Federated Learning Based on Extremely Sparse Series Clinic Monitoring Data

doi:10.12142/ZTECOM.202203004

ZTE Communications ›› 2022, Vol. 20 ›› Issue (3): 27-34.DOI: 10.12142/ZTECOM.202203004

收稿日期:2022-08-18 出版日期:2022-09-13 发布日期:2022-09-14

Federated Learning Based on Extremely Sparse Series Clinic Monitoring Data

LU Feng¹, GU Lin¹(), TIAN Xuehua¹, SONG Cheng¹, ZHOU Lun²

^1.School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
^2.Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430074, China

Received:2022-08-18 Online:2022-09-13 Published:2022-09-14
About author:LU Feng received her MS and PhD degrees in computer science from Huazhong University of Science and Technology, China in 1997 and 2006. She is currently an associate professor in School of Computer Science and Technology, Huazhong University of Science and Technology. Her current research interests include big data, artificial intelligence and distributed computing. She has authored two books and over 20 papers in refereed journals and conferences in these areas. She is a member of CCF and a senior member of the first Session of Chinese Hospital Association, Health Data Application and Management Committee. She was the recipient of three teaching achievement and curriculum development awards.|GU Lin (lingu@hust.edu.cn) received her MS and PhD degrees in computer science from University of Aizu, Fukushima, Japan in 2011 and 2015. She is currently an associate professor in School of Computer Science and Technology, Huazhong University of Science and Technology, China. Her current research interests include serverless computing, network function virtualization, cloud computing, software-defined networking, and data center networking. She has authored two books and over 40 papers in refereed journals and conferences in these areas. She is a member of IEEE and a senior number of CCF.|TIAN Xuehua received her MS degree from the School of Computer Science and Technology, Huazhong University of Science and Technology, China in 2022. She works on medical data mining and machine learning. She mainly focuses on working with sparse time series data.|SONG Cheng received his bachelor’s degree from the School of Computer Science and Artificial Intelligence, Wuhan University of Technology, China in 2021. He majored in software engineering. He is working on data mining and machine learning for an MS degree at Huazhong University of Science and Technology, China.|ZHOU Lun received his MS Degree in Tongji Medical College, Huazhong University of Science and Technology, China in 2003. He is an associate professor of geriatrics at Tongji Hospital. His research interests include the pathogenesis of congenital heart disease and early warning of severe diseases in the elderly. He has published more than 20 papers in refereed journals such as PNAS and JMCC.
Supported by:
Hubei Provincial Development and Reform Commission Program “Hubei Big Data Analysis Platform and Intelligent Service Project for Medical and Health”

摘要/Abstract

Abstract:

Decentralized machine learning frameworks, e.g., federated learning, are emerging to facilitate learning with medical data under privacy protection. It is widely agreed that the establishment of an accurate and robust medical learning model requires a large number of continuous synchronous monitoring data of patients from various types of monitoring facilities. However, the clinic monitoring data are usually sparse and imbalanced with errors and time irregularity, leading to inaccurate risk prediction results. To address this issue, this paper designs a medical data resampling and balancing scheme for federated learning to eliminate model biases caused by sample imbalance and provide accurate disease risk prediction on multi-center medical data. Experimental results on a real-world clinical database MIMIC-IV demonstrate that the proposed method can improve AUC (the area under the receiver operating characteristic) from 50.1% to 62.8%, with a significant performance improvement of accuracy from 76.8% to 82.2%, compared to a vanilla federated learning artificial neural network (ANN). Moreover, we increase the model’s tolerance for missing data from 20% to 50% compared with a stand-alone baseline model.

Key words: federate learning, time-series electronic health records (EHRs), feature engineering, imbalance sample

. [J]. ZTE Communications, 2022, 20(3): 27-34.

LU Feng, GU Lin, TIAN Xuehua, SONG Cheng, ZHOU Lun. Federated Learning Based on Extremely Sparse Series Clinic Monitoring Data[J]. ZTE Communications, 2022, 20(3): 27-34.

图/表 8

Figure 1 Sparse data with time irregularity in the Medical Information Mart for Intensive Care IV (MIMIC-IV) dataset

Figure 2 Overview of the proposed medical data resampling and balancing scheme

Figure 3 Data window

Figure 4 Definition of positive sample

Table 1 LSTM prediction results

Time/min	AUC	ACC	Pre.	Sens.	Spe.	F1
15	0.502	0.990	0.044	0.013	0.998	0.020
10	0.512	0.991	0.085	0.026	0.998	0.040
5	0.506	0.993	0.229	0.013	0.999	0.025

Table 2 Prediction results of the proposed model

AUC	ACC	Rec.	Pre.	Sens.	Spe.	F1
0.628	0.822	0.267	0.878	0.989	0.267	0.409

Table 3 Comparison of ablation experiments

	AUC	ACC	Rec.	Pre.	Sens.	Spe.	F1
Before Rebalancing	0.501	0.768	0.003	0.333	0.998	0.003	0.005
After Rebalancing	0.628	0.822	0.267	0.878	0.989	0.267	0.409

Figure 5 Comparison of SHAP values

参考文献 27

1	DENNY J C, COLLINS F S. Precision medicine in 2030—seven ways to transform healthcare [J]. Cell, 2021, 184(6): 1415–1419. DOI: 10.1016/j.cell.2021.01.015 DOI
2	RAGHUNATH S, ULLOA CERNA A E, JING L Y, et al. Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network [J]. Nature medicine, 2020, 26(6): 886–891. DOI: 10.1038/s41591-020-0870-z DOI
3	GAO Y, CAI G Y, FANG W, et al. Machine learning based early warning system enables accurate mortality risk prediction for COVID-19 [J]. Nature communications, 2020, 11: 5033. DOI: 10.1038/s41467-020-18684-2 DOI
4	TOMAŠEV N, GLOROT X, RAE J W, et al. A clinically applicable approach to continuous prediction of future acute kidney injury [J]. Nature, 2019, 572(7767): 116–119. DOI: 10.1038/s41586-019-1390-1 DOI
5	LIANG H Y, TSUI B Y, NI H, et al. Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence [J]. Nature medicine, 2019, 25(3): 433–438. DOI: 10.1038/s41591-018-0335-9 DOI
6	KOCH M. Artificial intelligence is becoming natural [J]. Cell, 2018, 173(3): 531–533. DOI: 10.1016/j.cell.2018.04.007 DOI
7	BACCHI S, TAN Y, OAKDEN‐RAYNER L, et al. Machine learning in the prediction of medical inpatient length of stay [J]. Internal medicine journal, 2022, 52(2): 176–185
8	VAROQUAUX G, CHEPLYGINA V. Machine learning for medical imaging: methodological failures and recommendations for the future [J]. NPJ digital medicine, 2022, 5(1): 1–8
9	COUDRAY N, OCAMPO P S, SAKELLAROPOULOS T, et al. Classification and mutation prediction from non‐small cell lung cancer histopathology images using deep learning [J]. Nature medicine, 2018, 24(10): 1559–1567. DOI: 10.1038/s41591-018-0177-5 DOI
10	YANG Q, LIU Y, CHENG Y, et al. Synthesis lectures on artificial intelligence and machine learning: federated learning [M]. Berlin Heidelberg, Germany: Springer, 2019: 1–207. DOI: 10.2200/s00960ed2v01y201910aim043 DOI
11	KELLY C J, KARTHIKESALINGAM A, SULEYMAN M, et al. Key challenges for delivering clinical impact with artificial intelligence [J]. BMC medicine, 2019, 17(1): 195. DOI: 10.1186/s12916-019-1426-2 DOI
12	LI R W, CHEN Y, RITCHIE M D, et al. Electronic health records and polygenic risk scores for predicting disease risk [J]. Nature reviews genetics, 2020, 21(8): 493–502. DOI: 10.1038/s41576-020-0224-1 DOI
13	SHILO S, ROSSMAN H, SEGAL E. Axes of a revolution: challenges and promises of big data in healthcare [J]. Nature medicine, 2020, 26(1): 29–38. DOI: 10.1038/s41591-019-0727-5 DOI
14	JOHNSON A, BULGARELLI L, POLLARD T, et al. MIMIC-IV-ED [DB]. PhysioNet, 2021. DOI: 10.13026/as7t-c445 DOI
15	LI J, YAN X S, CHAUDHARY D, et al. Imputation of missing values for electronic health record laboratory data [J]. NPJ digital medicine, 2021, 4(1): 147. DOI: 10.1038/s41746-021-00518-0 DOI
16	WU J R, VODOVOTZ Y, ABDELHAMID S, et al. Multi-omic analysis in injured humans: patterns align with outcomes and treatment responses [J]. Cell reports medicine, 2021, 2(12): 100478. DOI: 10.1016/j.xcrm.2021.100478 DOI
17	WEERAKODY P B, WONG K W, WANG G J, et al. A review of irregular time series data handling with gated recurrent neural networks [J]. Neurocomputing, 2021, 441: 161–178. DOI: 10.1016/j.neucom.2021.02.046 DOI
18	KUMAR Y, SINGLA R. Federated learning systems for healthcare: perspective and recent progress [J]. Federated learning systems, 2021: 141–156
19	VAID A, JALADANKI S K, XU J, et al. Federated learning of electronic health records improves mortality prediction in patients hospitalized with covid-19 [EB/OL]. (2020-08-11)[2021-11-21].
20	SATTLER F, WIEDEMANN S, MULLER K R, et al. Robust and communication-efficient federated learning from non-i.i.d. data [J]. IEEE transactions on neural networks and learning systems, 2020, 31(9): 3400–3413. DOI: 10.1109/TNNLS.2019.2944481 DOI
21	RAMASWAMY S, MATHEWS R, RAO K, et al. Federated learning for emoji prediction in a mobile keyboard [EB/OL]. (2019-06-11)[2021-11-21].
22	KANG J W, XIONG Z H, NIYATO D, et al. Reliable federated learning for mobile networks [J]. IEEE wireless communications, 2020, 27(2): 72–80. DOI: 10.1109/MWC.001.1900119 DOI
23	WEI R M, WANG J Y, SU M M, et al. Missing value imputation approach for mass spectrometry-based metabolomics data [J]. Scientific reports, 2018, 8(1): 663. DOI: 10.1038/s41598-017-19120-0 DOI
24	LUNDBERG S, LEE S I. A unified approach to interpreting model predictions [EB/OL]. (2017-11-25)[2021-12-05].
25	ANNANE D, BELLISSANT E, CAVAILLON J M. Septic shock [J]. The lancet, 2005, 365(9453): 63–78. DOI: 10.1016/S0140-6736(04)17667-8 DOI
26	ASTIZ M E, RACKOW E C. Septic shock [J]. The lancet, 1998, 351(9114): 1501–1505. DOI: 10.1016/S0140-6736(98)01134-9 DOI
27	RIVERS E P, MCINTYRE L, MORRO D C, et al. Early and innovative interventions for severe sepsis and septic shock: taking advantage of a window of opportunity [J]. CMAJ, 2005, 173(9): 1054–1065. DOI: 10.1503/cmaj.050632 DOI

Federated Learning Based on Extremely Sparse Series Clinic Monitoring Data

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 27

相关文章 0

编辑推荐

Metrics