This presentation by the Statistics & Data Science Department explores the transformative potential of big data analytics in healthcare, leveraging the vast treasure of information accumulated in the Hospital Authority's IT systems. By applying advanced analytics techniques and tools, the department demonstrates how diverse datasets can be harnessed to generate actionable insights, facilitating strategic decision-making and addressing the growing demand for healthcare services.
The objectives of this work are threefold: (1) to optimize healthcare decision-making through data-driven methodologies; (2) to develop predictive models including the Diabetes Mellitus (DM) Risk Engine, the Uncommon Disorders Database (UDD), and the Chemotherapy Toxicity Risk Model - to enable personalized care and early interventions; and (3) to promote sustainable and efficient healthcare systems using innovative technologies.
The projects employ cutting-edge techniques spanning machine and deep learning methods, such as the DeepHit model for survival analysis, alongside traditional statistical approaches like the Cox proportional-hazards model. Natural language processing (NLP) and LightGBM models were also utilised for text analysis and patient identification within clinical notes, supporting the development of comprehensive databases and risk prediction tools.
Key advancements include:
The DM Risk Engine, which enables risk stratification for diabetic patients, fostering timely interventions and fostering personlised care.
The UDD, a centralized resource for better management of rare diseases, enhancing diagnosis and care delivery for these conditions.
The Chemotherapy Toxicity Risk Model, which supports clinicians in identifying high-risk lung cancer patients with potential adverse events, ensuring safer treatments and optimizing decisions to reduce complications.
These innovative applications highlight the pivotal role of big data analytics in driving personalized healthcare, improving patient outcomes, and addressing the sustainability challenges faced by modern healthcare systems. The work underscores the importance of integrating advanced analytics into healthcare strategies to deliver impactful, data-driven solutions.
