Healthcare systems around the world are struggling to keep up with patient needs, and improve quality of care while reducing costs at the same time. At the same time, more and more data is being captured around healthcare processes in the form of Electronic Health Records (EHR), health insurance claims, medical imaging databases, disease registries, spontaneous reporting sites, and clinical trials. As this data gets collected, government regulations are requiring healthcare providers to not only store it in an electronic format but also use it in meaningful ways. Using this data in an effective way to improve quality of care and reduce costs requires innovation in data mining as well as academic, industry and government partnerships. The goals of this workshop are to:
Bring together the KDD community and the medical researchers & practitioners to discuss and explore mutual benefits of applying KDD to the right medical challenges and to collaborate on identifying and developing promising new techniques and methodologies.
Bring together researchers (from both academia and industry) as well as practitioners from all three different groups in medicine and healthcare (payers, providers, and pharmaceuticals) to talk about their different perspectives and to share their latest problems and ideas.
Attract healthcare professionals who have access to interesting sources of data and problems but not the expertise in data mining to solve them effectively. This group would otherwise not attend KDD and we believe through our personal experiences that it is essential for KDD research community to interact with them.
This workshop serves as a bridge between the traditional KDD community and professionals in medicine and healthcare - two groups of participants that have a lot to learn from and share with each other. We aim to emphasize the following aspects:
Addressing the fundamental challenges in improving healthcare and how data mining technologies will help
Presenting recent advances in data mining algorithms and methods for healthcare transformation
Identifying the next step of healthcare solutions and the possible data driven solutions
Fostering interactions and collaborations among researchers and practitioners (from different backgrounds), and healthcare professionals, to promote cross-fertilization of ideas.
Exploring unified platforms and data for better evaluation of the techniques
Deployed healthcare applications of data mining
New classes of research problems motivated by real-world business problems
Data mining applications as components of healthcare business processes
How data mining is useful for various participants in the healthcare system
Providers (hospitals, labs, clinics)
Payers (Insurance companies)
Pharmaceuticals
Topic areas for the workshop include (but are not limited to) the following:
Statistical analysis and characterization of healthcare data
Meaningful use of healthcare data for improved patient care and cost-reduction
Data quality assessment and improvement: preprocessing, cleaning, missing data treatment etc.
Pattern detection and hypothesis generation from observational data
Comparative effectiveness research
Medical information retrieval
Cloud-computing models and scalability
Privacy and security issues in healthcare
Information visualization for healthcare data
Information fusion and knowledge transfer in healthcare
Evolutionary and longitudinal patient and disease models
Mining knowledge from medical imaging data
Medical fraud detection
Clinical decision support
Bio-surveillance
Intelligent payment models
Collaborative care delivery models
Post-market surveillance of medical interventions
Text mining - mining free text in electronic medical records
Help with ICD 9 to ICD 10 conversions
Improving Clinical trial management and design
Pay for performance models in healthcare
Feasibility of Health Information exchanges