Representation Learning for Electronic Health Records: Comparison
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Please note this is a comparison between Version 2 by Sirius Huang and Version 1 by Atieh Khodadadi.

An electronic health record (EHR) is a vital high-dimensional part of medical concepts. Discovering implicit correlations in the information of this data set and the research and informative aspects can improve the treatment and management process. The challenge of concern is the data sources’ limitations in finding a stable model to relate medical concepts and use these existing connections.

 

  • electronic health record
  • deep learning
  • intensive care unit

1. Introduction

Medical and therapeutic techniques have substantially benefited from the collection of health data and the use of such data in the field of data science [1,2,3][1][2][3]. EHRs are one of these enormous sources of data, helpful for a variety of predictive tasks in medical applications [4]. EHRs hold a patient’s demographics, medical history, vital signs, laboratory tests, recommended medicine, diagnosis, and clinical outcomes during an interaction [5,6][5][6]. EHR databases may contain several patient visits, establishing a longitudinal patient record that can be used to aim the treatment process, such as disease prediction, mortality prediction, and enhancing the efficacy of the therapeutic process.
Initially, EHR systems were intended to manage the basic administrative functions of hospitals, permitting the use of regulated terminology and labelling schemes. Numerous labelling schemes exist, including ICD (International Statistical Classification of Diseases) codes for diagnostic [7,8[7][8][9][10],9,10], CPT (Current Procedural Terminology) codes for procedures [11[11][12][13],12,13], and LOINC (Logical Observation Identifiers Names and Codes) for laboratories [14,15][14][15], ATC (Anatomical Therapeutic Chemical) for drug [16[16][17],17], and RxNorm for medication [12]. The various labelling techniques produce standard datasets for varied specialisations. As the EHR system develops, the volume of EHR data increases annually, and several studies have been conducted on the secondary use of these data.
EHRs offer numerous benefits, including improved patient care, increased efficiency, and reduced healthcare costs [18]. Regardless of the potential for EHRs in various applications, their effective usage is hindered by data-specific restrictions [6], such as high missingness and irregular sampling [19[19][20][21],20,21], as well as imbalanced classes due to uneven prevalence of illnesses [22]. Therefore, it is important to address these limitations in order to fully realise the potential of EHRs.

2. Representation Learning for EHR Applications

2.1. Vector-Based Methods

One of the learning models that represents patient information on this basis is a fully connected Deep Neural Network (DNN). Futoma et al. [27][23] evaluated various models’ propensity to forecast hospital readmissions using data from a large EHR database. The outcome demonstrates DNN outperforms other approaches that have previously been used to solve this issue in terms of prediction performance. The study given in [28][24] employed a deep generative learning model to overcome the problem of insufficient data using MRI pictures efficiently by learning and categorising tumour locations from MRI images. The search by Zheng et al. [29][25] for suicide ideation, behaviour, or death prediction in the literature was based on the health records of patients who had visited a Berkshire Health System hospital. Multiple machine learning and deep learning methodologies are employed in EHRs to classify the severity of patients in [30][26]. The experimental findings indicate DNN performed exceptionally well. In the type II diabetes disease prediction [31][27], a deep learning neural network architecture model was adopted. All these studies demonstrated the DNN can be utilised for EHR data analysis and diagnosis. Despite this, the majority of recent research has considered this architecture to be the classic way [32][28].
Autoencoders are vector-based, unsupervised deep learning models, which are an efficient dimensionality reduction technique with promising performance for the deep representation of medical data [33][29]. Autoencoders have also been effectively applied to datasets comprising massive collections of electronic health records, where they are very adept at handling missing data [34][30]. A comparison study by Sadati et al. [35][31] emphasised the effectiveness of several types of autoencoders for electronic health record-based data sets. Combining a recurrent autoencoder with two GANs, Lee et al. [36][32] suggested sequential electronic health records with a dual adversarial autoencoder (DAAE). Biswal et al. [37][33] synthesised sequences of discrete EHR encounters and encounter features using a variational autoencoder. Very recently, in [38][34], for adverse drug event preventability, a model of dual autoencoders was explored in EHRs. Wang et al. [39][35] compared the model with autoencoder features to traditional models, which could show a reasonable result.
Convolutional Neural Networks (CNNs) are a further vector-based technique. EHR research [40][36] focuses on capturing the local temporal dependence of these data, which are then used to predict multiple diseases and for other related tasks. Wang et al. [41][37] adopted a CNN learning with 1929 features for the classification of 1099 international diseases. Researchers in [42][38] aimed to develop a convolutional neural network model for the prediction of the risk of advanced nonmelanoma skin cancer (NMSC) in Taiwanese adults. In an intriguing study [43][39], CNN was applied over electronic health records to determine the top 20 lung-cancer-related indicators in order to avoid radiation exposure and costs. CNN has shown its superior ability to measure patient similarity. However, the traditional CNN architecture could not properly exploit the temporal and contextual information of EHRs for disease prediction. Consequently, it is increasingly difficult to represent the timing and substance of EHR data concurrently [44][40].
Natural language processing was the original inspiration for word2vec [45][41], which was developed to learn word embeddings from large-scale text resources. In [46][42], the authors pursue the word2vec technique to train a two-layer neural network to improve clinical application prediction accuracy relative to baselines. Choi et al. [47][43] applied skip-gram to longitudinal EHR data to learn low-dimensional representations of medical concepts. To improve the performance of a convolutional neural network for patient phenotyping, Yang et al. [48][44] explored a model that combines token-level and sentence-level inputs. Similarly, in [49][45], clinical text was employed to expect clinical notions. Steinberg et al. [50][46] proposed a novel analogy of language modelling on discretised clinical time-series data. However, these techniques do not explicitly model dynamic temporal information or address the challenges of heterogeneous data sources [51][47].

2.2. Temporal Matrix-Based Methods

Lee and Seu [52][48] presented Non-Negative Matrix Factorisation (NMF) as a method for discovering a collection of basic functions for expressing non-negative data. This matrix pertains to electronic health records, which generate a matrix with a time dimension and a clinical event dimension. Bioinformatics has extensively used NMF for clustering sources of variation [53,54,55][49][50][51]. There are other efforts to use NMF or its variants in the depiction of patient data in EHRs. In [56][52], disease trajectories are analysed using NMF to extract multi-morbidity patterns from a huge data collection of electronic health records. Zhao et al. [57][53] suggested that the NMF identifies relationships between genetic variants and disease phenotypes. In a recent study [58][54], NMF was used to examine the symptoms of covid and predict long-term infection. Controlling the degree to which the representation is sparse is difficult since sparseness is a side effect of the NMF algorithm [59][55]. The huge number of various diagnosis codes is an additional obstacle that results in a combinatorial explosion of the number of possible diseases, many of which are unique to a single patient [60][56].

2.3. Graph-Based Methods

The graph technique can be expressed using the EHR by using nodes to represent medical events and edges between the nodes to highlight the temporal links among clinical events. One emerging method of deep learning on graph-structured data is Graph Neural Networks (GNNs) [61][57]. GNNs can infer the missing information, leading to a representation that is more explicable [62][58]. The hierarchical relationships in EHRs were captured using GNN, as described in reference [63,64][59][60]. In [65][61], GNN reflected the links between drugs, side effects, diagnosis, associated treatments, and test results. For instance, Park et al. [66][62] suggested a knowledge graph-based question answering with EHR. Research [67][63] introduced an EHR-oriented knowledge graph system to efficiently utilise non-used information buried in EHRs. In EHRs, it is typical for spurious edges to be included and for other edges to be absent. Even though the observed graph is clean, it may contravene the properties of GNNs because it is not jointly optimised with them. These flaws in the observed graph may precipitously degrade the performance of GNNs [68][64].

2.4. Sequence-Based Methods

Sequence-based patient representation turns EHR data into a temporally ordered sequence of clinical events for use in prediction. A recurrent neural network (RNN) is a neural network that includes the GRU and LSTM networks as specific cases, according to Sherstinsky’s study [69][65]. RNNs are widely used in patient representation research that focuses on combinations or sequences of clinical codes [62][58]. The research included aid in early diagnosis [70,71][66][67] and disease prediction [72,73,74,75,76,77,78,79][68][69][70][71][72][73][74][75]. Recently, Gupta et al. [80][76] adopted a general LSTM network architecture to make improved predictions of BMI and obesity. Ref. [81][77] examined the performance of various deep neural network architectures, including LSTM, in scenarios involving clinical factors and chest X-ray radiology reports, revealing that the recommended BiLSTM model outperforms other DNN baseline models. RNN is frequently stated without context or rationale. In addition, training equations are frequently removed entirely; therefore, partial descriptions or missing formulas in RNN may result in its inefficiency [69][65].

2.5. Tensor-Based Methods

Tensor-based methods apply an n-dimensional tensor to represent patient information. The multi-dimensional and high level of tensor factors in EHR data make complex relationships understandable and interpretable [82][78]. Zhao et al. [83][79] identified previously unknown cardiovascular characteristics using a modified non-negative tensor-factorisation technique. Afshar et al. [84][80] implemented temporal and static tensor factorisation to extract clinically significant characteristics. Hernandez et al. [85][81] used a novel tensor-based dimensionality reduction method to predict the onset of haemodynamic decompensation.

 

References

  1. Kouchaki, S.; Ding, X.R.; Sanei, S.; Zhang, Z.; Liu, Y.; Zhang, J.; Zhang, Y.; Shen, D.; Zhang, J. Artificial Intelligence-Based Applications in Medical Imaging: A Review of Recent Advances and Future Directions. Sensors 2021, 21, 4800.
  2. Bieberle, A.; Windisch, D.; Iskander, K.; Bieberle, M.; Hampel, U. Artificial Intelligence in Medical Sensors. Sensors 2020, 20, 5174.
  3. Rajpurkar, P.; Chen, E.; Banerjee, O.; Topol, E.J. AI in health and medicine. Nat. Med. 2022, 28, 31–38.
  4. Nordo, A.H.; Levaux, H.P.; Becnel, L.B.; Galvez, J.; Rao, P.; Stem, K.; Prakash, E.; Kush, R.D. Use of EHRs data for clinical research: Historical progress and current applications. Learn. Health Syst. 2019, 3, e10076.
  5. Birkhead, G.S.; Klompas, M.; Shah, N.R. Uses of electronic health records for public health surveillance to advance public health. Annu. Rev. Public Health 2015, 36, 345–359.
  6. Ghosheh, G.; Li, J.; Zhu, T. A review of Generative Adversarial Networks for Electronic Health Records: Applications, evaluation measures and data sources. arXiv 2022, arXiv:2203.07018.
  7. Cloitre, M. ICD-11 complex post-traumatic stress disorder: Simplifying diagnosis in trauma populations. Br. J. Psychiatry 2020, 216, 129–131.
  8. Harrison, J.E.; Weber, S.; Jakob, R.; Chute, C.G. ICD-11: An international classification of diseases for the twenty-first century. BMC Med. Inform. Decis. Mak. 2021, 21, 206.
  9. Jacobs, J.P.; Franklin, R.C.; Béland, M.J.; Spicer, D.E.; Colan, S.D.; Walters, H.L.; Bailliard, F.; Houyel, L.; Louis, J.D.S.; Lopez, L.; et al. Nomenclature for pediatric and congenital cardiac care: Unification of clinical and administrative nomenclature—The 2021 international paediatric and congenital cardiac code (IPCCC) and the eleventh revision of the International classification of diseases (ICD-11). Cardiol. Young 2021, 31, 1057–1188.
  10. Maercker, A. Development of the new CPTSD diagnosis for ICD-11. Borderline Personal. Disord. Emot. Dysregulation 2021, 8, 1–4.
  11. Joo, H.; Burns, M.; Kalidaikurichi Lakshmanan, S.S.; Hu, Y.; Vydiswaran, V.V. Neural machine translation–based automated current procedural terminology classification system using procedure text: Development and validation study. JMIR Form. Res. 2021, 5, e22461.
  12. Bowie, M.J. Understanding Current Procedural Terminology and HCPCS Coding Systems; Cengage Learning: Boston, MA, USA, 2021.
  13. Levy, J.; Vattikonda, N.; Haudenschild, C.; Christensen, B.; Vaickus, L. Comparison of machine-learning algorithms for the prediction of current procedural terminology (CPT) codes from pathology reports. J. Pathol. Inform. 2022, 13, 100165.
  14. Stram, M.; Gigliotti, T.; Hartman, D.; Pitkus, A.; Huff, S.M.; Riben, M.; Henricks, W.H.; Farahani, N.; Pantanowitz, L. Logical observation identifiers names and codes for laboratorians: Potential solutions and challenges for interoperability. Arch. Pathol. Lab. Med. 2020, 144, 229–239.
  15. Yeh, C.Y.; Peng, S.J.; Yang, H.C.; Islam, M.; Poly, T.N.; Hsu, C.Y.; Huff, S.M.; Chen, H.C.; Lin, M.C. Logical observation identifiers names and codes (Loinc®) applied to microbiology: A national laboratory mapping experience in Taiwan. Diagnostics 2021, 11, 1564.
  16. Tayebati, S.K.; Nittari, G.; Mahdi, S.S.; Ioannidis, N.; Sibilio, F.; Amenta, F. Identification of World Health Organisation ship’s medicine chest contents by Anatomical Therapeutic Chemical (ATC) classification codes. Int. Marit. Health 2017, 68, 39–45.
  17. Tang, S.; Chen, L. iATC-NFMLP: Identifying Classes of Anatomical Therapeutic Chemicals Based on Drug Networks, Fingerprints, and Multilayer Perceptron. Curr. Bioinform. 2022, 17, 814–824.
  18. Kataria, S.; Ravindran, V. Electronic health records: A critical appraisal of strengths and limitations. J. R. Coll. Physicians Edinb. 2020, 50, 262–268.
  19. Conway, M.; Berg, R.L.; Carrell, D.; Denny, J.C.; Kho, A.N.; Kullo, I.J.; Linneman, J.G.; Pacheco, J.A.; Peissig, P.; Rasmussen, L.; et al. Analyzing the heterogeneity and complexity of Electronic Health Record oriented phenotyping algorithms. AMIA Annu. Symp. Proc. 2011, 2011, 274–283.
  20. Madden, J.M.; Lakoma, M.D.; Rusinak, D.; Lu, C.Y.; Soumerai, S.B. Missing clinical and behavioral health data in a large electronic health record (EHR) system. J. Am. Med. Inform. Assoc. 2016, 23, 1143–1149.
  21. Chauhan, V.K.; Thakur, A.; O’Donoghue, O.; Clifton, D.A. COPER: Continuous patient state perceiver. In Proceedings of the 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Ioannina, Greece, 27–30 September 2022; pp. 1–4.
  22. Wu, J.; Roy, J.; Stewart, W.F. Prediction modeling using EHR data: Challenges, strategies, and a comparison of machine learning approaches. Med. Care 2010, 48, S106–S113.
  23. Futoma, J.; Morris, J.; Lucas, J. A comparison of models for predicting early hospital readmissions. J. Biomed. Inform. 2015, 56, 229–238.
  24. Solares, J.R.A.; Raimondi, F.E.D.; Zhu, Y.; Rahimian, F.; Canoy, D.; Tran, J.; Gomes, A.C.P.; Payberah, A.H.; Zottoli, M.; Nazarzadeh, M.; et al. Deep learning for electronic health records: A comparative review of multiple deep neural architectures. J. Biomed. Inform. 2020, 101, 103337.
  25. Zheng, L.; Wang, O.; Hao, S.; Ye, C.; Liu, M.; Xia, M.; Sabo, A.N.; Markovic, L.; Stearns, F.; Kanov, L.; et al. Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records. Transl. Psychiatry 2020, 10, 1–10.
  26. Alam, A.; Reza, R.; Abrar, A.; Ahmed, T.; Ahmed, S.; Sharar, S.; Rasel, A.A. Patients’ Severity States Classification based on Electronic Health Record (EHR) Data using Multiple Machine Learning and Deep Learning Approaches. arXiv 2022, arXiv:2209.14907.
  27. Nguyen, B.P.; Pham, H.N.; Tran, H.; Nghiem, N.; Nguyen, Q.H.; Do, T.T.; Tran, C.T.; Simpson, C.R. Predicting the onset of type 2 diabetes using wide and deep learning with electronic health records. Comput. Methods Progr. Biomed. 2019, 182, 105055.
  28. Ma, F.; You, Q.; Xiao, H.; Chitta, R.; Zhou, J.; Gao, J. Kame: Knowledge-based attention model for diagnosis prediction in healthcare. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino, Italy, 22–26 October 2018; pp. 743–752.
  29. Koumakis, L. Deep learning models in genomics; are we there yet? Comput. Struct. Biotechnol. J. 2020, 18, 1466–1473.
  30. Beaulieu-Jones, B.K.; Moore, J.H.; CONSORTIUM, P.R.O.A.A.C.T. Missing data imputation in the electronic health record using deeply learned autoencoders. In Proceedings of the Pacific Symposium on Biocomputing 2017, Fairmont Orchid, HI, USA, 3–7 January 2017; pp. 207–218.
  31. Sadati, N.; Nezhad, M.Z.; Chinnam, R.B.; Zhu, D. Representation learning with autoencoders for electronic health records: A comparative study. arXiv 2018, arXiv:1801.02961.
  32. Lee, D.; Yu, H.; Jiang, X.; Rogith, D.; Gudala, M.; Tejani, M.; Zhang, Q.; Xiong, L. Generating sequential electronic health records using dual adversarial autoencoder. J. Am. Med. Inform. Assoc. 2020, 27, 1411–1419.
  33. Biswal, S.; Ghosh, S.; Duke, J.; Malin, B.; Stewart, W.; Xiao, C.; Sun, J. EVA: Generating longitudinal electronic health records using conditional variational autoencoders. In Proceedings of the Machine Learning for Healthcare Conference, PMLR, Virtual, 6–7 August 2021; pp. 260–282.
  34. Liao, W.; Derijks, H.J.; Blencke, A.A.; de Vries, E.; van Seyen, M.; van Marum, R.J. Dual autoencoders modeling of electronic health records for adverse drug event preventability prediction. Intell.-Based Med. 2022, 6, 100077.
  35. Wang, L.; Tong, L.; Davis, D.; Arnold, T.; Esposito, T. The application of unsupervised deep learning in predictive models using electronic health records. BMC Med. Res. Methodol. 2020, 20, 1–9.
  36. Suo, Q.; Ma, F.; Yuan, Y.; Huai, M.; Zhong, W.; Zhang, A.; Gao, J. Personalized disease prediction using a CNN-based similarity learning method. In Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, USA, 13–16 November 2017; pp. 811–816.
  37. Wang, Y.H.; Nguyen, P.A.; Islam, M.M.; Li, Y.C.; Yang, H.C. Development of Deep Learning Algorithm for Detection of Colorectal Cancer in EHR Data. MedInfo 2019, 264, 438–441.
  38. Kreinovich, V.; Phuong, N.H. Soft Computing for Biomedical Applications and Related Topics; Springer: Berlin/Heidelberg, Germany, 2021.
  39. Yeh, M.C.H.; Wang, Y.H.; Yang, H.C.; Bai, K.J.; Wang, H.H.; Li, Y.C.J. Artificial intelligence–based prediction of lung cancer risk using nonimaging electronic medical records: Deep learning approach. J. Med. Internet Res. 2021, 23, e26256.
  40. Zhu, Z.; Yin, C.; Qian, B.; Cheng, Y.; Wei, J.; Wang, F. Measuring patient similarities via a deep architecture with medical concept embedding. In Proceedings of the 2016 IEEE 16th International Conference on Data Mining (ICDM), Barcelona, Spain, 12–15 December 2016; pp. 749–758.
  41. Mikolov, T.; Sutskever, I.; Chen, K.; Corrado, G.S.; Dean, J. Distributed representations of words and phrases and their compositionality. Adv. Neural Inf. Process. Syst. 2013, 26, 3111–3119.
  42. Choi, E.; Bahadori, M.T.; Searles, E.; Coffey, C.; Thompson, M.; Bost, J.; Tejedor-Sojo, J.; Sun, J. Multi-layer representation learning for medical concepts. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August 2016; pp. 1495–1504.
  43. Choi, Y.; Chiu, C.Y.I.; Sontag, D. Learning low-dimensional representations of medical concepts. AMIA Summits Transl. Sci. Proc. 2016, 2016, 41.
  44. Yang, Z.; Dehmer, M.; Yli-Harja, O.; Emmert-Streib, F. Combining deep learning with token selection for patient phenotyping from electronic health records. Sci. Rep. 2020, 10, 1432.
  45. Bai, T.; Chanda, A.K.; Egleston, B.L.; Vucetic, S. EHR phenotyping via jointly embedding medical concepts and words into a unified vector space. BMC Med. Inform. Decis. Mak. 2018, 18, 15–25.
  46. Steinberg, E.; Jung, K.; Fries, J.A.; Corbin, C.K.; Pfohl, S.R.; Shah, N.H. Language models are an effective representation learning technique for electronic health record data. J. Biomed. Inform. 2021, 113, 103637.
  47. Che, C.; Xiao, C.; Liang, J.; Jin, B.; Zho, J.; Wang, F. An rnn architecture with dynamic temporal matching for personalized predictions of parkinson’s disease. In Proceedings of the 2017 SIAM International Conference on Data Mining, Houston, TX, USA, 27–29 April 2017; pp. 198–206.
  48. Lee, D.D.; Seung, H.S. Learning the parts of objects by non-negative matrix factorization. Nature 1999, 401, 788–791.
  49. Stein-O’Brien, G.L.; Arora, R.; Culhane, A.C.; Favorov, A.V.; Garmire, L.X.; Greene, C.S.; Goff, L.A.; Li, Y.; Ngom, A.; Ochs, M.F.; et al. Enter the matrix: Factorization uncovers knowledge from omics. Trends Genet. 2018, 34, 790–805.
  50. Esposito, F.; Boccarelli, A.; Del Buono, N. An NMF-Based methodology for selecting biomarkers in the landscape of genes of heterogeneous cancer-associated fibroblast Populations. Bioinform. Biol. Insights 2020, 14, 1177932220906827.
  51. Quintero, A.; Hübschmann, D.; Kurzawa, N.; Steinhauser, S.; Rentzsch, P.; Krämer, S.; Andresen, C.; Park, J.; Eils, R.; Schlesner, M.; et al. ShinyButchR: Interactive NMF-based decomposition workflow of genome-scale datasets. Biol. Methods Protoc. 2020, 5, bpaa022.
  52. Hassaine, A.; Canoy, D.; Solares, J.R.A.; Zhu, Y.; Rao, S.; Li, Y.; Zottoli, M.; Rahimi, K.; Salimi-Khorshidi, G. Learning multimorbidity patterns from electronic health records using non-negative matrix factorisation. J. Biomed. Inform. 2020, 112, 103606.
  53. Zhao, J.; Feng, Q.; Wu, P.; Warner, J.L.; Denny, J.C.; Wei, W.Q. Using topic modeling via non-negative matrix factorization to identify relationships between genetic variants and disease phenotypes: A case study of Lipoprotein (a)(LPA). PLoS ONE 2019, 14, e0212112.
  54. Huang, Y.; Pinto, M.D.; Borelli, J.L.; Mehrabadi, M.A.; Abrihim, H.; Dutt, N.; Lambert, N.; Nurmi, E.L.; Chakraborty, R.; Rahmani, A.M.; et al. COVID symptoms, symptom clusters, and predictors for becoming a long-hauler: Looking for clarity in the haze of the pandemic. MedRxiv 2021.
  55. Hoyer, P.O. Non-negative matrix factorization with sparseness constraints. J. Mach. Learn. Res. 2004, 5, 1–13.
  56. Haug, N.; Deischinger, C.; Gyimesi, M.; Kautzky-Willer, A.; Thurner, S.; Klimek, P. High-risk multimorbidity patterns on the road to cardiovascular mortality. BMC Med. 2020, 18, 1–12.
  57. Molaei, S.; Bousejin, N.G.; Zare, H.; Jalili, M.; Pan, S. Learning graph representations with maximal cliques. IEEE Trans. Neural Networks Learn. Syst. 2021, 34, 1089–1096.
  58. Si, Y.; Du, J.; Li, Z.; Jiang, X.; Miller, T.; Wang, F.; Zheng, W.J.; Roberts, K. Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review. J. Biomed. Inform 2021, 115, 103671.
  59. Xie, X.; Xiong, Y.; Yu, P.S.; Zhu, Y. Ehr coding with multi-scale feature attention and structured knowledge graph propagation. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, China, 3–7 November 2019; pp. 649–658.
  60. Lu, C.; Reddy, C.K.; Ning, Y. Self-Supervised Graph Learning with Hyperbolic Embedding for Temporal Health Event Prediction. IEEE Trans. Cybern. 2021, 51, 1–14.
  61. Choi, E.; Xu, Z.; Li, Y.; Dusenberry, M.; Flores, G.; Xue, E.; Dai, A. Learning the graphical structure of electronic health records with graph convolutional transformer. In Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA, 7–12 February 2020; Volume 34, pp. 606–613.
  62. Park, J.; Cho, Y.; Lee, H.; Choo, J.; Choi, E. Knowledge graph-based question answering with electronic health records. In Proceedings of the Machine Learning for Healthcare Conference, PMLR, Virtual, 6–7 August 2021; pp. 36–53.
  63. Shang, Y.; Tian, Y.; Zhou, M.; Zhou, T.; Lyu, K.; Wang, Z.; Xin, R.; Liang, T.; Zhu, S.; Li, J. EHR-Oriented Knowledge Graph System: Toward Efficient Utilization of Non-Used Information Buried in Routine Clinical Practice. IEEE J. Biomed. Health Inform. 2021, 25, 2463–2475.
  64. Wang, R.; Mou, S.; Wang, X.; Xiao, W.; Ju, Q.; Shi, C.; Xie, X. Graph structure estimation neural networks. In Proceedings of the Web Conference 2021, Ljubljana, Slovenia, 19–23 April 2021; pp. 342–353.
  65. Sherstinsky, A. Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Phys. Nonlinear Phenom. 2020, 404, 132306.
  66. Lipton, Z.C.; Kale, D.C.; Elkan, C.; Wetzel, R. Learning to diagnose with LSTM recurrent neural networks. arXiv 2015, arXiv:1511.03677.
  67. Ma, F.; Chitta, R.; Zhou, J.; You, Q.; Sun, T.; Gao, J. Dipole: Diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, 13–17 August 2017; pp. 1903–1911.
  68. Choi, E.; Schuetz, A.; Stewart, W.F.; Sun, J. Using recurrent neural network models for early detection of heart failure onset. J. Am. Med. Inform. Assoc. 2017, 24, 361–370.
  69. Choi, E.; Bahadori, M.T.; Schuetz, A.; Stewart, W.F.; Sun, J. Doctor ai: Predicting clinical events via recurrent neural networks. In Proceedings of the Machine Learning for Healthcare Conference, PMLR, Los Angeles, CA, USA, 19–20 August 2016; pp. 301–318.
  70. Choi, E.; Bahadori, M.T.; Sun, J.; Kulas, J.; Schuetz, A.; Stewart, W. Retain: An interpretable predictive model for healthcare using reverse time attention mechanism. Adv. Neural Inf. Process. Syst. 2016, 29.
  71. Esteban, C.; Staeck, O.; Baier, S.; Yang, Y.; Tresp, V. Predicting clinical events by combining static and dynamic information using recurrent neural networks. In Proceedings of the 2016 IEEE International Conference on Healthcare Informatics (ICHI), Chicago, IL, USA, 4–7 October 2016; pp. 93–101.
  72. Liu, J.; Zhang, Z.; Razavian, N. Deep ehr: Chronic disease prediction using medical notes. In Proceedings of the Machine Learning for Healthcare Conference, PMLR, Palo Alto, CA, USA, 17–18 August 2018; pp. 440–464.
  73. Ashfaq, A.; Sant’Anna, A.; Lingman, M.; Nowaczyk, S. Readmission prediction using deep learning on electronic health records. J. Biomed. Inform. 2019, 97, 103256.
  74. Gao, C.; Osmundson, S.; Edwards, D.R.V.; Jackson, G.P.; Malin, B.A.; Chen, Y. Deep learning predicts extreme preterm birth from electronic health records. J. Biomed. Inform. 2019, 100, 103334.
  75. Wang, Z.; Li, H.; Liu, L.; Wu, H.; Zhang, M. Predictive multi-level patient representations from electronic health records. In Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA, 18–21 November 2019; pp. 987–990.
  76. Gupta, M.; Phan, T.L.T.; Bunnell, H.T.; Beheshti, R. Obesity Prediction with EHR Data: A deep learning approach with interpretable elements. ACM Trans. Comput. Healthc. Health 2022, 3, 1–19.
  77. Bagheri, A.; Groenhof, T.K.J.; Veldhuis, W.B.; de Jong, P.A.; Asselbergs, F.W.; Oberski, D.L. Multimodal learning for cardiovascular risk prediction using EHR data. arXiv 2020, arXiv:2008.11979.
  78. He, H.; Henderson, J.; Ho, J.C. Distributed tensor decomposition for large scale health analytics. In Proceedings of the World Wide Web Conference, San Francisco, CA, USA, 13–17 May 2019; pp. 659–669.
  79. Zhao, J.; Zhang, Y.; Schlueter, D.J.; Wu, P.; Kerchberger, V.E.; Rosenbloom, S.T.; Wells, Q.S.; Feng, Q.; Denny, J.C.; Wei, W.Q. Detecting time-evolving phenotypic topics via tensor factorization on electronic health records: Cardiovascular disease case study. J. Biomed. Inform. 2019, 98, 103270.
  80. Afshar, A.; Perros, I.; Park, H.; Defilippi, C.; Yan, X.; Stewart, W.; Ho, J.; Sun, J. Taste: Temporal and static tensor factorization for phenotyping electronic health records. In Proceedings of the ACM Conference on Health, Inference, and Learning, Toronto, ON, Canada, 2–4 April 2020; pp. 193–203.
  81. Hernandez, L.; Kim, R.; Tokcan, N.; Derksen, H.; Biesterveld, B.E.; Croteau, A.; Williams, A.M.; Mathis, M.; Najarian, K.; Gryak, J. Multimodal tensor-based method for integrative and continuous patient monitoring during postoperative cardiac care. Artif. Intell. Med. 2021, 113, 102032.
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