Abstract
Causal inference platform is rather useful in the medical domain, and relies heavily on data quality and prior knowledge. Nowadays, the advancement of medical methods and the application of high-technology devices have brought a large amount of multi-source heterogeneous multi-modal data, which supports data-driven causal inference. In addition, when we need causal inference to assist medical research, it will be more reliable if we consider both data and prior knowledge. However, most of the current causal inference platforms only involve parts of the causal inference process, ranging from multi-modal data fusion, exploratory data analysis with doctor-in-loop, and causal inference based on data lake. A unified closed loop of causal inference that can be applied to most datasets and improve the reliability of research in the medical domain has not been established yet. Therefore, we propose a hybrid medical causal inference platform based on data lake, which is both data-driven and knowledge-driven. It can manage and fuse massive multi-heterogeneous data, interact well with doctors in data exploration process, and offer a convenient medical causal inference interface. This platform has been used on Knee Osteoarthritis disease, which proves the effectiveness of our work.
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Zhang, Y., Sheng, M., et al.: HKGB: an inclusive, extensible, intelligent, semi-auto-constructed knowledge graph framework for healthcare with clinicians’ expertise incorporated. Inf. Process. Manage. 57(6), 102324 (2020)
Du, J., Michalska, S., Subramani, S., Wang, H., Zhang, Y.: Neural attention with character embeddings for hay fever detection from twitter. Health Inf. Sci. Syst. 7(1), 1–7 (2019). https://doi.org/10.1007/s13755-019-0084-2
Jasu, J., Tolonen, T., et al.: Combined longitudinal clinical and autopsy phenomic assessment in lethal metastatic prostate cancer: recommendations for advancing precision medicine. Eur. Urol. Open Sci. 30, 47–62 (2021)
Wartner, S., Girardi, D., Wiesinger-Widi, M., Trenkler, J., Kleiser, R., Holzinger, A.: Ontology-guided principal component analysis: reaching the limits of the doctor-in-the-loop. In: Renda, M.E., Bursa, M., Holzinger, A., Khuri, S. (eds.) ITBAM 2016. LNCS, vol. 9832, pp. 22–33. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-43949-5_2
Castro, D.C., Walker, I., Glocker, B.: Causality matters in medical imaging. Nat. Commun. 11(1), 1–10 (2020)
Qi, Z., Yaping, W., et al.: Research on influencing factors of prognosis treatment for lung cancer patients based on causality. Comput. Technol. Dev. 31(08), 145–149 (2021)
Decruyenaere, A., Steen, J., et al.: The obesity paradox in critically ill patients: a causal learning approach to a casual finding. Crit. Care 24(1), 1–11 (2020)
Mitchell, J., Naddaf, R., Davenport, S.: A medical microcomputer database management system. Methods Inf. Med. 24(2), 73–78 (1985)
Sebaa, A., Chikh, F., Nouicer, A., Tari, A.K.: Medical big data warehouse: architecture and system design, a case study: improving healthcare resources distribution. J. Med. Syst. 42(4), 1–16 (2018). https://doi.org/10.1007/s10916-018-0894-9
Ren, P., et al.: MHDP: an efficient data lake platform for medical multi-source heterogeneous data. In: Xing, C., Fu, X., Zhang, Y., Zhang, G., Borjigin, C. (eds.) WISA 2021. LNCS, vol. 12999, pp. 727–738. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87571-8_63
Cominetti, O., et al.: Identification of a novel clinical phenotype of severe malaria using a network-based clustering approach. Sci. Rep. 8(1), 1–10 (2018)
Pareekutty, N.M., Kadam, S., Ankalkoti, B., Balasubramanian, S., Anilkumar, B.: Gastrectomy with D2 lymphadenectomy for carcinoma of the stomach in a stand-alone cancer centre in rural India. Indian J. Surg. Oncol. 11(2), 256–262 (2020). https://doi.org/10.1007/s13193-020-01059-w
Sauver, J.L.S., et al.: Peer reviewed: Rochester epidemiology project data exploration portal. Prev. Chronic Dis. 15 (2018)
Wu, J.Q., et al.: Automated causal inference in application to randomized controlled clinical trials. Nat. Mach. Intell. 4(5), 436–444 (2022)
Whata, A., Chimedza, C.: Evaluating uses of deep learning methods for causal inference. IEEE Access 10, 2813–2827 (2022)
Wang, X., Xu, X., Tong, W., Roberts, R., Liu, Z.: InferBERT: a transformer-based causal inference framework for enhancing pharmacovigilance. Front. Artif. Intell. 4, 659622 (2021)
Pandey, D., Wang, H., et al.: Automatic breast lesion segmentation in phase preserved DCE-MRIs. Health Inf. Sci. Syst. 10(1), 1–19 (2022). https://doi.org/10.1007/s13755-022-00176-w
Sarki, R., Ahmed, K., Wang, H., Zhang, Y.: Automated detection of mild and multi-class diabetic eye diseases using deep learning. Health Inf. Sci. Syst. 8(1), 1–9 (2020). https://doi.org/10.1007/s13755-020-00125-5
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This work was supported by National Key R &D Program of China (2020AAA0109603).
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Ren, P. et al. (2022). A Hybrid Medical Causal Inference Platform Based on Data Lake. In: Traina, A., Wang, H., Zhang, Y., Siuly, S., Zhou, R., Chen, L. (eds) Health Information Science. HIS 2022. Lecture Notes in Computer Science, vol 13705. Springer, Cham. https://doi.org/10.1007/978-3-031-20627-6_13
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