Constructing Health Management Information System for Major Chronic Diseases Based on Large Language Model

Wu Tianxing; Cao Xudong; Bi Sheng; Chen Ya; Cai Pingqiang; Sha Hangyu; Qi Guilin; Wang Haofen

doi:10.7544/issn1000-1239.202440570

Journal of Computer Research and Development > 2025 > Accepted Manuscript > DOI: 10.7544/issn1000-1239.202440570 CSTR: 32373.14.issn1000-1239.202440570

Wu Tianxing, Cao Xudong, Bi Sheng, Chen Ya, Cai Pingqiang, Sha Hangyu, Qi Guilin, Wang Haofen. Constructing Health Management Information System for Major Chronic Diseases Based on Large Language Model[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202440570

Citation:

PDF (1788 KB)

Constructing Health Management Information System for Major Chronic Diseases Based on Large Language Model

1.
School of Computer Science and Engineering, Southeast University, Nanjing 211189
2.
Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, Nanjing 211189
3.
School of Law , Southeast University, Nanjing 211189
4.
Jiangsu Yahuan Software Co., Ltd., Nanjing 210024
5.
Medical School, Nanjing University, Nanjing 210008
6.
College of Design and Innovation, Tongji University, Shanghai 200092

More Information

Author Bio:
Wu Tianxing: born in 1990. PhD, associate professor. Senior member of CCF. His main research interests include knowledge graph and large language model

Cao Xudong: born in 1999. Master candidate. His main research interests include natural language processing and knowledge graph

Bi Sheng: born in 1990. PhD, lecturer. His main research interests include large language model and natural language processing

Chen Ya: born in 1984. Master. His main research interest includes medical health management

Cai Pingqiang: born in 1987. PhD, associate professor. His main research interest includes medical health management

Sha Hangyu: born in 2001. Master candidate. His main research interests include natural language processing and knowledge graph

Qi Guilin: born in 1977. PhD, professor. His main research interests include knowledge representation and reasoning, and knowledge graph

Wang Haofen: born in 1982. PhD, professor. Senior member of CCF. His main research interests include knowledge graph and large language model
Received Date: June 20, 2024
Accepted Date: January 25, 2025
Available Online: January 25, 2025

Graphical Abstract

Abstract

Abstract

With the global population aging and lifestyle changing, the management and treatment of chronic diseases become increasingly important. Chronic diseases include cardiovascular diseases, diabetes, chronic respiratory diseases, etc. They require long-term or even lifelong health management, the core of which is to design and implement long-term health plans, including balanced dieting, appropriate exercising, regular inspection, and medication management. In recent years, large language models make progress in the medical field but do not focus on chronic disease health management. Therefore, they lack understanding of Chinese dietary habits and culture. These medical large language models also have limited capabilities in handling numerical information. To address these issues, this paper constructs a chronic disease health management information system based on large language model. By integrating foundational knowledge of chronic diseases, health management guidelines, and actual health management plans as domain data, this paper trains the QingTing large language model as the core of the system for effectively answering health-related questions. Additionally, the system introduces a tool enhancement strategy, improving the QingTing’s ability to handle numerical information in health data by invoking tools. The system also adopts a retrieval-augmented generation technology based on uncertain knowledge graph to enhance the accuracy and reliability of QingTing. Experiments on the chronic disease health management information system based on a large language model demonstrate that QingTing significantly outperforms other baseline large language models in health management dialogues, and verify the effectiveness of the designed tool enhancement and retrieval-augmented methods.
- information system,
- large language model,
- health management,
- chronic disease,
- retrieval-augmented generation

FullText(HTML)

References (36)

References

[1]	He Kai, Mao Rui, Lin Qika, et al. A survey of large language models for healthcare: From data, technology, and applications to accountability and ethics[J]. arXiv preprint, arXiv: 2310.05694, 2023
[2]	Chowdhery A, Narang S, Devlin J, et al. PaLM: Scaling language modeling with pathways[J]. Journal of Machine Learning Research, 2023, 24(240): 1−113
[3]	Touvron H, Lavril T, Izacard G, et al. LLaMA: Open and efficient foundation language models[J]. arXiv preprint, arXiv: 2302.13971, 2023
[4]	Achiam J, Adler S, Agarwal S, et al. GPT−4 technical report[J]. arXiv preprint, arXiv: 2303.08774, 2023
[5]	Gao Yunfan, Xiong Yun, Gao Xinyu, et al. Retrieval-augmented generation for large language models: A survey[J]. arXiv preprint, arXiv: 2312.10997, 2023
[6]	Yang Xi, Chen Aokun, PourNejatian N, et al. GatorTron: A large clinical language model to unlock patient information from unstructured electronic health records[J]. arXiv preprint, arXiv: 2203.03540, 2022
[7]	Peng Cheng, Yang Xi, Chen Aokun, et al. A study of generative large language model for medical research and healthcare[J]. NPJ Digital Medicine, 2023, 6(1): 210 doi: 10.1038/s41746-023-00958-w
[8]	Brown T, Mann B, Ryder N, et al. Language models are few-shot learners[C]//Proc of the 34th Int Conf on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc, 2020: 159
[9]	Ouyang Long, Wu J, Jiang Xu, et al. Training language models to follow instructions with human feedback[C]//Proc of the 36th Int Conf on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc, 2024: 2011
[10]	Singhal K, Azizi S, Tu Tao, et al. Large language models encode clinical knowledge[J]. Nature, 2023, 620(7972): 172−180 doi: 10.1038/s41586-023-06291-2
[11]	Singhal K, Tu Tao, Gottweis J, et al. Towards expert-level medical question answering with large language models[J]. arXiv preprint, arXiv: 2305.09617, 2023
[12]	Anil R, Dai A, Firat O, et al. PaLM 2 technical report[J]. arXiv preprint, arXiv: 2305.10403, 2023
[13]	Li Yunxiang, Li Zihan, Zhang Kai, et al. ChatDoctor: A medical chat model fine-tuned on a large language model meta-AI (LLaMA) using medical domain knowledge[J]. Cureus, 2023, 15(6): e40895
[14]	Han Tianyu, Adams L C, Papaioannou J M, et al. MedAlpaca ― An open-source collection of medical conversational AI models and training data[J]. arXiv preprint, arXiv: 2304.08247, 2023
[15]	Xiong Honglin, Wang Sheng, Zhu Yitao, et al. DoctorGLM: Fine-tuning your chinese doctor is not a herculean task[J]. arXiv preprint, arXiv: 2304.01097, 2023
[16]	Chen Yirong, Wang Zhenyu, Zheng Huimin, et al. BianQue: Balancing the questioning and suggestion ability of health LLMs with multi-turn health conversations polished by ChatGPT[J]. arXiv preprint, arXiv: 2310.15896, 2023
[17]	Du Zhengxiao, Qian Yujie, Liu Xiao, et al. GLM: General language model pretraining with autoregressive blank infilling[C]//Proc of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA: ACL, 2022: 320−335
[18]	Zhang Hongbo, Chen Junying, Jiang Feng, et al. HuatuoGPT, towards taming language model to be a doctor[C]//Findings of the Association for Computational Linguistics (EMNLP 2023). Stroudsburg, PA: ACL, 2023: 10859−10885
[19]	Yang Aiyuan, Xiao Bin, Wang Bingning, et al. Baichuan 2: Open large-scale language models[J]. arXiv preprint, arXiv: 2309.10305, 2023
[20]	Montagna S, Ferretti S, Klopfenstein L C, et al. Data decentralisation of LLM-based chatbot systems in chronic disease self-management[C]//Proc of the 2023 ACM Conf on Information Technology for Social Good. New York: ACM, 2023: 205−212
[21]	Dao Dung, Teo J Y C, Wang Wenru, et al. LLM-powered multimodal AI conversations for diabetes prevention[C]//Proc of the 1st ACM Workshop on AI-Powered Q&A Systems for Multimedia. New York: ACM, 2024: 1−6
[22]	Joulin A, Grave E, Bojanowski P, et al. Bag of tricks for efficient text classification[C]//Proc of the 15th Conf of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers). Stroudsburg, PA: ACL, 2017: 427−431
[23]	Lambora A, Gupta K, Chopra K. Genetic algorithm ― A literature review[C]//Proc of the 2019 Int Conf on Machine Learning, Big Data, Cloud and Parallel Computing. Piscataway, NJ: IEEE, 2019: 380−384
[24]	Shukla A, Pandey H M, Mehrotra D. Comparative review of selection techniques in genetic algorithm[C]//Proc of the 1st Int Conf on Futuristic Trends on Computational Analysis and Knowledge Management. Piscataway, NJ: IEEE, 2015: 515−519
[25]	Alibrahim H, Ludwig S A. Hyperparameter optimization: Comparing genetic algorithm against grid search and bayesian optimization[C]//Proc of the 2021 IEEE Congress on Evolutionary Computation. Piscataway, NJ: IEEE, 2021: 1551−1559
[26]	中国营养学会. 中国居民膳食营养素参考摄入量(2023版)[M]. 北京:人民卫生出版社,2023 Chinese Nutrition Society. Reference Intake of Dietary Nutrients for Chinese Residents[M]. Beijing: People’s Medical Publishing House, 2023 (in Chinese)
[27]	Jia Shengbin, Xiang Yang, Chen Xiaojun. Triple trustworthiness measurement for knowledge graph[C]//Proc of the World Wide Web Conf. New York: ACM, 2019: 2865−2871
[28]	Zhang Jiatao, Wu Tianxng, Qi Guilin. Gaussian metric learning for few-shot uncertain knowledge graph completion[C]//Proc of the 26th Int Conf on Database Systems for Advanced Applications, Part I. Berlin: Springer, 2021: 256−271
[29]	Ye Xi, Yavuz S, Hashimoto K, et al. RNG-KBQA: Generation augmented iterative ranking for knowledge base question answering[C]//Proc of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA: ACL, 2022: 6032−6043
[30]	Dettmers T, Pagnoni A, Holtzman A, et al. QLORA: Efficient finetuning of quantized LLMs[C]//Proc of the 37th Int Conf on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc, 2024: 10088−10115
[31]	何宇纳,叶晨,房玥晖,等. 中国膳食平衡指数的修订:DBI_22[J]. 营养学报,2024,46(3):209−214 doi: 10.3969/j.issn.0512-7955.2024.03.003 He Yuna, Ye Chen, Fang Yuehui, et al. Revised Chinese dietary balance index: DBI-22[J]. Acta Nutrimenta Sinica, 2024, 46(3): 209−214 (in Chinese) doi: 10.3969/j.issn.0512-7955.2024.03.003
[32]	Es S, James J, Anke L, et al. RAGAs: Automated evaluation of retrieval augmented generation[C]//Proc of the 18th Conf of the European Chapter of the Association for Computational Linguistics: System Demonstrations. Stroudsburg, PA: ACL, 2024: 150−158
[33]	Wang Jingting, Wu Tianxing, Chen Shilin, et al. unKR: A python library for uncertain knowledge graph reasoning by representation learning[C]//Proc of the 47th Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 2024: 2822−2826
[34]	Bordes A, Usunier N, Garcia-Durán A, et al. Translating embeddings for modeling multi-relational data[C]//Proc of the 26th Int Conf on Neural Information Processing Systems (Volume 2). Red Hook, NY: Curran Associates Inc, 2013: 2787−2795
[35]	Trouillon T, Welbl J, Riedel S, et al. Complex embeddings for simple link prediction[C]//Proc of the 33rd Int Conf on Machine Learning. New York: JMLR. org, 2016: 2071−2080
[36]	Sun Zhiqing, Deng Zhihong, Nie Jianyun, et al. RotatE: Knowledge graph embedding by relational rotation in complex space[C/OL]//Proc of the 7th Int Conf on Learning Representations. New Orleans, LA: OpenReview. net, 2019[2024-07-10]. https://openreview.net/forum?id= HkgEQnRqYQ

[1]	Wu Huanhuan, Xie Ruilin, Qiao Yuanxin, Chen Xiang, Cui Zhanqi. Optimizing Deep Neural Network Based on Interpretability Analysis[J]. Journal of Computer Research and Development, 2024, 61(1): 209-220. DOI: 10.7544/issn1000-1239.202220803
[2]	Li Qian, Lin Chenhao, Yang Yulong, Shen Chao, Fang Liming. Adversarial Attacks and Defenses Against Deep Learning Under the Cloud-Edge-Terminal Scenes[J]. Journal of Computer Research and Development, 2022, 59(10): 2109-2129. DOI: 10.7544/issn1000-1239.20220665
[3]	Shen Zhengchen, Zhang Qianli, Zhang Chaofan, Tang Xiangyu, Wang Jilong. Location Privacy Attack Based on Deep Learning[J]. Journal of Computer Research and Development, 2022, 59(2): 390-402. DOI: 10.7544/issn1000-1239.20200843
[4]	Gu Mianxue, Sun Hongyu, Han Dan, Yang Su, Cao Wanying, Guo Zhen, Cao Chunjie, Wang Wenjie, Zhang Yuqing. Software Security Vulnerability Mining Based on Deep Learning[J]. Journal of Computer Research and Development, 2021, 58(10): 2140-2162. DOI: 10.7544/issn1000-1239.2021.20210620
[5]	Wang Huijiao, Cong Peng, Jiang Hua, Wei Yongzhuang. Security Analysis of SIMON32/64 Based on Deep Learning[J]. Journal of Computer Research and Development, 2021, 58(5): 1056-1064. DOI: 10.7544/issn1000-1239.2021.20200900
[6]	Zhou Chunyi, Chen Dawei, Wang Shang, Fu Anmin, Gao Yansong. Research and Challenge of Distributed Deep Learning Privacy and Security Attack[J]. Journal of Computer Research and Development, 2021, 58(5): 927-943. DOI: 10.7544/issn1000-1239.2021.20200966
[7]	Chen Jinyin, Chen Yipeng, Chen Yiming, Zheng Haibin, Ji Shouling, Shi Jie, Cheng Yao. Fairness Research on Deep Learning[J]. Journal of Computer Research and Development, 2021, 58(2): 264-280. DOI: 10.7544/issn1000-1239.2021.20200758
[8]	Wang Ruiqin, Wu Zongda, Jiang Yunliang, Lou Jungang. An Integrated Recommendation Model Based on Two-stage Deep Learning[J]. Journal of Computer Research and Development, 2019, 56(8): 1661-1669. DOI: 10.7544/issn1000-1239.2019.20190178
[9]	Zhou Yucong, Liu Yi, Wang Rui. Training Deep Neural Networks for Image Applications with Noisy Labels by Complementary Learning[J]. Journal of Computer Research and Development, 2017, 54(12): 2649-2659. DOI: 10.7544/issn1000-1239.2017.20170637
[10]	Zhang Lei, Zhang Yi. Big Data Analysis by Infinite Deep Neural Networks[J]. Journal of Computer Research and Development, 2016, 53(1): 68-79. DOI: 10.7544/issn1000-1239.2016.20150663