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J Clin Med. 2019 Jul 9;8(7). pii: E995. doi: 10.3390/jcm8070995.

Artificial Intelligence Prediction Model for the Cost and Mortality of Renal Replacement Therapy in Aged and Super-Aged Populations in Taiwan.

Lin SY1,2, Hsieh MH3, Lin CL4,5, Hsieh MJ6, Hsu WH1,7, Lin CC1,8, Hsu CY1, Kao CH9,10,11.

Author information

1
Graduate Institute of Biomedical Sciences, China Medical University, Taichung 404, Taiwan.
2
Division of Nephrology and Kidney Institute, China Medical University Hospital, Taichung 404, Taiwan.
3
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA.
4
Management Office for Health Data, China Medical University Hospital, Taichung 404, Taiwan.
5
College of Medicine, China Medical University, Taichung 404, Taiwan.
6
Department of Medicine, Poznan University of Medical Sciences, 061 Poznan, Poland.
7
Division of Pulmonary and Critical Care Medicine, China Medical University Hospital and China Medical University, Taichung 404, Taiwan.
8
Department of Family Medicine, China Medical University Hospital, Taichung 404, Taiwan.
9
Graduate Institute of Biomedical Sciences, China Medical University, Taichung 404, Taiwan. d10040@mail.cmuh.org.tw.
10
Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung 404, Taiwan. d10040@mail.cmuh.org.tw.
11
Department of Bioinformatics and Medical Engineering, Asia University, Taichung 404, Taiwan. d10040@mail.cmuh.org.tw.

Abstract

BACKGROUND:

Prognosis of the aged population requiring maintenance dialysis has been reportedly poor. We aimed to develop prediction models for one-year cost and one-year mortality in aged individuals requiring dialysis to assist decision-making for deciding whether aged people should receive dialysis or not.

METHODS:

We used data from the National Health Insurance Research Database (NHIRD). We identified patients first enrolled in the NHIRD from 2000-2011 for end-stage renal disease (ESRD) who underwent regular dialysis. A total of 48,153 Patients with ESRD aged ≥65 years with complete age and sex information were included in the ESRD cohort. The total medical cost per patient (measured in US dollars) within one year after ESRD diagnosis was our study's main outcome variable. We were also concerned with mortality as another outcome. In this study, we compared the performance of the random forest prediction model and of the artificial neural network prediction model for predicting patient cost and mortality.

RESULTS:

In the cost regression model, the random forest model outperforms the artificial neural network according to the mean squared error and mean absolute error. In the mortality classification model, the receiver operating characteristic (ROC) curves of both models were significantly better than the null hypothesis area of 0.5, and random forest model outperformed the artificial neural network. Random forest model outperforms the artificial neural network models achieved similar performance in the test set across all data.

CONCLUSIONS:

Applying artificial intelligence modeling could help to provide reliable information about one-year outcomes following dialysis in the aged and super-aged populations; those with cancer, alcohol-related disease, stroke, chronic obstructive pulmonary disease (COPD), previous hip fracture, osteoporosis, dementia, and previous respiratory failure had higher medical costs and a high mortality rate.

KEYWORDS:

National Health Insurance Research Database (NHIRD); artificial intelligence modeling; dialysis; end-stage renal disease (ESRD)

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