Learning hidden patterns from patient multivariate time series data using convolutional neural networks: A case study of healthcare cost prediction
Open accessAbstract
Objective To develop an effective and scalable individual-level patient cost prediction method by automatically learning hidden temporal patterns from multivariate time series data in patient insurance claims using a convolutional neural network (CNN) architecture.Methods We used three years of medical and pharmacy claims data from 2013 to 2016 from a healthcare insurer, where data from the first two years were used to build the model to predict costs in the third year. The data consisted of the multivariate time series of cost, visit and medical features that were shaped as images of patients' health status (i.e., matrices with time windows on one dimension and the medical, visit and cost features on the other dimension). Patients' multivariate time series images were given to a CNN method with a proposed architecture. After hyper-parameter tuning, the proposed architecture consisted of three building blocks of convolution and pooling layers with an LReLU activation function and a customized kernel size at each layer for healthcare data. The proposed CNN learned temporal patterns became inputs to a fully connected layer. We benchmarked the proposed method against three other methods: (1) a spike temporal pattern detection method, as the most accurate method for healthcare cost prediction described to date in the literature; (2) a symbolic temporal pattern detection method, as the most common approach for leveraging healthcare temporal data; and (3) the most commonly used CNN architectures for image pattern detection (i.e., AlexNet, VGGNet and ResNet) (via transfer learning). Moreover, we assessed the contribution of each type of data (i.e., cost, visit and medical). Finally, we externally validated the proposed method against a separate cohort of patients. All prediction performances were measured in terms of mean absolute percentage error (MAPE).Results The proposed CNN configuration outperformed the spike temporal pattern detection and symbolic temporal pattern detection methods with a MAPE of 1.67 versus 2.02 and 3.66, respectively (p < 0.01). The proposed CNN outperformed ResNet, AlexNet and VGGNet with MAPEs of 4.59, 4.85 and 5.06, respectively (p < 0.01). Removing medical, visit and cost features resulted in MAPEs of 1.98, 1.91 and 2.04, respectively (p < 0.01).Conclusions Feature learning through the proposed CNN configuration significantly improved individual-level healthcare cost prediction. The proposed CNN was able to outperform temporal pattern detection methods that look for a pre-defined set of pattern shapes, since it is capable of extracting a variable number of patterns with various shapes. Temporal patterns learned from medical, visit and cost data made significant contributions to the prediction performance. Hyper-parameter tuning showed that considering three-month data patterns has the highest prediction accuracy. Our results showed that patients' images extracted from multivariate time series data are different from regular images, and hence require unique designs of CNN architectures. The proposed method for converting multivariate time series data of patients into images and tuning them for convolutional learning could be applied in many other healthcare applications with multivariate time series data.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Existing studies examined health cost prediction from a payer perspective, such as using claims data to predict cost in subsequent years.
“Existing studies21,22 examined health cost prediction from a payer perspective, such as using claims data to predict cost in subsequent years.”
From Early prediction of diagnostic-related groups and estimation of hospital cost by processing clinical notes (2021) · cites this paper as [22]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
(2014) · Cited by 33,419 · see citing works
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741 · see citing works
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works
Statistical Comparisons of Classifiers over Multiple Data Sets
Demšar, J. (2006) · doi:10.5555/1248547.1248548 · Cited by 11,966 · see citing works
Maintaining knowledge about temporal intervals
Allen, J. F. (1983) · Communications of the ACM · doi:10.1145/182.358434 · Cited by 7,593 · cited 1× · see citing works
“The most common approach to extract TIRPs is using Allen’s temporal relations [27], with seven relations to capture the state of two alphabetic time intervals against each other (e.g., overlap , equals , meets ).”
Two-Stream Convolutional Networks for Action Recognition in Videos
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1406.2199 · Cited by 6,756 · see citing works
Large-Scale Video Classification with Convolutional Neural Networks
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.223 · Cited by 6,354 · see citing works
Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759 · see citing works
Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.010 · Cited by 4,842 · see citing works
The Handbook of brain theory and neural networks
(1996) · Choice Reviews Online · doi:10.5860/choice.33-4484 · Cited by 3,913 · see citing works
When to use the <scp>B</scp> onferroni correction
Armstrong, R. A. (2014) · Ophthalmic and Physiological Optics · doi:10.1111/opo.12131 · Cited by 3,031 · see citing works
A symbolic representation of time series, with implications for streaming algorithms
Lin, J., Keogh, E. J., Lonardi, S., & Chiu, B. (2003) · Workshop on Research Issues on Data Mining and Knowledge Discovery · doi:10.1145/882082.882086 · Cited by 2,199 · see citing works
A review of unsupervised feature learning and deep learning for time-series modeling
Längkvist, M., Karlsson, L., & Loutfi, A. (2014) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.01.008 · Cited by 1,234 · see citing works
The History Began from AlexNet: A Comprehensive Survey on Deep Learning ApproachesPreprint
Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S. K. E., Sidike, P., Nasrin, M. S., Essen, B. C. V., Awwal, A. A. S., & Asari, V. K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.01164 · Cited by 1,190 · see citing works
Time Series Classification Using Multi-Channels Deep Convolutional Neural Networks
Zheng, Y., Liu, Q., Chen, E., Ge, Y., & Zhao, J. L. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-08010-9_33 · Cited by 689 · see citing works
Deep Learning for Emotion Recognition on Small Datasets using Transfer Learning
Ng, H.-W., Nguyen, V. D., Vonikakis, V., & Winkler, S. (2015) · Proceedings of the 2015 ACM on International Conference on Multimodal Interaction · doi:10.1145/2818346.2830593 · Cited by 657 · see citing works
Analysis of function of rectified linear unit used in deep learning
Hara, K., Saito, D., & Shouno, H. (2015) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2015.7280578 · Cited by 428 · see citing works
Risk Prediction with Electronic Health Records: A Deep Learning Approach
Cheng, Y., Wang, F., Zhang, P., & Hu, J. (2016) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611974348.49 · Cited by 403 · see citing works
Wang, S., Phillips, P., Sui, Y., Liu, B., Yang, M., & Cheng, H. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-0932-7 · Cited by 350 · see citing works
Burlina, P., Pacheco, K. D., Joshi, N., Freund, D., & Bressler, N. M. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.01.018 · Cited by 219 · see citing works
Algorithmic Prediction of Health-Care Costs
Bertsimas, D., Bjarnadóttir, M. V., Kane, M. A., Kryder, J. C., Pandey, R., Vempala, S., & Wang, G. J. (2008) · Operations Research · doi:10.1287/opre.1080.0619 · Cited by 205 · see citing works
Maintaining Knowledge about Temporal Intervals
Allen, J. F. (1990) · Elsevier eBooks · doi:10.1016/b978-1-4832-1447-4.50033-x · Cited by 196 · see citing works
Comparing SVM and convolutional networks for epileptic seizure prediction from intracranial EEG
Mirowski, P., LeCun, Y., Madhavan, D., & Kuzniecky, R. (2008) · Machine learning for signal processing ... · doi:10.1109/mlsp.2008.4685487 · Cited by 170 · see citing works
Liew, S. S., Khalil-Hani, M., & Bakhteri, R. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.08.037 · Cited by 168 · see citing works
Mining recent temporal patterns for event detection in multivariate time series data
Batal, I., Fradkin, D., Harrison, J. H., Moerchen, F., & Hauskrecht, M. (2012) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2339530.2339578 · Cited by 162 · see citing works
Wodziński, M., Skalski, A., Hemmerling, D., Orozco‐Arroyave, J. R., & Nöth, E. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856972 · Cited by 157 · see citing works
Yıldırım, Ö., Talo, M., Ay, B., Baloğlu, U. B., Galip Aydin, B., & Acharya, U. R. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103387 · Cited by 152 · see citing works
A temporal pattern mining approach for classifying electronic health record data
Batal, I., Valizadegan, H., Cooper, G. F., & Hauskrecht, M. (2013) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2508037.2508044 · Cited by 147 · see citing works
Mining frequent arrangements of temporal intervals
Papapetrou, P., George Kollios, D., Sclaroff, S., & Gunopulos, D. (2009) · Knowledge and Information Systems · doi:10.1007/s10115-009-0196-0 · Cited by 115 · see citing works
Classification of multivariate time series via temporal abstraction and time intervals mining
Moskovitch, R., & Shahar, Y. (2014) · Knowledge and Information Systems · doi:10.1007/s10115-014-0784-5 · Cited by 110 · see citing works
Fast time intervals mining using the transitivity of temporal relations
Moskovitch, R., & Shahar, Y. (2013) · Knowledge and Information Systems · doi:10.1007/s10115-013-0707-x · Cited by 104 · see citing works
Martin, A. B., Hartman, M., Washington, B., & Catlin, A. (2018) · Health Affairs · doi:10.1377/hlthaff.2018.05085 · Cited by 95 · see citing works
Algorithms for time series knowledge mining
Moerchen, F. (2006) · Knowledge Discovery and Data Mining · doi:10.1145/1150402.1150485 · Cited by 94 · see citing works
Nearest-neighbor-based approach to time-series classification
Lee, Y., Wei, C., Cheng, T.-H., & Yang, C.-T. (2012) · Decision Support Systems · doi:10.1016/j.dss.2011.12.014 · Cited by 85 · see citing works
Examining A Health Care Price Transparency Tool: Who Uses It, And How They Shop For Care
Sinaiko, A. D., & Rosenthal, M. B. (2016) · Health Affairs · doi:10.1377/hlthaff.2015.0746 · Cited by 77 · see citing works
Towards heterogeneous temporal clinical event pattern discovery
Wang, F., Lee, N., Hu, J., Sun, J., & Ebadollahi, S. (2012) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2339530.2339605 · Cited by 76 · see citing works
Kuo, R. N.-C., Dong, Y., Liu, J., Chang, C., Shau, W., & Lai, M. (2011) · Medical Care · doi:10.1097/mlr.0b013e31822ebe11 · Cited by 75 · see citing works
A Pattern Mining Approach for Classifying Multivariate Temporal Data
Batal, I., Valizadegan, H., Cooper, G. F., & Hauskrecht, M. (2011) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2011.39 · Cited by 72 · see citing works
Population Cost Prediction on Public Healthcare Datasets
Sushmita, S., Newman, S., Marquardt, J., Ram, P., Prasad, V., Cock, M. D., & Teredesai, A. (2015) · Digital Health · doi:10.1145/2750511.2750521 · Cited by 67 · see citing works
Verduijn, M., Sacchi, L., Peek, N., Bellazzi, R., de Jonge, E., & de Mol, B. (2007) · Artif. Intell. Medicine · doi:10.1016/j.artmed.2007.06.003 · Cited by 65 · see citing works
Testing Alternative Regression Frameworks for Predictive Modeling of Health Care Costs
Duncan, I. G., Loginov, M., & Ludkovski, M. (2016) · North American Actuarial Journal · doi:10.1080/10920277.2015.1110491 · Cited by 61 · see citing works
Shen, B., Liang, X., Ouyang, Y., Liu, M., Zheng, W., & Carley, K. M. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3219931 · Cited by 55 · see citing works
Actuarial Applications of Multivariate Two-Part Regression Models
Frees, E. W., Jin, X., & Lin, X. (2013) · Annals of Actuarial Science · doi:10.1017/s1748499512000346 · Cited by 53 · see citing works
for the MultiCare study group, König, H., Leicht, H., Bickel, H., Fuchs, Â., Gensichen, J. S., Maier, W., Mergenthal, K., Riedel-Heller, S. G., Schäfer, I., et al. (2013) · BMC Health Services Research · doi:10.1186/1472-6963-13-219 · Cited by 47 · see citing works
Healthcare cost prediction: Leveraging fine-grain temporal patterns
Morid, M. A., Sheng, O. R. L., Kawamoto, K., Ault, T., Dorius, J., & Abdelrahman, S. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103113 · Cited by 35 · see citing works
Self-citationConsistent discovery of frequent interval-based temporal patterns in chronic patients’ data
Shknevsky, A., D, Y. S. M., & Moskovitch, R. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.10.002 · Cited by 32 · see citing works
Analyzing health insurance claims on different timescales to predict days in hospital
Xie, Y., Schreier, G., Hoy, M., Liu, Y., Neubauer, S., Chang, D. C. W., Redmond, S. J., & Lovell, N. H. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.01.002 · Cited by 25 · see citing works
Bi-linearly weighted fractional max pooling
Hang, S. T., & Aono, M. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4840-5 · Cited by 19 · see citing works
Challenges For Middle-Income Elders In An Aging Society
Rowe, J. W. (2019) · Health Affairs · doi:10.1377/hlthaff.2019.00095 · Cited by 9 · see citing works
Guo, S., Li, X., Liu, H., Zhang, P., Du, X., Xie, G., & Wang, F. (2017) · PubMed Central · Cited by 8 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- M.A. Morid, K. Kawamoto, T. Ault, J. Dorius, S. Abdelrahman, Supervised Learning Methods for Predicting Healthcare Costs: Systematic Literature Review and Empirical Evaluation., in: Proceeding Am. Med. Informatics Assoc., 2017: pp. 1312-1321.
- G.K. Dziugaite, D.M. Roy, Z. Ghahramani, Deep Learning, MIT Press, 2016.
- R. Moskovitch, Y. Shahar, Medical Temporal-Knowledge Discovery via Temporal Abstraction, in: AMIA 2009 Symp. Proc., 2009: pp. 452-456.
- I. Batal, D. Fradkin, J. Harrison, F. Moerchen, M. Hauskrecht, Mining recent temporal patterns for event detection in multivariate time series data, in: Proc. 18th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. -KDD '12, 2012: pp. 280-288.
- D.P. Kingma, J. Ba, Adam: A Method for Stochastic Optimization, in: 3rd Int. Conf. Learn. Represent., 2015.
Cited by
Sorted by citations
A scoping review of transfer learning research on medical image analysis using ImageNet
Morid, M. A., Borjali, A., & Del Fiol, G. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104115 · Cited by 508 · see citing works
Self-citationTime 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
Self-citationClouds Proportionate Medical Data Stream Analytics for Internet of Things-Based Healthcare Systems
Kumar, P. M., Hong, C. S., Afghah, F., Manogaran, G., Yu, K., Hua, Q., & Gao, J. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3106387 · Cited by 79 · see citing works
Shamim, D. M. M. I., Hamid, A. B. A., Nyamasvisva, T. E., & Rafi, N. S. B. (2025) · Modelling—International Open Access Journal of Modelling in Engineering Science · doi:10.3390/modelling6020035 · Cited by 57 · see citing works
A Convolutional Transformer Model for Multivariate Time Series Prediction
Kim, D.-K., & Kim, K. (2022) · IEEE Access · doi:10.1109/access.2022.3203416 · Cited by 56 · see citing works
Liu, J., Capurro, D., Nguyen, A., & Verspoor, K. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00474-9 · Cited by 50 · see citing works
Nguyen, D. C., Salamak, M., Katunin, A., Poprawa, G., & Gerges, M. (2024) · Engineering Structures · doi:10.1016/j.engstruct.2024.118431 · Cited by 39 · see citing works
PSO based Hyperparameter tuning of CNN Multivariate Time- Series Analysis
Utama, A. B. P., Wibawa, A. P., Muladi, M., & Nafalski, A. (2022) · Jurnal Online Informatika · doi:10.15575/join.v7i2.858 · Cited by 33 · see citing works
Kuo, R. J., & Zulvia, F. E. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107711 · Cited by 22 · see citing works
Anghel, I. M., Cioara, T., Bevilacqua, R., Barbarossa, F., Grimstad, T., Hellman, R., Solberg, A., Boye, L. T., Anchidin, O., Nemes, A., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-03332-w · Cited by 20 · see citing works
Predictive analytics for step-up therapy: Supervised or semi-supervised learning?
Morid, M. A., Lau, M., & Fiol, G. D. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103842 · Cited by 18 · see citing works
Self-citationNew Hybrid Deep Learning Models to Predict Cost From Healthcare Providers in Smart Hospitals
Bhatti, M. H., Javaid, N., Mansoor, B., Alrajeh, N. A., Aslam, M., & Asad, M. (2023) · IEEE Access · doi:10.1109/access.2023.3336424 · Cited by 16 · see citing works
Cost prediction for product development using hybrid deep learning model: a meta-heuristic model
Abdel-Jaber, M., Makhoul, N., Abdel-Jaber, M., & Beale, R. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20437-y · Cited by 14 · see citing works
Efficient Deep Learning Models for Predicting Super-Utilizers in Smart Hospitals
Jaffar, M., Shafiq, S., Shahzadi, N., Alrajeh, N. A., Jamil, M., & Javaid, N. (2023) · IEEE Access · doi:10.1109/access.2023.3305379 · Cited by 14 · see citing works
A robust neural network for privacy-preserving heart rate estimation in remote healthcare systems
Islam, T. N., & Imtiaz, H. (2024) · Healthcare Analytics · doi:10.1016/j.health.2024.100329 · Cited by 13 · see citing works
Neural network methods for diagnosing patient conditions from cardiopulmonary exercise testing data
Brown, D. E., Sharma, S., Jablonski, J. A., & WELTMAN, A. L. (2022) · BioData Mining · doi:10.1186/s13040-022-00299-6 · Cited by 12 · see citing works
Nomura, Y., Ishii, Y., Chiba, Y., Suzuki, S., Suzuki, A., Suzuki, S., Morita, K., Tanabe, J., Yamakawa, K., Ishiwata, Y., et al. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18020565 · Cited by 10 · see citing works
Predicting hospitalization costs for pulmonary tuberculosis patients based on machine learning
Fan, S., Abulizi, A., You, Y., Huang, C., Yimit, Y., Li, Q., Zou, X., & Nijiati, M. (2024) · BMC Infectious Diseases · doi:10.1186/s12879-024-09771-6 · Cited by 9 · see citing works
Zhang, L., Shi, Y., & Wang, D. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3521248 · Cited by 9 · see citing works
Uncertainty-Aware Self-Attention Model for Time Series Prediction with Missing Values
Li, J., Wang, C., Su, W., Ye, D., & Wang, Z. (2025) · Fractal and Fractional · doi:10.3390/fractalfract9030181 · Cited by 9 · see citing works
Dave, D., Vyas, K., Jayagopal, J. K., Garcia, A., Erraguntla, M., & Lawley, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-22316-4 · Cited by 13 · see citing works
Song, L., Xu, Y., Wang, M., & Leng, Y. (2021) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2021.104428 · Cited by 9 · see citing works
Chen, W., & Li, J. (2021) · Healthcare · doi:10.3390/healthcare9080992 · Cited by 8 · see citing works
Morid, M. A., & Sheng, O. R. L. (2025) · Information Systems Research · doi:10.1287/isre.2021.0643 · Cited by 6 · see citing works
Self-citationErniyazov, S., & Lim, C. G. (2025) · Energy Reports · doi:10.1016/j.egyr.2025.03.063 · Cited by 6 · see citing works
Huang, Y., Zheng, Z., Ma, M., Xin, X., Liu, H., Fei, X., Wei, L., & Chen, H. (2022) · Journal of Medical Internet Research · doi:10.2196/37486 · Cited by 6 · see citing works
Klunder, L. A., Guericke, D., Bemthuis, R., Koenderink, M., & Machado, M. (2026) · International Journal of Data Science and Analytics · doi:10.1007/s41060-026-01138-1 · Cited by 4 · see citing works
AB-LSTM: Attention Bidirectional Long Short-Term Memory for Multivariate Time-Series Forecasting
Zhou, X., Pranolo, A., & Mao, Y. (2023) · International Conference on Computer, Electronics & Electrical Engineering & their Applications (IC2E3) · doi:10.1109/ic2e357697.2023.10262559 · Cited by 4 · see citing works
A deformable convolutional time-series prediction network with extreme peak and interval calibration
Bi, X., Zhang, G., Lu, L., Yuan, G., Zhao, X., Sun, Y., & Ma, Y. (2023) · GeoInformatica · doi:10.1007/s10707-023-00502-8 · Cited by 3 · see citing works
Akter, S. B., Akter, S., Hasan, R., Hasan, M. M., Islam, A. M. T., Pias, T. S., Fresneda, J. E., Alam, M. G. R., & Eisenberg, D. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104770 · Cited by 3 · see citing works
Multidimensional Time Series Analysis for Anomaly Pattern Detection and Interpretation
Deng, Z., Kang, J., & Wang, X. (2024) · IEEE International Conference on Power, Electronics and Computer Applications (ICPECA) · doi:10.1109/icpeca60615.2024.10471100 · Cited by 2 · see citing works
Sobhana, M., Ch, S. C., Koneru, S., Mohan, G. K., & Kumar, K. K. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19759-8 · Cited by 2 · see citing works
Scalable Hybrid Deep Models for Individual Pharmacy Cost Prediction
Ashfaq, M. T., Javaid, N., Alrajeh, N. A., Asim, Y., & Akber, S. M. A. (2025) · IEEE Access · doi:10.1109/access.2025.3541750 · Cited by 2 · see citing works
Artificial Intelligence in Anesthesia Control and Monitoring
Pei, B. (2023) · Springer eBooks · doi:10.1007/978-981-99-5925-9_4 · Cited by 1 · see citing works
Deep Fusion Network with Feature Engineering for Discharge Risk Assessment
Wang, L., Li, R., Wang, S., Yan, S., Ma, L., & Xing, Y. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-3752-2_2 · Cited by 1 · see citing works
Nonhypothesis-Driven Research: Data Mining and Knowledge Discovery
Cummins, M. R., Nachimuthu, S., Abdelrahman, S., Facelli, J. C., & Gouripeddi, R. (2023) · Health informatics · doi:10.1007/978-3-031-27173-1_20 · Cited by 1 · see citing works
Self-citationHani, S. B., & Ahmad, M. M. (2025) · Electronic Journal of General Medicine · doi:10.29333/ejgm/15854 · Cited by 1 · see citing works
Huang, Y., Zheng, Z., Ma, M., Xin, X., Liu, H., Fei, X., Lan, W., & Chen, H. (2022) · doi:10.2196/preprints.37486
Long-Term Time Series Prediction with Extreme Peak and Interval Calibration
Bi, X., Zhang, G., Yuan, M., Huang, X., & Zhao, X. (2022) · International Conference on Behavioral, Economic, and Socio-Cultural Computing · doi:10.1109/besc57393.2022.9994938
Islam, T. N., & Imtiaz, H. (2023) · Research Square · doi:10.21203/rs.3.rs-3448229/v1
Health Care Data Mining using Intelligent Classification Techniques
Antil, J., Rishi, R., Sharma, S., & Yadav, R. (2023) · International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · doi:10.1109/icac3n60023.2023.10541547
Pribadi, A. I., Mustafa, N. B. A., Syahbanu, A., Dani, D. H. T. P., & Jumantoro, E. (2025) · International Conference on Technology and Policy in Energy and Electric Power (ICT-PEP) · doi:10.1109/ict-pep67281.2025.11232387
Farajpour, A., & Ingman, W. V. (2026) · Mathematics · doi:10.3390/math14040720
Benabbes-Taarji, H., Do, J.-M., & Moulignier, T. (2026) · Nuclear Science and Engineering · doi:10.1080/00295639.2026.2659999
Dhinesh Kumar, K. S., & Annamalai, R. (2026) · International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE) · doi:10.1109/rmkmate69073.2026.11518942
Abdulsahib, H. M., Saeed, Z. A., & Al-Moadhen, A. A. (2026) · Results in Engineering · doi:10.1016/j.rineng.2026.112017
Kowalska, A., Nowak, P., Zielinski, T., & Mazur, K. (2024) · Journal of Health Informatics and Digital Systems · doi:10.68159/e263842856
- Authors
- Morid, M. A., Sheng, O. R. L., Kawamoto, K., & Abdelrahman, S.
- Affiliations
- Santa Clara University (US)
- University of Utah (US)
- Cairo University (EG)
- Corresponding author
- Abdelrahman, Samir
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 111
- Pages
- 103565
- ISSN
- 1532-0464 (ISSN-L)
- OpenAlex
- W3085023160
- Cited by
- 49
- References
- 49
- Citations by year
- 2026: 5 · 2025: 12 · 2024: 9 · 2023: 8 · 2022: 7 · 2021: 7 · 2020: 1
- FWCI
- 2.26
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- University of Utah
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- healthcare cost prediction, multivariate time series, convolutional neural networks, health insurance claims, temporal pattern detection, medical claims, pharmacy claims data, representation learning, transfer learning, external validation, temporal convolutional network, recurrent neural networks
- MeSH terms
- Humans, Cohort Studies, Neural Networks, Computer, Health Care Costs
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
