bias successful an algorithm utilized to negociate the wellness of populations. Science 366, 447–53. (2019).
Wang, H. et al. Using instrumentality learning to integrate sociobehavioral factors successful predicting cardiovascular-related mortality risk. Stud. Health Technol. Inf. 264, 433–437 (2019).
Christof, E. Open root bundle successful industry. IEEE Softw. 25, 52–53 (2008).
Robert, M. S. Why make open-source software? The relation of non-pecuniary benefits, monetary rewards, and open-source licence type. Oxf. Rev. Economic Policy 23, 605–619 (2007).
Luo, Y., Wunderink, R. G. & Lloyd-Jones, D. Proactive vs reactive instrumentality learning successful wellness care: Lessons from the COVID-19 pandemic. JAMA 327, 623–624 (2022).
Ke, G. et al. LightGBM: A highly businesslike gradient boosting determination tree. Adv. Neural Inf. Process Syst. 30 (2017).
Derara Duba, R., Taye Girma, D., Achim, I. & Worku Gachena, N. Diagnosis of diabetes mellitus utilizing gradient boosting instrumentality (Lightgbm). Diagnostics 11, 1714 (2021).
Xiaolei, S., Mingxi, L. & Zeqian, S. A caller cryptocurrency terms inclination forecasting exemplary based connected LightGBM. Financ. Res. Lett. 32, 101084 (2020).
Dongzi, J., Yiqin, L., Jiancheng, Q., Zhe, C. & Zhongshu, M. SwiftIDS: Real-time intrusion detection strategy based connected LightGBM and parallel intrusion detection mechanism. Comput. Secur. 97, 101984 (2020).
Wilkinson, M. D. et al. The FAIR guiding principles for technological information absorption and stewardship. Nature 3, 160018 (2016).
Weissler, E. H., et al. The relation of instrumentality learning successful objective research: Transforming the aboriginal of grounds generation. Trials 22, 537 (2021).
Pratik, S. et al. Artificial quality and instrumentality learning successful objective development: A translational perspective. npj Digital Med. 2, 69 (2019).
Inna, K. & Simeon, S. Interpretability of instrumentality learning solutions successful nationalist healthcare: The CRISP-ML approach. Front. Big Data 4, 660206 (2021).
Marx, V. Method of the Year: Spatially resolved transcriptomics. Nat. Methods 18, 9–14 (2021).
Zeng, Z., Li, Y., Li, Y. & Luo, Y. Statistical and instrumentality learning methods for spatially resolved transcriptomics information analysis. Genome Biol. 23, 1–23. (2022).
Longo, S. K., Guo, M. G., Ji, A. L. & Khavari, P. A. Integrating single-cell and spatial transcriptomics to elucidate intercellular insubstantial dynamics. Nat. Rev. Genet. 22, 627–644 (2021).
PRISMA hold for scoping reviews (PRISMA-ScR): Checklist and explanation. Ann. Intern. Med. 169 467–473 (2018).
English (US)