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Chung MC, Su LJ, Chen CL, Wu LC. AI-assisted literature exploration of innovative Chinese medicine formulas. Front Pharmacol 2024; 15:1347882. [PMID: 38584602 PMCID: PMC10995307 DOI: 10.3389/fphar.2024.1347882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 02/21/2024] [Indexed: 04/09/2024] Open
Abstract
Objective: Our study provides an innovative approach to exploring herbal formulas that contribute to the promotion of sustainability and biodiversity conservation. We employ data mining, integrating keyword extraction, association rules, and LSTM-based generative models to analyze classical Traditional Chinese Medicine (TCM) texts. We systematically decode classical Chinese medical literature, conduct statistical analyses, and link these historical texts with modern pharmacogenomic references to explore potential alternatives. Methods: We present a novel iterative keyword extraction approach for discerning diverse herbs in historical TCM texts from the Pu-Ji Fang copies. Utilizing association rules, we uncover previously unexplored herb pairs. To bridge classical TCM herbal pairs with modern genetic relationships, we conduct gene-herb searches in PubMed and statistically validate this genetic literature as supporting evidence. We have expanded on the present work by developing a generative language model for suggesting innovative TCM formulations based on textual herb combinations. Results: We collected associations with 7,664 PubMed cross-search entries for gene-herb and 934 for Shenqifuzheng Injection as a positive control. We analyzed 16,384 keyword combinations from Pu-Ji Fang's 426 volumes, employing statistical methods to probe gene-herb associations, focusing on examining differences among the target genes and Pu-Ji Fang herbs. Conclusion: Analyzing Pu-Ji Fang reveals a historical focus on flavor over medicinal aspects in TCM. We extend our work on developing a generative model from classical textual keywords to rapidly produces novel herbal compositions or TCM formulations. This integrated approach enhances our comprehension of TCM by merging ancient text analysis, modern genetic research, and generative modeling.
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Affiliation(s)
- Meng-Chi Chung
- Department of Biomedical Science and Engineering, National Central University (NCU), Jhong-Li City, Taiwan
| | - Li-Jen Su
- Department of Biomedical Science and Engineering, National Central University (NCU), Jhong-Li City, Taiwan
- Education and Research Center for Technology Assisted Substance Abuse Prevention and Management, National Central University (NCU), Taoyuan, Taiwan
- Core Facilities for High Throughput Experimental Analysis, Department of Biomedical Sciences and Engineering, National Central University (NCU), Taoyuan, Taiwan
- IIHMED Reproductive Center, Taipei, Taiwan
- Tian Medicine Phamaceutical Company Ltd., Taipei, Taiwan
| | - Chien-Lin Chen
- IIHMED Reproductive Center, Taipei, Taiwan
- School of Post-Baccalaureate Chinese Medicine, Tzu Chi University, Hualien, Taiwan
- Department of Health Promotion and Health Education, National Taiwan Normal University, Taipei, Taiwan
| | - Li-Ching Wu
- Department of Biomedical Science and Engineering, National Central University (NCU), Jhong-Li City, Taiwan
- Education and Research Center for Technology Assisted Substance Abuse Prevention and Management, National Central University (NCU), Taoyuan, Taiwan
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Chakraborty C, Pal S, Bhattacharya M, Dash S, Lee SS. Overview of Chatbots with special emphasis on artificial intelligence-enabled ChatGPT in medical science. Front Artif Intell 2023; 6:1237704. [PMID: 38028668 PMCID: PMC10644239 DOI: 10.3389/frai.2023.1237704] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 10/05/2023] [Indexed: 12/01/2023] Open
Abstract
The release of ChatGPT has initiated new thinking about AI-based Chatbot and its application and has drawn huge public attention worldwide. Researchers and doctors have started thinking about the promise and application of AI-related large language models in medicine during the past few months. Here, the comprehensive review highlighted the overview of Chatbot and ChatGPT and their current role in medicine. Firstly, the general idea of Chatbots, their evolution, architecture, and medical use are discussed. Secondly, ChatGPT is discussed with special emphasis of its application in medicine, architecture and training methods, medical diagnosis and treatment, research ethical issues, and a comparison of ChatGPT with other NLP models are illustrated. The article also discussed the limitations and prospects of ChatGPT. In the future, these large language models and ChatGPT will have immense promise in healthcare. However, more research is needed in this direction.
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Affiliation(s)
- Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal, India
| | - Soumen Pal
- School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | | | - Snehasish Dash
- School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Sang-Soo Lee
- Institute for Skeletal Aging and Orthopedic Surgery, Hallym University Chuncheon Sacred Heart Hospital, Chuncheon-si, Gangwon-do, Republic of Korea
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Cai J, Chen S, Guo S, Wang S, Li L, Liu X, Zheng K, Liu Y, Chen S. RegEMR: a natural language processing system to automatically identify premature ovarian decline from Chinese electronic medical records. BMC Med Inform Decis Mak 2023; 23:126. [PMID: 37464410 PMCID: PMC10353087 DOI: 10.1186/s12911-023-02239-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 07/13/2023] [Indexed: 07/20/2023] Open
Abstract
BACKGROUND The ovarian reserve is a reservoir for reproductive potential. In clinical practice, early detection and treatment of premature ovarian decline characterized by abnormal ovarian reserve tests is regarded as a critical measure to prevent infertility. However, the relevant data are typically stored in an unstructured format in a hospital's electronic medical record (EMR) system, and their retrieval requires tedious manual abstraction by domain experts. Computational tools are therefore needed to reduce the workload. METHODS We presented RegEMR, an artificial intelligence tool composed of a rule-based natural language processing (NLP) extractor and a knowledge-based disease scoring model, to automatize the screening procedure of premature ovarian decline using Chinese reproductive EMRs. We used regular expressions (REs) as a text mining method and explored whether REs automatically synthesized by the genetic programming-based online platform RegexGenerator + + could be as effective as manually formulated REs. We also investigated how the representativeness of the learning corpus affected the performance of machine-generated REs. Additionally, we translated the clinical diagnostic criteria into a programmable disease diagnostic model for disease scoring and risk stratification. Four hundred outpatient medical records were collected from a Chinese fertility center. Manual review served as the gold standard, and fivefold cross-validation was used for evaluation. RESULTS The overall F-score of manually built REs was 0.9444 (95% CI 0.9373 to 0.9515), with no significant difference (paired t test p > 0.05) compared with machine-generated REs that could be affected by training set sizes and annotation portions. The extractor performed effectively in automatically tracing the dynamic changes in hormone levels (F-score 0.9518-0.9884) and ultrasonographic measures (F-score 0.9472-0.9822). Applying the extracted information to the proposed diagnostic model, the program obtained an accuracy of 0.98 and a sensitivity of 0.93 in risk screening. For each specific disease, the automatic diagnosis in 76% of patients was consistent with that of the clinical diagnosis, and the kappa coefficient was 0.63. CONCLUSION A Chinese NLP system named RegEMR was developed to automatically identify high risk of early ovarian aging and diagnose related diseases from Chinese reproductive EMRs. We hope that this system can aid EMR-based data collection and clinical decision support in fertility centers.
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Affiliation(s)
- Jie Cai
- Center for Reproductive Medicine, Department of Gynecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Shenglin Chen
- Center for Reproductive Medicine, Department of Gynecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Siyun Guo
- Center for Reproductive Medicine, Department of Gynecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Suidong Wang
- Center for Reproductive Medicine, Department of Gynecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Lintong Li
- Center for Reproductive Medicine, Department of Gynecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Xiaotong Liu
- Center for Reproductive Medicine, Department of Gynecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Keming Zheng
- Center for Reproductive Medicine, Department of Gynecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Yudong Liu
- Center for Reproductive Medicine, Department of Gynecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Shiling Chen
- Center for Reproductive Medicine, Department of Gynecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
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Hasan MR, Alsaiari AA, Fakhurji BZ, Molla MHR, Asseri AH, Sumon MAA, Park MN, Ahammad F, Kim B. Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process. Molecules 2022; 27:4169. [PMID: 35807415 PMCID: PMC9268380 DOI: 10.3390/molecules27134169] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 06/22/2022] [Accepted: 06/27/2022] [Indexed: 01/18/2023] Open
Abstract
The conventional drug discovery approach is an expensive and time-consuming process, but its limitations have been overcome with the help of mathematical modeling and computational drug design approaches. Previously, finding a small molecular candidate as a drug against a disease was very costly and required a long time to screen a compound against a specific target. The development of novel targets and small molecular candidates against different diseases including emerging and reemerging diseases remains a major concern and necessitates the development of novel therapeutic targets as well as drug candidates as early as possible. In this regard, computational and mathematical modeling approaches for drug development are advantageous due to their fastest predictive ability and cost-effectiveness features. Computer-aided drug design (CADD) techniques utilize different computer programs as well as mathematics formulas to comprehend the interaction of a target and drugs. Traditional methods to determine small-molecule candidates as a drug have several limitations, but CADD utilizes novel methods that require little time and accurately predict a compound against a specific disease with minimal cost. Therefore, this review aims to provide a brief insight into the mathematical modeling and computational approaches for identifying a novel target and small molecular candidates for curing a specific disease. The comprehensive review mainly focuses on biological target prediction, structure-based and ligand-based drug design methods, molecular docking, virtual screening, pharmacophore modeling, quantitative structure-activity relationship (QSAR) models, molecular dynamics simulation, and MM-GBSA/MM-PBSA approaches along with valuable database resources and tools for identifying novel targets and therapeutics against a disease. This review will help researchers in a way that may open the road for the development of effective drugs and preventative measures against a disease in the future as early as possible.
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Affiliation(s)
- Md Rifat Hasan
- Department of Mathematics, Faculty of Science, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia;
- Department of Applied Mathematics, Faculty of Science, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
| | - Ahad Amer Alsaiari
- College of Applied Medical Science, Clinical Laboratories Science Department, Taif University, Taif 21944, Saudi Arabia;
| | - Burhan Zain Fakhurji
- iGene Medical Training and Molecular Research Center, Jeddah 21589, Saudi Arabia;
| | | | - Amer H. Asseri
- Biochemistry Department, Faculty of Science, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia;
- Centre for Artificial Intelligence in Precision Medicines, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia
| | - Md Afsar Ahmed Sumon
- Department of Marine Biology, Faculty of Marine Sciences, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia;
| | - Moon Nyeo Park
- College of Korean Medicine, Kyung Hee University, Hoigidong, Dongdaemungu, Seoul 02453, Korea;
| | - Foysal Ahammad
- Department of Biological Sciences, Faculty of Science, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia;
| | - Bonglee Kim
- College of Korean Medicine, Kyung Hee University, Hoigidong, Dongdaemungu, Seoul 02453, Korea;
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Schmidt L, Finnerty Mutlu AN, Elmore R, Olorisade BK, Thomas J, Higgins JPT. Data extraction methods for systematic review (semi)automation: Update of a living systematic review. F1000Res 2021; 10:401. [PMID: 34408850 PMCID: PMC8361807 DOI: 10.12688/f1000research.51117.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/27/2023] [Indexed: 10/12/2023] Open
Abstract
Background: The reliable and usable (semi)automation of data extraction can support the field of systematic review by reducing the workload required to gather information about the conduct and results of the included studies. This living systematic review examines published approaches for data extraction from reports of clinical studies. Methods: We systematically and continually search PubMed, ACL Anthology, arXiv, OpenAlex via EPPI-Reviewer, and the dblp computer science bibliography. Full text screening and data extraction are conducted within an open-source living systematic review application created for the purpose of this review. This living review update includes publications up to December 2022 and OpenAlex content up to March 2023. Results: 76 publications are included in this review. Of these, 64 (84%) of the publications addressed extraction of data from abstracts, while 19 (25%) used full texts. A total of 71 (93%) publications developed classifiers for randomised controlled trials. Over 30 entities were extracted, with PICOs (population, intervention, comparator, outcome) being the most frequently extracted. Data are available from 25 (33%), and code from 30 (39%) publications. Six (8%) implemented publicly available tools Conclusions: This living systematic review presents an overview of (semi)automated data-extraction literature of interest to different types of literature review. We identified a broad evidence base of publications describing data extraction for interventional reviews and a small number of publications extracting epidemiological or diagnostic accuracy data. Between review updates, trends for sharing data and code increased strongly: in the base-review, data and code were available for 13 and 19% respectively, these numbers increased to 78 and 87% within the 23 new publications. Compared with the base-review, we observed another research trend, away from straightforward data extraction and towards additionally extracting relations between entities or automatic text summarisation. With this living review we aim to review the literature continually.
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Affiliation(s)
- Lena Schmidt
- NIHR Innovation Observatory, Newcastle University, Newcastle upon Tyne, NE4 5TG, UK
- Sciome LLC, Research Triangle Park, North Carolina, 27713, USA
- Bristol Medical School, University of Bristol, Bristol, BS8 2PS, UK
| | | | - Rebecca Elmore
- Sciome LLC, Research Triangle Park, North Carolina, 27713, USA
| | - Babatunde K. Olorisade
- Bristol Medical School, University of Bristol, Bristol, BS8 2PS, UK
- Evaluate Ltd, London, SE1 2RE, UK
- Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, CF5 2YB, UK
| | - James Thomas
- UCL Social Research Institute, University College London, London, WC1H 0AL, UK
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