Combining structured and unstructured data for predictive models: a deep learning approach
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Abstract
BACKGROUND: The broad adoption of electronic health records (EHRs) provides great opportunities to conduct health care research and solve various clinical problems in medicine. With recent advances and success, methods based on machine learning and deep learning have become increasingly popular in medical informatics. However, while many research studies utilize temporal structured data on predictive modeling, they typically neglect potentially valuable information in unstructured clinical notes. Integrating heterogeneous data types across EHRs through deep learning techniques may help improve the performance of prediction models. METHODS: In this research, we proposed 2 general-purpose multi-modal neural network architectures to enhance patient representation learning by combining sequential unstructured notes with structured data. The proposed fusion models leverage document embeddings for the representation of long clinical note documents and either convolutional neural network or long short-term memory networks to model the sequential clinical notes and temporal signals, and one-hot encoding for static information representation. The concatenated representation is the final patient representation which is used to make predictions. RESULTS: We evaluate the performance of proposed models on 3 risk prediction tasks (i.e. in-hospital mortality, 30-day hospital readmission, and long length of stay prediction) using derived data from the publicly available Medical Information Mart for Intensive Care III dataset. Our results show that by combining unstructured clinical notes with structured data, the proposed models outperform other models that utilize either unstructured notes or structured data only. CONCLUSIONS: The proposed fusion models learn better patient representation by combining structured and unstructured data. Integrating heterogeneous data types across EHRs helps improve the performance of prediction models and reduce errors.
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The dataset is split into 0.70-0.15-0.15 train-validation-test sets with no overlap between sets by patient ID and admission ID.
“For evaluation, we use a 0.70-0.15-0.15 train-validation-test split following the previous work [13]. However, we equalized the size of the validation set and the test set to get a better estimation of test performance on the validation set. We split the dataset according to patient ID and admission ID so that there's no overlap between training, validation, and test sets.”
From Multimodal risk prediction with physiological signals, medical images and clinical notes (2024) · cites this paper as [13]
The cited paper’s Results and discussion > Experiment setup says it used 70% of the data for training, 10% for validation, and 20% for testing—not the cited 70%-15%-15% split. The citing sentence presents equal validation and test sizes and the patient-ID/admission-ID separation as its own procedures; the cited paper’s passage does not establish those procedures. (LLM, conf=0.91)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredMachine learning and deep learning techniques hold great promise in deriving meaningful insights from EHR, which can aid in accurately predicting clinical outcomes, such as mortality and readmission.
“They hold great promise in deriving meaningful insights from EHR, which can aid in accurately predicting clinical outcomes, such as mortality [13] and readmission [13], [14].”
From Multimodal risk prediction with physiological signals, medical images and clinical notes (2024) · cites this paper as [13]
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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.01703 · Cited by 31,473 · see citing works
Distributed Representations of Sentences and DocumentsPreprint
Le, Q. V., & Mikolov, T. (2014) · International Conference on Machine Learning · doi:10.48550/arxiv.1405.4053 · Cited by 10,307 · see citing works
Santos, A. G., da Rocha, G. O., & de Andrade, J. B. (2019) · Scientific Reports · doi:10.1038/s41598-018-37186-2 · Cited by 8,495 · see citing works
MIMIC-III, a freely accessible critical care database
Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L.-W. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., & Mark, R. G. (2016) · Scientific Data · doi:10.1038/sdata.2016.35 · Cited by 8,141 · see citing works
A unified architecture for natural language processing: deep neural networks with multitask learning
Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058 · see citing works
Software Framework for Topic Modelling with Large Corpora
Rek, R. R. U., & Sojka, P. (2010) · doi:10.13140/2.1.2393.1847 · Cited by 3,766 · see citing works
Scalable and accurate deep learning with electronic health records
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0029-1 · Cited by 2,648 · see citing works
Scalable and accurate deep learning with electronic health records.
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · eScholarship (California Digital Library) · Cited by 2,242 · see citing works
Zimmerman, J. E., Kramer, A. A., McNair, D. S., & Malila, F. M. (2006) · Critical Care Medicine · doi:10.1097/01.ccm.0000215112.84523.f0 · Cited by 1,764 · see citing works
Risk Prediction Models for Hospital Readmission
Kansagara, D. L., Englander, H. L., Salanitro, A. H., Kagen, D. I., Theobald, C. N., Freeman, M., & Kripalani, S. B. (2011) · JAMA · doi:10.1001/jama.2011.1515 · Cited by 1,712 · see citing works
Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission
Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2788613 · Cited by 1,685 · see citing works
Deng, L., Hinton, G. E., & Kingsbury, B. E. D. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639344 · Cited by 1,222 · see citing works
Martinón‐Torres, M., Li, X. J., Xia, Y., Benzonelli, A., Bevan, A., Ma, S., Huang, J., Wang, L., Lan, D., Liu, J., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-40613-7 · Cited by 1,090 · see citing works
Simulation physiologischer Regelkreise mit der objektorientierten Modellbibliothek “HumanLib”
Brunberg, A., Spillner, J. W., Autschbach, R., & Abel, D. (2011) · at - Automatisierungstechnik · doi:10.1524/auto.2011.0951 · Cited by 1,008 · see citing works
van Walraven, C., Dhalla, I. A., Bell, C. M., Etchells, E. E., Stiell, I. G., Zarnke, K. B., Austin, P. C., & Forster, A. J. (2010) · Canadian Medical Association Journal · doi:10.1503/cmaj.091117 · Cited by 966 · see citing works
Multitask learning and benchmarking with clinical time series data
Harutyunyan, H., Khachatrian, H., Kale, D. C., Ver Steeg, G., & Galstyan, A. (2019) · Scientific Data · doi:10.1038/s41597-019-0103-9 · Cited by 958 · see citing works
Deep Learning for Content-Based Image Retrieval
Wan, J., Wang, D., Hoi, S. C. H., Wu, P., Zhu, J., Zhang, Y., & Li, J. (2014) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2647868.2654948 · Cited by 814 · see citing works
Donzé, J., Aujesky, D., Williams, D., & Schnipper, J. L. (2013) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2013.3023 · Cited by 553 · see citing works
Benchmarking deep learning models on large healthcare datasets
Purushotham, S., Meng, C., Che, Z., & Liu, Y. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.04.007 · Cited by 406 · see citing works
A comparison of models for predicting early hospital readmissions
Futoma, J., Morris, J. D., & Lucas, J. E. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2015.05.016 · Cited by 284 · see citing works
Predicting death and readmission after intensive care discharge
Campbell, A. J., Cook, J. A., ADEY, G. D., & Cuthbertson, B. H. (2008) · British Journal of Anaesthesia · doi:10.1093/bja/aen069 · Cited by 172 · see citing works
Min, X., Yu, B., & Wang, F. (2019) · Scientific Reports · doi:10.1038/s41598-019-39071-y · Cited by 170 · see citing works
Hu, Z., Melton, G. B., Arsoniadis, E. G., Wang, Y., Kwaan, M. R., & Simon, G. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.03.009 · Cited by 101 · see citing works
Liu, V., Kipnis, P., Gould, M. K., & Escobar, G. J. (2010) · Medical Care · doi:10.1097/mlr.0b013e3181e359f3 · Cited by 87 · see citing works
Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal LearningPreprint
Jin, M., Bahadori, M. T., Colak, A., Bhatia, P., Celikkaya, B., Bhakta, R., Senthivel, S., Khalilia, M. A., Navarro, D., Zhang, B., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.12276 · Cited by 58 · see citing works
What's in a Note? Unpacking Predictive Value in Clinical Note Representations.
Boag, W., Doss, D., Naumann, T., & Peter Szolovits, B. (2018) · Europe PMC (PubMed Central) · Cited by 55 · see citing works
Predictive modeling in urgent care: a comparative study of machine learning approaches
Tang, F., Xiao, C., Wang, F., & Zhou, J. (2018) · JAMIA Open · doi:10.1093/jamiaopen/ooy011 · Cited by 48 · see citing works
Deep EHR: Chronic Disease Prediction Using Medical NotesPreprint
Liu, J., Zhang, Z., & Razavian, N. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.04928 · Cited by 48 · see citing works
Effectiveness of SAPS III to predict hospital mortality for post-cardiac arrest patients
Bisbal, M., Jouve, É., Papazian, L., de Bourmont, S., Perrin, G., Éon, B., & Gainnier, M. (2014) · Resuscitation · doi:10.1016/j.resuscitation.2014.03.302 · Cited by 46 · see citing works
Neural Document Embeddings for Intensive Care Patient Mortality PredictionPreprint
Grnarova, P., Schmidt, F., Hyland, S. L., & Eickhoff, C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.00467 · Cited by 46 · see citing works
Patient representation learning and interpretable evaluation using clinical notes
Sushil, M., Šuster, S., Luyckx, K., & Daelemans, W. M. P. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.06.016 · Cited by 45 · see citing works
Interpretable Topic Features for Post-ICU Mortality Prediction.
Luo, Y.-F., & Rumshisky, A. (2016) · PubMed Central · Cited by 15 · see citing works
Flagged first, then by citations
1 of the 35 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- [1]Henry J Pylypchuk Y Searcy T Patel V Adoption of electronic health record systems among US non-federal acute care hospitals: 2008–2015 ONC Data Brief 2016 35 1 9 40127322
- [13]Ghassemi M, Naumann T, Joshi R, Rumshisky A. Topic models for mortality modeling in intensive care units. In: ICML machine learning for clinical data analysis workshop; 2012. p. 1–4.
Cited by
Sorted by citations
Multimodal deep learning for biomedical data fusion: a review
Stahlschmidt, S. R., Ulfenborg, B., & Synnergren, J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab569 · Cited by 766 · see citing works
“[64] proposed a CNN- and a RNN-based fusion model that take temporal signals, sequential clinical notes as well as static demographic and admission data as input to the different branches.”
MethodMultimodal machine learning in precision health: A scoping review
Kline, A., Wang, H., Li, Y., Dennis, S., Hutch, M., Xu, Z., Wang, F., Cheng, F., & Luo, Y. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00712-8 · Cited by 528 · see citing works
Synthetic data generation for tabular health records: A systematic review
Hernandez, M., Epelde, G., Alberdi, A., Cilla, R., & Rankin, D. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.053 · Cited by 278 · see citing works
Time Series Prediction Using Deep Learning Methods in Healthcare
Morid, M. A., Sheng, O. R. L., & Dunbar, J. (2022) · ACM Transactions on Management Information Systems · doi:10.1145/3531326 · Cited by 256 · see citing works
A systematic review of the prediction of hospital length of stay: Towards a unified framework
Stone, K., Reyer Zwiggelaar, A., Jones, P., & Parthaláin, N. M. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000017 · Cited by 250 · see citing works
Digital Mental Health Challenges and the Horizon Ahead for Solutions
Balcombe, L., & Leo, D. D. (2021) · JMIR Mental Health · doi:10.2196/26811 · Cited by 197 · see citing works
A narrative review on the validity of electronic health record-based research in epidemiology
Gianfrancesco, M. A., & Goldstein, N. D. (2021) · BMC Medical Research Methodology · doi:10.1186/s12874-021-01416-5 · Cited by 182 · see citing works
Theranostics and artificial intelligence: new frontiers in personalized medicine
Bilgin, G. B., Bilgin, C., Burkett, B. J., Orme, J. J., Childs, D. S., Thorpe, M. P., Halfdanarson, T. R., Johnson, G. B., Kendi, A. T., & Sartor, O. A. (2024) · Theranostics · doi:10.7150/thno.94788 · Cited by 151 · see citing works
Automated Machine Learning for Healthcare and Clinical Notes Analysis
Mustafa, A., & Azghadi, M. R. (2021) · Computers · doi:10.3390/computers10020024 · Cited by 131 · see citing works
Deep leaning in food safety and authenticity detection: An integrative review and future prospects
Wang, Y., Gu, H., Yin, X., Geng, T., Long, W., Fu, H., & She, Y. (2024) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2024.104396 · Cited by 115 · see citing works
A survey on clinical natural language processing in the United Kingdom from 2007 to 2022
Wu, H., Wang, M., Wu, J., Francis, F., Chang, Y.-H., Shavick, A., Dong, H., Poon, M. T. C., Fitzpatrick, N., Levine, A. P., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00730-6 · Cited by 101 · see citing works
Multimodal deep learning approaches for single-cell multi-omics data integration
Athaya, T., Ripan, R. C., Li, X., & Hu, H. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad313 · Cited by 94 · see citing works
Piccialli, F., Giampaolo, F., Prezioso, E., Camacho, D., & Acampora, G. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.03.004 · Cited by 87 · see citing works
Use of unstructured text in prognostic clinical prediction models: a systematic review
Seinen, T. M., Fridgeirsson, E. A., Ioannou, S., Jeannetot, D. Y. B., John, L. H., Kors, J. A., Markus, A. F., Pera, V., Rekkas, A., Williams, R. D., et al. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac058 · Cited by 80 · see citing works
Syed, M., Syed, S., Sexton, K. W., Syeda, H. B., Garza, M. Y., Zozus, M. N., Syed, F., Begum, S., Syed, A. U., Sanford, J. A., et al. (2021) · Informatics · doi:10.3390/informatics8010016 · Cited by 68 · see citing works
Best practices in the real-world data life cycle
Zhang, J., Symons, J., Agapow, P., Teo, J., Paxton, C. A., Abdi, J., Mattie, H., Davie, C., Torres, A. Z., Folarin, A., et al. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000003 · Cited by 68 · see citing works
Liu, J., Capurro, D., Nguyen, A., & Verspoor, K. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104466 · Cited by 66 · see citing works
Natural Language Processing Applications for Computer-Aided Diagnosis in Oncology
Li, C., Zhang, Y., Weng, Y., Wang, B., & Li, Z. (2023) · Diagnostics · doi:10.3390/diagnostics13020286 · Cited by 63 · see citing works
Multimodal risk prediction with physiological signals, medical images and clinical notes
Wang, Y., Yin, C., & Zhang, P. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e26772 · Cited by 54 · see citing works
“Zhang et al. [13] integrated time series variables with unstructured clinical notes in MIMIC-III to perform predictive modeling tasks, using LSTM and CNN for sequential feature extraction.”
BackgroundMethodInfluentialTsietso, D., Yahya, A., Samikannu, R., Tariq, M. U., Babar, M. A., Qureshi, B., & Koubâa, A. (2023) · IEEE Access · doi:10.1109/access.2023.3280422 · Cited by 51 · see citing works
Tsai, M., Chen, K., & Chen, P. (2025) · Journal of the American Heart Association · doi:10.1161/jaha.124.036946 · Cited by 53 · see citing works
Lybarger, K., Dobbins, N. J., Long, R., Singh, A., Wedgeworth, P., Uzuner, Ö., & Yetisgen, M. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad073 · Cited by 48 · see citing works
Chiu, C., Wu, C.-M., Chien, T.-N., Kao, L., Li, C., & Chu, C.-M. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20054340 · Cited by 45 · see citing works
Conformal Prediction: A Data Perspective
Zhou, X., Chen, B., Gui, Y., & Cheng, L. (2025) · ACM Computing Surveys · doi:10.1145/3736575 · Cited by 55 · see citing works
Taha, M., & Morren, J. A. (2023) · Muscle & Nerve · doi:10.1002/mus.28023 · Cited by 41 · see citing works
“Note Bloat” impacts deep learning-based NLP models for clinical prediction tasks
Liu, J., Daniel Capurro, N., Nguyen, A. N., & Verspoor, K. M. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104149 · Cited by 38 · see citing works
Bhuiyan, M. S. M., Rafi, M. A., Rodrigues, G. N., Mir, M. N. H., Ishraq, A., Mridha, M. F., & Shin, J. (2025) · Array · doi:10.1016/j.array.2025.100390 · Cited by 56 · see citing works
Ingwersen, E. W., Stam, W. T., Meijs, B. J. V., Roor, J., Besselink, M. G., Koerkamp, B. G., de Hingh, I. H. J. T., van Santvoort, H. C., Stommel, M. W. J., & Daams, F. D. (2023) · Surgery · doi:10.1016/j.surg.2023.03.012 · Cited by 39 · see citing works
Tang, S., Tariq, A., Dunnmon, J., Sharma, U., Elugunti, P., Rubin, D. L., Patel, B. N., & Banerjee, I. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3236888 · Cited by 37 · see citing works
González-Castro, L., Chávez, M., Duflot, P., Bleret, V., Martin, A. G., Zobel, M., Nateqi, J., Lin, S. M., Pazos-Arias, J. J., Fiol, G. D., et al. (2023) · Cancers · doi:10.3390/cancers15102741 · Cited by 37 · see citing works
Cardiovascular Care Innovation through Data-Driven Discoveries in the Electronic Health Record
Dhingra, L. S., Shen, M., Mangla, A., & Khera, R. (2023) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2023.06.104 · Cited by 40 · see citing works
Identification of piRNA disease associations using deep learning
Ali, S. D., Tayara, H., & Chong, K. T. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.02.026 · Cited by 37 · see citing works
Lyu, W., Dong, X., Wong, R., Zheng, S., Abell-Hart, K., Wang, F., & Chen, C. (2022) · arXiv · doi:10.48550/arxiv.2208.10240 · Cited by 46 · see citing works
“Some works merely extract features from structured and unstructured data separately, and then concatenate the two features(Zhang et al., 2020).”
BackgroundYu, M.-Y., & Son, Y. (2024) · European Journal of Cardiovascular Nursing · doi:10.1093/eurjcn/zvae031 · Cited by 34 · see citing works
Seinen, T. M., Kors, J. A., van Mulligen, E. M., & Rijnbeek, P. R. (2024) · Journal of Medical Internet Research · doi:10.2196/66910 · Cited by 34 · see citing works
Garriga, R., Buda, T. S., Guerreiro, J., Iglesias, J. O., Aguerri, I. E., & Matić, A. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101260 · Cited by 36 · see citing works
Using Deep Learning to Identify High-Risk Patients with Heart Failure with Reduced Ejection Fraction
Wang, Z., Chen, X., Tan, X., Yang, L., Kannapur, K., Vincent, J. L., Kessler, G. N., Ru, B., & Yang, M. (2021) · Journal of health economics and outcomes research · doi:10.36469/jheor.2021.25753 · Cited by 31 · see citing works
Usmani, U. A., Happonen, A., & Watada, J. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-18461-1_19 · Cited by 30 · see citing works
Distilling the knowledge from large-language model for health event prediction
Ding, S., Ye, J., Hu, X., & Zou, N. (2024) · Scientific Reports · doi:10.1038/s41598-024-75331-2 · Cited by 28 · see citing works
Generative AI Models in Time-Varying Biomedical Data: Scoping Review
He, R., Sarwal, V., Qiu, X., Zhuang, Y., Zhang, L., Liu, Y., & Chiang, J. N. (2024) · Journal of Medical Internet Research · doi:10.2196/59792 · Cited by 32 · see citing works
Wu, J., He, K., Mao, R., Shang, X., & Cambria, E. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103283 · Cited by 28 · see citing works
Lin, W.-C., Chen, A., Song, X. B., Weiskopf, N. G., Chiang, M. F., & Hribar, M. R. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad213 · Cited by 27 · see citing works
Multi-modal learning for inpatient length of stay prediction
Chen, J., Wen, Y., Pokojovy, M., Tseng, T.-L., McCaffrey, P., Vo, A. H., Walser, E., & Moen, S. T. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108121 · Cited by 26 · see citing works
Prediction of Hemorrhagic Transformation after Ischemic Stroke Using Machine Learning
Choi, J.-M., Seo, S.-Y., Kim, P.-J., Kim, Y.-S., Lee, S., Sohn, J., Kim, D., Lee, J., & Kim, C. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11090863 · Cited by 26 · see citing works
Chen, P., Chen, L., Lin, Y.-K., Li, G.-H., Lai, F., Lü, C.-W., Yang, C.-Y., Chen, K., & Lin, T. (2022) · JMIR Medical Informatics · doi:10.2196/38241 · Cited by 26 · see citing works
Analyzing the Challenges Facing Digital Mental Health (DMH) Between Aspiration and Reality
Tassnim, R., Ja'far, R. A. T., & Al-Tarawneh, A. M. A. (2024) · International Conference on Cyber Resilience (ICCR) · doi:10.1109/iccr61006.2024.10533151 · Cited by 25 · see citing works
Allen, K. S., Hood, D. R., Cummins, J., Kasturi, S., Mendonca, E. A., & Vest, J. R. (2023) · JAMIA Open · doi:10.1093/jamiaopen/ooad024 · Cited by 25 · see citing works
The quality and utility of artificial intelligence in patient care
Wehkamp, K., Krawczak, M., & Schreiber, S. (2023) · Deutsches Ärzteblatt international · doi:10.3238/arztebl.m2023.0124 · Cited by 26 · see citing works
Qiu, Z., Chen, X., Xie, D., Ren, Y., Wang, Y., Yang, Z., Guo, M., Song, Y., Guo, J., Feng, Y., et al. (2024) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2024.104797 · Cited by 23 · see citing works
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Arya, M., Hanumat Sastry, G., Motwani, A., Kumar, S., & Zaguia, A. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2021.797877 · Cited by 23 · see citing works
Enhancing early autism prediction based on electronic records using clinical narratives
Chen, J., Engelhard, M., Henao, R., Berchuck, S. I., Eichner, B., Perrin, E. M., Sapiro, G., & Dawson, G. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104390 · Cited by 23 · see citing works
Jaotombo, F., Adorni, L., Ghattas, B., & Boyer, L. (2023) · PLoS ONE · doi:10.1371/journal.pone.0289795 · Cited by 24 · see citing works
Multi-Class Text Classification Using Machine Learning Models for Online Drug Reviews
Joshi, S., & Abdelfattah, E. (2021) · IEEE World AI IoT Congress (AIIoT) · doi:10.1109/aiiot52608.2021.9454250 · Cited by 22 · see citing works
Dipnall, J. F., Page, R. S., Du, L., Costa, M. L., Lyons, R. A., Cameron, P. A., de Steiger, R. N., Hau, R., Bucknill, A., Oppy, A., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0257361 · Cited by 22 · see citing works
Liu, Z., Zhang, J., Hou, Y., Zhang, X., Li, G., & Xiang, Y. (2023) · Communications in computer and information science · doi:10.1007/978-981-19-9865-2_10 · Cited by 22 · see citing works
A systematic data characteristic understanding framework towards physical-sensor big data challenges
Ma, Z., Jôrgensen, B. N., & Ma, Z. G. (2024) · Journal Of Big Data · doi:10.1186/s40537-024-00942-5 · Cited by 27 · see citing works
Explainable Artificial Intelligence for State of Charge Estimation of Lithium-Ion Batteries
Chan, H. T. J., Rubeša-Zrim, J., Pichler, F., Salihi, A., Mourad, A., Šimić, I., Časni, K., & Veas, E. E. (2025) · Applied Sciences · doi:10.3390/app15095078 · Cited by 21 · see citing works
Interpretable prediction of mortality in liver transplant recipients based on machine learning
Zhang, X., Gavaldà, R., & Baixeries, J. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106188 · Cited by 21 · see citing works
Khalate, P., Gite, S., Pradhan, B., & Lee, C.-W. (2024) · Frontiers in Physics · doi:10.3389/fphy.2024.1445204 · Cited by 23 · see citing works
Natural Language Processing Enhanced Qualitative Methods: An Opportunity to Improve Health Outcomes
Parker, R. D., Mancini, K., & Abram, M. D. (2023) · International Journal of Qualitative Methods · doi:10.1177/16094069231214144 · Cited by 20 · see citing works
An Interpretable Adaptive Multiscale Attention Deep Neural Network for Tabular Data
Dentamaro, V., Giglio, P., Impedovo, D., Pirlo, G., & Ciano, M. D. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3392355 · Cited by 23 · see citing works
Teles, A. S., de Moura, I. R., Silva, F., Roberts, A., & Stahl, D. R. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.102981 · Cited by 22 · see citing works
Wang, N., Wang, M., Yang, Z., Liu, H., Lan, W., Fei, X., & Chen, H. (2021) · Journal of Medical Internet Research · doi:10.2196/30720 · Cited by 19 · see citing works
Vagliano, I., Dormosh, N., Rios, M., Luik, T. T., Buonocore, T. M., Elbers, P., Dongelmans, D. A., Schut, M. C., & Abu–Hanna, A. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104504 · Cited by 20 · see citing works
Jujjavarapu, C., Suri, P., Pejaver, V., Friedly, J., Gold, L., Meier, E., Cohen, T., Mooney, S., Heagerty, P., & Jarvik, J. (2023) · BMC Medical Informatics Decis. Mak. · doi:10.1186/s12911-022-02096-x · Cited by 18 · see citing works
Gaviria-Valencia, S., Murphy, S. P., Kaggal, V. C., McBane, R. D., Rooke, T. W., Chaudhry, R., Aguirre, M. A., & Arruda-Olson, A. M. (2023) · JMIR Medical Informatics · doi:10.2196/40964 · Cited by 19 · see citing works
Interpretable machine learning for time-to-event prediction in medicine and healthcare
Baniecki, H., Sobieski, B., Szatkowski, P., Bombiński, P., & Biecek, P. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.103026 · Cited by 20 · see citing works
Kabir, R., Watanobe, Y., Ding, D., Islam, M. R., & Naruse, K. (2025) · IEEE Access · doi:10.1109/access.2025.3619776 · Cited by 17 · see citing works
The risk assessment tool for intensive care unit readmission: A systematic review and meta-analysis
Long, J., Wang, M., Li, W., Cheng, J., Yuan, M., Zhong, M., Zhang, Z., & Zhang, C. (2023) · Intensive and Critical Care Nursing · doi:10.1016/j.iccn.2022.103378 · Cited by 17 · see citing works
Gezimati, M., & Singh, G. K. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3547084 · Cited by 16 · see citing works
Loaiza‐Bonilla, A., Thaker, N. G., Chung, C. D., Parikh, R. B., Stapleton, S., & Borkowski, P. (2025) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk-25-100048 · Cited by 16 · see citing works
Ostropolets, A., Hripcsak, G. M., Husain, S. A., Richter, L. R., Spotnitz, H. M., Elhussein, A., & Ryan, P. B. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad202 · Cited by 16 · see citing works
Li, Y., Liang, Z., Li, Y., Cao, Y., Zhang, H., & Dong, B. (2024) · European Journal of Radiology · doi:10.1016/j.ejrad.2024.111714 · Cited by 16 · see citing works
Kim, Y., Kim, M., Kim, Y., & Choi, M. (2025) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2025.105133 · Cited by 20 · see citing works
Li, F., Rasmy, L., Xiang, Y., Feng, J., Abdelhameed, A. M., Hu, X., Sun, Z., Aguilar, D. A., Dhoble, A., Du, J., et al. (2024) · Journal of the American Heart Association · doi:10.1161/jaha.123.029900 · Cited by 15 · see citing works
Accelerating the characterization of dynamic DNA origami devices with deep neural networks
Wang, Y., Jin, X., & Castro, C. (2023) · Scientific Reports · doi:10.1038/s41598-023-41459-w · Cited by 16 · see citing works
Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature
Douthit, B. J., Walden, R. L., Cato, K., Coviak, C. P., Cruz, C., D’Agostino, F., Forbes, T. H., Gao, G., Kapetanovic, T. A., Lee, M. A., et al. (2022) · Applied Clinical Informatics · doi:10.1055/s-0041-1742218 · Cited by 14 · see citing works
Oxygenation Fluctuations Associated with Severe Retinopathy of Prematurity
Lin, W.-C., Jordan, B. K., Scottoline, B. P., Ostmo, S. R., Coyner, A. S., Singh, P., Kalpathy–Cramer, J., Deniz Erdogmus, B., Chan, R. V. P., Chiang, M. F., et al. (2023) · Ophthalmology Science · doi:10.1016/j.xops.2023.100417 · Cited by 14 · see citing works
Bandyopadhyay, A., Albashayreh, A., Zeinali, N., Fan, W., & Gilbertson‐White, S. (2024) · JAMIA Open · doi:10.1093/jamiaopen/ooae082 · Cited by 14 · see citing works
Jia, Q., Zhu, Y., Xu, R., Zhang, Y., & Zhao, Y. (2022) · Industrial Management & Data Systems · doi:10.1108/imds-12-2021-0769 · Cited by 16 · see citing works
Introduction to machine learning
Rezaei, N., & Jabbari, P. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-822400-7.00012-9 · Cited by 14 · see citing works
Khairuddin, M. Z. F., Hasikin, K. B., Razak, N. A. A., Mohshim, S. A., & Ibrahim, S. S. (2023) · IEEE Access · doi:10.1109/access.2023.3304328 · Cited by 13 · see citing works
Pingi, S. T., Zhang, D., Bashar, M. A., & Nayak, R. (2023) · Data Science and Engineering · doi:10.1007/s41019-023-00232-9 · Cited by 13 · see citing works
Moharrami, M., Zavareh, P. A., Watson, E. E., Singhal, S., Johnson, A. E. W., Hosni, A., Quiñonez, C., & Glogauer, M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0307531 · Cited by 13 · see citing works
Hunik, L., Chaabouni, A., van Laarhoven, T., Hartman, T. O., Leijenaar, R. T. H., Cals, J., Uijen, A. A., & Schers, H. (2025) · JMIR Medical Informatics · doi:10.2196/62862 · Cited by 13 · see citing works
Galimzhanov, A. M., Matetić, A., Tenekecioğlu, E., & Mamas, M. A. (2023) · International Journal of Cardiology · doi:10.1016/j.ijcard.2023.131339 · Cited by 12 · see citing works
Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis
Pang, H., Zhou, L. N., Dong, Y., Chen, P., Gu, D., Lyu, T., & Zhang, H.-J. (2024) · ICCK Transactions on Intelligent Systematics · doi:10.62762/tis.2025.367320 · Cited by 12 · see citing works
Classification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation
Veiga, R., Schüler‐Faccini, L., de França, G. V. A., Andrade, R. F. S., Teixeira, M. G., Costa, L. C., Paixão, E. S., Costa, M. D. C. N., Barreto, M. L., de Oliveira, J. F., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-86361-5 · Cited by 12 · see citing works
Alam, M. A. U. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i11.21545 · Cited by 13 · see citing works
Multi-Gate Mixture of Multi-View Graph Contrastive Learning on Electronic Health Record
Cao, Y., Wang, Q., Wang, X., Peng, D., & Li, P. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3325221 · Cited by 12 · see citing works
EL‐Manzalawy, Y., Abbas, M. M., Hoaglund, I., Cerna, A. U., Morland, T., Haggerty, C. M., Hall, E. S., & Fornwalt, B. K. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01517-7 · Cited by 11 · see citing works
Pham, T.-H., Yin, C., Mehta, L. S., Zhang, X., & Zhang, P. (2022) · Knowledge and Information Systems · doi:10.1007/s10115-022-01813-2 · Cited by 11 · see citing works
Sax, D. R., Warton, E. M., Sofrygin, O., Mark, D. G., Ballard, D. W., Kene, M. V., Vinson, D. R., & Reed, M. E. (2023) · Journal of the American College of Emergency Physicians Open · doi:10.1002/emp2.13003 · Cited by 11 · see citing works
Salybekov, A. A., Wolfien, M., Kobayashi, S., Steinhoff, G., & Asahara, T. (2021) · Cells · doi:10.3390/cells10123266 · Cited by 10 · see citing works
Research on Multimodal Fusion of Temporal Electronic Medical Records
Ma, M., Wang, M., Gao, B., Li, Y., Huang, J., & Chen, H. (2024) · Bioengineering · doi:10.3390/bioengineering11010094 · Cited by 10 · see citing works
AI-Enhanced Lung Cancer Prediction: A Hybrid Model's Precision Triumph
Kesiku, C. Y., & García-Zapirain, B. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3447583 · Cited by 10 · see citing works
Ovarian cancer data analysis using deep learning: A systematic review
Hira, M. T., Razzaque, M. A., & Sarker, M. H. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109250 · Cited by 22 · see citing works
ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis
Gan, Z., Zhou, D., Rush, E. N., Panickan, V. A., Ho, Y.-L. A., Ostrouchovm, G., Xu, Z., Shen, S., Xiong, X., Greco, K. F., et al. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104761 · Cited by 10 · see citing works
Shi, J., Hubbard, A., Fong, N., & Pirracchio, R. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-03058-9 · Cited by 10 · see citing works
- Authors
- Zhang, D., Yin, C., Zeng, J., Yuan, X., & Zhang, P.
- Affiliations
- Wuhan University of Technology (CN)
- The Ohio State University (US)
- Corresponding author
- Zhang, Ping
- Venue
- BMC Medical Informatics and Decision Making
- Published
- 2020-10-29
- Volume
- 20
- Issue
- 1
- Pages
- 280-280
- ISSN
- 1472-6947 (ISSN-L)
- PMC
- PMC7596962
- OpenAlex
- W3095791468
- Cited by
- 269
- References
- 35
- Citations by year
- 2026: 41 · 2025: 72 · 2024: 52 · 2023: 52 · 2022: 34 · 2021: 18
- FWCI
- 10.13
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Medical Informatics and Decision Making (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Nursing › Issues, ethics and legal aspects › Nursing Diagnosis and Documentation
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- electronic health records, unstructured clinical notes, multimodal neural networks, patient representation learning, in-hospital mortality prediction, hospital readmission prediction, length of stay prediction, MIMIC-III, clinical risk prediction
- MeSH terms
- Machine Learning, Deep Learning, Humans, Patient Readmission, Neural Networks, Computer, Electronic Health Records
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