Jaydeep Unni, Ph.D.
Sr. Project Manager, Robert Bosch Healthcare Systems, Palo Alto, CA
Remote Patient Monitoring – Challenges and Opportunities
Remote Patient Monitoring (RPM) is gaining importance and acceptance with rising number of chronic disease conditions and with increase in the aging population. As instances of Heart diseases, Diabetes etc are increasing the demand for these technologies are increasing. RPM devices typically collect patient vital sign data and in some case also patient responses to health related questions. Thus collected data is then transmitted through various modalities (wireless/Bluetooth/cellular) to Hospitals/Doctor’s office for clinical evaluation. With these solutions Doctors are able to access patient’s vital data ‘any time any where’ thus enabling them to intervene on a timely and effective manner. For older adult population chronic disease management, post-acute care management and safety monitoring are areas were RPM finds application. That said, there are significant challenges in adoption of Remote Patient Monitoring including patient willingness and compliance for adoption, affordability, availability of simpler/smarter technology to mention a few. But experts contend that if implemented correctly Remote Patient Monitoring can contain healthcare expenditure by reducing avoidable hospitalization while greatly improving quality of care.
Srisairam Achuthan, Ph.D.
Senior Scientific Programmer, Research Informatics Division, Department of Information Sciences, City of Hope, CA, USA
Applying Machine learning for Automated Identification of Patient Cohorts
Srisairam Achuthan, Mike Chang, Ajay Shah, Joyce Niland
Patient cohorts for a clinical study are typically identified based on specific selection criteria. In most cases considerable time and effort are spent in finding the most relevant criteria that could potentially lead to a successful study. For complex diseases, this process can be more difficult and error prone since relevant features may not be easily identifiable. Additionally, the information captured in clinical notes is in non-coded text format. Our goal is to discover patterns within the coded and non-coded fields and thereby reveal complex relationships between clinical characteristics across different patients that would be difficult to accomplish manually. Towards this, we have applied machine learning techniques such as artificial neural networks and decision trees to determine patients sharing similar characteristics from available medical records. For this proof of concept study, we used coded and non-coded (i.e., clinical notes) patient data from a clinical database. Coded clinical information such as diagnoses, labs, medications and demographics recorded within the database were pooled together with non-coded information from clinical notes including, smoking status, life style (active / inactive) status derived from clinical notes. The non-coded textual information was identified and interpreted using a Natural Language Processing (NLP) tool I2E from Linguamatics.
Rustom Mody, Ph.D.
Head R & D Lupin Ltd., Pune
Biosimilars are follow-on biologics also known as Similar Biologics – terms used to describe officially approved subsequent versions of innovator biopharmaceutical products made by rDNA technology when made by a different sponsor following patent expiry on the innovator product. These products are drawing global attention as a large number of block buster biopharmaceuticals have expired and many will soon seize to have patent protection in the coming years, opening the doors for the entry of biosimilars. However, the regulatory landscape is getting complex across the globe. The talk focuses on opportunities and challenges in the field of biosimilars and the future of biosimilar companies in India.