Press release: KenSci research paper on End-of-Life Prediction to be presented at the AAAI 2018 conference
The AAAI (Association for the Advancement of Artificial Intelligence) recognized the paper as EMERGING for its ideas on using machine learning to predict end of life
SEATTLE, WASH. — Feb. 6, 2018 — The Association for the Advancement of Artificial Intelligence (AAAI) has accepted a KenSci research paper as an emerging applications paper to be presented at the Innovative Applications of Artificial Intelligence (IAAI-18) track at the 2018 AAAI Annual Conference in New Orleans, Louisiana. The paper dives into how machine learning techniques are used to predict the risk of mortality for patients from two large hospital systems in the Pacific Northwest, along with an explanation for how end-of-life predictions, and insights derived from the predictions, can then be used to improve the quality of patient care toward the end of life.
A great deal of importance has been given to end-of-life care due to the recent identification of a large cohort in the aging population who may not necessarily be receiving medically appropriate care during the late stages of their lives. Additionally, high costs associated with end-of-life care have triggered an interest in the overall healthcare system owing to its impact on Medicare and Medicaid. According to publicly available data, in 2011, the U.S. health system spent $205 billion on the care of individuals in their last year of life. Emerging predictive analytics solutions allow physicians to move away from the low accuracy and predictability of a patient’s end of life and have the vital conversation by sharing prediction statistics to make informed decisions.
Ankur Teredesai, CTO and co-founder, KenSci, commented on the win. “Having been conceived within academia, KenSci’s philosophy is dedicated to delving into research that helps us use machine learning to improve healthcare across the care continuum. Due to Medicare policies, re-evaluating end-of-life care has increasingly gained importance over the last few years, and we believe predictive analytics can help improve the overall quality of care in the last six months of a patient’s life. We’re thrilled to have our research be chosen by the AAAI as emerging and excitedly look forward to presenting this at the IAAI-18.”
Michael Youngblood, IAAI Program Chair and researcher at Xerox Parc commented, “IAAI-18 has seen phenomenal growth in the quality and number of submissions over the past few years, doubling this year from 2017 alone. It is an exciting time to be working in applied AI, and we are seeing it have positive impacts on daily life across all facets of society. The paper, Death vs. Data Science: Predicting End of Life, by Muhammad Aurangzeb Ahmad, Carly Eckert, Greg McKelvey, Kiyana Zolfagar, Anam Zahid, and Ankur Teredesai was well-received in review by peer scientists and practitioners who selected it for acceptance as an emerging technology paper. It is scheduled to be presented in a session including other impactful work at 10 a.m. on Tuesday, February 6.”
The KenSci Platform ingests disparate sources of data into a single System of Record where ML models utilize claims and EHR data, including demographics, diagnosis and utilization history, labs and vital signs to provide insights only available by leveraging machine learning. KenSci utilizes multiple ML predictive models to determine the probability of a patient dying within 6 to 12 months from the date of prediction. The solution identifies at-risk patients through risk scores provided to physicians and care managers, enabling appropriate care prioritization and helping to optimize end-of-life care. The risk predictions are visualized through user-customized applications or via API’s inputted into the current end-user workflow. The
IAAI conference is a selective, peer-reviewed international gathering that highlights case studies of emerging technologies and existing deployed applications of artificial intelligence. This year is the thirtieth annual IAAI meeting and the thirty-second AAAI meeting. The events are co-located and held in New Orleans, Louisiana from February 2 to 7.
KenSci is the world’s first vertically integrated machine learning platform for healthcare, making it more proactive, coordinated, accountable and fast. KenSci’s platform is engineered to ingest, transform and integrate healthcare data across clinical, claims and patient generated sources. A library of over 180+ prebuilt models and modular solutions for clinical and operational risk prediction enable customers to ask and answer harder questions faster, with average deployment taking 12 weeks and ROI visibility in 90 days. KenSci is headquartered in Seattle, with offices in Singapore and Hyderabad. email@example.com | www.kensci.com
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