Combining Artificial Intelligence with Traditional Chinese Medicine for Intelligent Health Management

The growth of artificial intelligence (AI) is being referred to as the beginning of "the fourth industrial revolution". With the rapid development of hardware, algorithms, and applications, AI not only provides a new concept and relevant solutions to solve the problem of complexity science but also provides a new concept and method to promote the development of traditional Chinese medicine (TCM). In this study, based on the research and development of AI technology applications in biomedical and clinical diagnosis and treatment, we introduce AI technologies in current TCM research. This can have applications in intelligent clinical information acquisition, intelligent clinical decision, and efficacy evaluation of TCM; intelligent classification management, intelligent prescription, and drug research in Chinese herbal medicine; and health management. Furthermore, we propose a framework of "intelligent TCM" and outline its development prospects

Smart Healthcare solutions in China and Europe, an international business perspective

The thesis is part of the Marie Curie Fellowship project addressing health related challenges with IoT solutions. The author tries to address the challenge for the implementation of telehealth solutions by finding out the demand of the telehealth solution in selected European economies and in China (chapter 1), analyzing the emerging business models for telehealth solution ecosystems in China (chapter 2), how to integrate telehealth solutions with institutional stakeholders (chapter 3) and why are elderly users willing to use telehealth solutions in China. Chapter 1 and chapter 2 form the theoretical background for empirical work in chapter 3 and chapter 4. The thesis addressed four research questions, namely “Which societal and social-economics unmet needs that Internet of Healthcare Things can help to resolve?”, “What are the business model innovation for tech companies in China for the smart health industry?”, “What are the facilitators and hurdles for implementing telehealth solutions”, “Are elderly users willing to use telehealth solutions in China?”. Both qualitative study and quantitative analysis has been made based on data collected by in depth interviews with stakeholders, focus group study work with urban and rural residents in China. The digital platform framework was used in chapter 2 as the theoretical framework where as the stakeholder power mapping framework was used in chapter 3. The discretion choice experiment was used in chapter 4 to design questionnaire study while ordered logit regression was used to analyze the data. Telehealth solutions have great potential to fill in the gap for lack of community healthcare and ensuring health continuity between home care setting, community healthcare and hospitals. There is strong demand for such solutions if they can prove the medical value in managing chronic disease by raising health awareness and lowering health risks by changing the patients’ lifestyle. Analyzing how to realize the value for preventive healthcare by proving the health-economic value of digital health solutions (telehealth solutions) is the focus of research. There remain hurdles to build trust for telehealth solutions and the use of AI in healthcare. Next step of research can also be extended to addressing such challenges by analyzing how to improve the transparency of algorithms by disclosing the data source, and how the algorithms were built. Further research can be done on data interoperability between the EHR systems and telehealth solutions. The medical value of telehealth solutions can improve if doctors could interpret data collected from telehealth solutions; furthermore, if doctors could make diagnosis and provide treatment, adjust healthcare management plans based on such data, telehealth solutions then can be included in insurance packages, making them more accessible

Multi-omics and machine learning for the prevention and management of female reproductive health

Females typically carry most of the burden of reproduction in mammals. In humans, this burden is exacerbated further, as the evolutionary advantage of a large and complex human brain came at a great cost of women’s reproductive health. Pregnancy thus became a highly demanding phase in a woman’s life cycle both physically and emotionally and therefore needs monitoring to assure an optimal outcome. Moreover, an increasing societal trend towards reproductive complications partly due to the increasing maternal age and global obesity pandemic demands closer monitoring of female reproductive health. This review first provides an overview of female reproductive biology and further explores utilization of large-scale data analysis and -omics techniques (genomics, transcriptomics, proteomics, and metabolomics) towards diagnosis, prognosis, and management of female reproductive disorders. In addition, we explore machine learning approaches for predictive models towards prevention and management. Furthermore, mobile apps and wearable devices provide a promise of continuous monitoring of health. These complementary technologies can be combined towards monitoring female (fertility-related) health and detection of any early complications to provide intervention solutions. In summary, technological advances (e.g., omics and wearables) have shown a promise towards diagnosis, prognosis, and management of female reproductive disorders. Systematic integration of these technologies is needed urgently in female reproductive healthcare to be further implemented in the national healthcare systems for societal benefit.publishedVersio

Exploring the Potential of Large Language models in Traditional Korean Medicine: A Foundation Model Approach to Culturally-Adapted Healthcare

Introduction: Traditional Korean medicine (TKM) emphasizes individualized diagnosis and treatment, making AI modeling difficult due to limited data and implicit processes. GPT-3.5 and GPT-4, large language models, have shown impressive medical knowledge despite lacking medicine-specific training. This study aimed to assess the capabilities of GPT-3.5 and GPT-4 for TKM using the Korean National Licensing Examination for Korean Medicine Doctors. Methods: GPT-3.5 (February 2023) and GPT-4 (March 2023) models answered 340 questions from the 2022 examination across 12 subjects. Each question was independently evaluated five times in an initialized session. Results: GPT-3.5 and GPT-4 achieved 42.06% and 57.29% accuracy, respectively, with GPT-4 nearing passing performance. There were significant differences in accuracy by subjects, with 83.75% accuracy for neuropsychiatry compared to 28.75% for internal medicine (2). Both models showed high accuracy in recall-based and diagnosis-based questions but struggled with intervention-based ones. The accuracy for questions that require TKM-specialized knowledge was relatively lower than the accuracy for questions that do not GPT-4 showed high accuracy for table-based questions, and both models demonstrated consistent responses. A positive correlation between consistency and accuracy was observed. Conclusion: Models in this study showed near-passing performance in decision-making for TKM without domain-specific training. However, limits were also observed that were believed to be caused by culturally-biased learning. Our study suggests that foundation models have potential in culturally-adapted medicine, specifically TKM, for clinical assistance, medical education, and medical research.Comment: 31 pages, 6 figure

HICH Image/Text (HICH-IT): Comprehensive Text and Image Datasets for Hypertensive Intracerebral Hemorrhage Research

In this paper, we introduce a new dataset in the medical field of hypertensive intracerebral hemorrhage (HICH), called HICH-IT, which includes both electronic medical records (EMRs) and head CT images. This dataset is designed to enhance the accuracy of artificial intelligence in the diagnosis and treatment of HICH. This dataset, built upon the foundation of standard text and image data, incorporates specific annotations within the EMRs, extracting key content from the text information, and categorizes the annotation content of imaging data into four types: brain midline, hematoma, left and right cerebral ventricle. HICH-IT aims to be a foundational dataset for feature learning in image segmentation tasks and named entity recognition. To further understand the dataset, we have trained deep learning algorithms to observe the performance. The pretrained models have been released at both www.daip.club and github.com/Deep-AI-Application-DAIP. The dataset has been uploaded to https://github.com/CYBUS123456/HICH-IT-Datasets. Index Terms-HICH, Deep learning, Intraparenchymal hemorrhage, named entity recognition, novel datase

Personalized hypertension treatment recommendations by a data-driven model

BACKGROUND: Hypertension is a prevalent cardiovascular disease with severe longer-term implications. Conventional management based on clinical guidelines does not facilitate personalized treatment that accounts for a richer set of patient characteristics. METHODS: Records from 1/1/2012 to 1/1/2020 at the Boston Medical Center were used, selecting patients with either a hypertension diagnosis or meeting diagnostic criteria (≥ 130 mmHg systolic or ≥ 90 mmHg diastolic, n = 42,752). Models were developed to recommend a class of antihypertensive medications for each patient based on their characteristics. Regression immunized against outliers was combined with a nearest neighbor approach to associate with each patient an affinity group of other patients. This group was then used to make predictions of future Systolic Blood Pressure (SBP) under each prescription type. For each patient, we leveraged these predictions to select the class of medication that minimized their future predicted SBP. RESULTS: The proposed model, built with a distributionally robust learning procedure, leads to a reduction of 14.28 mmHg in SBP, on average. This reduction is 70.30% larger than the reduction achieved by the standard-of-care and 7.08% better than the corresponding reduction achieved by the 2nd best model which uses ordinary least squares regression. All derived models outperform following the previous prescription or the current ground truth prescription in the record. We randomly sampled and manually reviewed 350 patient records; 87.71% of these model-generated prescription recommendations passed a sanity check by clinicians. CONCLUSION: Our data-driven approach for personalized hypertension treatment yielded significant improvement compared to the standard-of-care. The model implied potential benefits of computationally deprescribing and can support situations with clinical equipoise.GM135930 - National Institute of General Medical Sciences; UL54 TR004130 - National Center for Advancing Translational Sciences; IIS-1914792 - National Science Foundation; DMS-1664644 - National Science Foundation; CCF-2200052 - National Science FoundationPublished versio

Advances in automated tongue diagnosis techniques

This paper reviews the recent advances in a significant constituent of traditional oriental medicinal technology, called tongue diagnosis. Tongue diagnosis can be an effective, noninvasive method to perform an auxiliary diagnosis any time anywhere, which can support the global need in the primary healthcare system. This work explores the literature to evaluate the works done on the various aspects of computerized tongue diagnosis, namely preprocessing, tongue detection, segmentation, feature extraction, tongue analysis, especially in traditional Chinese medicine (TCM). In spite of huge volume of work done on automatic tongue diagnosis (ATD), there is a lack of adequate survey, especially to combine it with the current diagnosis trends. This paper studies the merits, capabilities, and associated research gaps in current works on ATD systems. After exploring the algorithms used in tongue diagnosis, the current trend and global requirements in health domain motivates us to propose a conceptual framework for the automated tongue diagnostic system on mobile enabled platform. This framework will be able to connect tongue diagnosis with the future point-of-care health system

Soft Transducer for Patient’s Vitals Telemonitoring with Deep Learning-Based Personalized Anomaly Detection

This work addresses the design, development and implementation of a 4.0-based wearable soft transducer for patient-centered vitals telemonitoring. In particular, first, the soft transducer measures hypertension-related vitals (heart rate, oxygen saturation and systolic/diastolic pressure) and sends the data to a remote database (which can be easily consulted both by the patient and the physician). In addition to this, a dedicated deep learning algorithm, based on a Long-Short-Term-Memory Autoencoder, was designed, implemented and tested for providing an alert when the patient’s vitals exceed certain thresholds, which are automatically personalized for the specific patient. Furthermore, a mobile application (EcO2u) was developed to manage the entire data flow and facilitate the data fruition; this application also implements an innovative face-detection algorithm that ensures the identity of the patient. The robustness of the proposed soft transducer was validated experimentally on five individuals, who used the system for 30 days. The experimental results demonstrated an accuracy in anomaly detection greater than 93%, with a true positive rate of more than 94