Recently there has been a paradigm shift from evidence
based medicine to personalized medicine. Earlier optimal therapy selection
based on populations e.g. If a patient belonged to a homogenous category such
as T2 stage, node negative, non-metastatic, non-small cell lung cancer, the
best treatment was selected on clinical trials for the various medications on
the same population. Historically, treatment is identical for all members of
this patient cohort. While this approach was developed to utilize the
statistical power of significantly large sample of a relatively homogeneous
group of patients, it ignores the heterogeneity of the individuals within the
cohort. This is slowly being replaced by personalized predictive models utilize
all available information from each patient (exams, demographics, imaging, lab,
genomic etc.) to identify optimal therapy in an individualized manner. This
approach improves outcomes because it exploits more detailed patient
information to reduce uncertainty in predicting patient outcomes as a function
This finds applications in preventive care, diagnosis,
therapy selection and monitoring. For example, a) predicting patients at risk
of developing hypertension and preventing manifestation ahead of time with
appropriate intervention (medications, diet, lifestyle changes etc.); b)
improving the early detection of cancer in asymptomatic patient; c) selecting
the optimal chemotherapy/radiation dosage or other therapy parameters based on
patient characteristics. Chemotherapy is expensive with terrible side effects
and often only works for less than 50% of the patients treated with it.
Identifying the right subset of patients that can benefit from it reduces the
costs and improves efficacy of the treatment. d) predicting patient response to
a given medication or/and treatment: Often the outcomes of therapy manifest too
late e.g. outcomes of chemo-radiation therapy in patients with non-small cell
lung cancer may take many months to manifest. By monitoring surrogate markers,
one may be able to predict poor outcomes early on and modify the therapy plan.
Also by predicting patient response and adequate dosage for a given medication
, undesirable possible drugs adverse side effects can be avoided. A good example
of this is the recent work from the International Warfarin Pharmacogenetics
Consortium (see references) on estimation of the Warfarin Dose with Clinical
and Pharmacogenetic Data.
The purpose of this cross-discipline workshop is to bring together machine learning and healthcare researchers interested in problems and applications of predictive models in the field of personalized medicine. The goal of the workshop will be to bridge the gap between the theory of predictive models and the applications and needs of the healthcare community. There will be exchange of ideas, identification of important and challenging applications and discovery of possible synergies. Ideally this will spur discussion and collaboration between the two disciplines and result in collaborative grant submissions. The emphasis will be on the mathematical and engineering aspects of predictive models and how it relates to practical medical problems.
Although, predictive modeling for healthcare has been explored by biostatisticians for several decades, this workshop focuses on substantially different needs and problems that are better addressed by modern machine learning technologies. For example, how should we organize clinical trials to validate the clinical utility of predictive models for personalized therapy selection? This workshop does not focus on issues of basic science; rather, we focus on predictive models that combine all available patient data (including imaging, pathology, lab, genomics etc.) to impact point of care decision making.
NIPS style. The accepted papers will be available for downloading from the workshop website's Proceedings page. Accepted papers will be either presented as a talk or poster (with poster spotlight). Papers should be emailed to the organizers at email@example.com. Please indicate your preference for oral or poster presentation.
online proceedings that will be made available prior to the workshop. Extended versions of some accepted papers will also be invited for inclusion in an edited book on the same topic as the workshop.
October 22 - Deadline of submission *New*
November 2 - Notification of Acceptance *New*
November 26 - Camera Ready Submission
December 11 - Workshop Proper