10 Sep – 12 Sep 2025  | 
Exhibitor Conversations

Unlocking the Power of AI in Healthcare Management

DKABio Co Ltd

Unveiling their revolutionary smart health risk prediction system, DKABio shares the fruits of eight years of research, showcasing how AI techniques help users manage their health effectively. By accurately positioning an individual's health status and analysing future risk of chronic diseases, users can proactively set clear health management goals before diseases occur, leading to more informed decision-making and improved health outcomes. Here we talk to Ya Hui, Yang – General Manager / President at DKABio Co Ltd.

Tell us more about your participation in the Start-up Park.

Based on eight-year research, DKABio adopted AI techniques to help users manage their health by positioning individual's health status and analyzing the risk of getting chronic diseases in the future. Thus, users are able to set clear health management goal before diseases occur and review the effectiveness of health improving.

DKABio undertands that MEDICAL FAIR THAILAND, as an important medical event, has a great influence in Thailand and Southeast Asia market. Therefore, DKABIO decided to participate in the Start-up Park at MEDICAL FAIR THAILAND 2023 to increase our visibility and hope to help people with comprehensive health management.

What collaborations or partnerships are you seeking to establish in Southeast Asia?

We hope to gain specific opportunities and benefits, including:

  1. Showcasing our products: We aim to raise awareness of our smart health risk prediction system among potential partners and demonstrate its potential benefits to a diverse audience of industry professionals.
  2. Networking and collaborations: We seek to connect with industry professionals, investors, and potential partners in the Southeast Asian market. Through these connections, we hope to explore collaborative opportunities that can mutually benefit all parties involved.
  3. Market expansion: Participating in the event allows us to explore business opportunities in the Southeast Asian market. We aim to establish partnerships and alliances with local organizations to expand our presence and reach within the region.

In summary, DKABio's participation in the Start-up Park at MEDICAL FAIR THAILAND 2023 aims to gain collaborative opportunities, establish partnerships, and expand our market presence in Southeast Asia.

What specific target audience or customer segments do you hope to engage with?

Through MEDICAL FAIR THAILAND, we hope to find six related industries - medical, pharmacy, health management, health nurturing, fitness and insurance- to cooperate with us so that DKABio can become the core engine of AI in their services.

What can visitors expect to see from DKABio?

We plan to show the following products:
DKABio 1.0, the smart health risk prediction system, which can predict a person's risk on 15 diseases over the 10- year duration; and AQHIPROBE, which can be used to warning regional air pollution risk on personal health. In addition, we are engaging to develop DKABio 2.0, which predicts a person's risk on one single disease over the next 1-5 years, and DKABio HIoT, which uses IoT devices to predict a person's risk of hypertension, hyperlipidemia and hyperglycemia in real time.

Could you tell us more about your personal smart health risk prediction system and the role it can play in setting health management goals.

We apply clustering result to calculate a “disease-related” health score (HS) to summarize the health status of an individual into one number. Using HS, we establish a classification rule (called “disease map”) to discriminate diseased and non-diseased people. The classification rule also discriminates the non-disease people into healthy and sub-healthy people so that different health management can be taken for disease prevention.

How does DKABio's health score, which incorporates genetic, behavioural, and environmental data, serves as a valuable indicator for assessing overall health and identifying sub-healthy individuals?

DKABio's health scores are calculated using 148 health variables, with variables containing heredity, environment, and behavioral analysis. We show that the HS is a consistent score in the sense that the difference of health scores based on the training sample and validation sample, respectively, is very small (differences of mean absolute percentage errors within 0 to10 years were less than 0.1%, and all mean absolute percentage errors were between 1.2-1.6 %).

That health scores can be grouped into 4 groups: M1: people in healthy status, M2: people in sub-healthy status, M3: people having HS between 45 and 60, M4: people having HS below 45.

Higher HS indicates that the health status is better or disease risk is lower. HS decreases as age or number of diseases increase. For the disease-free subjects, we also determine cut-offs of HS (depending on age and gender), so that the subjects with HS below the cutoff have much greater chance of developing diseases than those above the cutoff. We define the people satisfying the former condition to be in sub-healthy condition, and the latter, in healthy condition.

How are AI techniques utilised to assist users in managing their health, including the process of positioning an individual's health status and analyzing future risk of chronic diseases? Additionally, how does this approach empower users to set proactive health management goals and assess the effectiveness of their health improvement efforts?

Health is invisible and intangible and therefore, all health scores in use have one thing in common: they want to capture and measure health and wellness and make it visible. Health score is very important in at least 3 directions of healthcare management. First, it is useful in interpreting data on the outcome of medical treatments or health management. If the illness or wellness of an individual can be quantified, a range of appropriate scores can be defined with different levels of health conditions. For example, our disease map does provide such function for not only the diseased people but also the non-diseased people. Second, a severity measure of illness such as HS and the corresponding risk predictions can help in identifying groups of patients, who have more severe illness, currently or potentially, and may need extra treatment or care. Third, it may be very useful for refining measures of healthcare resource in individual or institutional level.

Smart Health Risk Analysis will point out the "key impact factor" to provide people with the most significant direction to improve their health, and when the health management effect is achieved, the health score will show a positive response.

In your opinion, where do you see AI contributing to in the prediction of an individual's health trajectory in the coming years?

With the advancement of AI analysis technology and the accumulation of data, the prediction accuracy will be improved, and according to our research, the prediction time can be shortened from 10 years of disease risk prediction to 1-5 years of risk prediction, which can provide more accurate information about people's disease risk, and even more accurate prediction can be obtained with fewer variables in the future, so as to achieve the benefit of early detection and early prevention.