algorithms rely on information that
occurred in the past, then attempt
to create links between covariates
and outcomes, and reflect the
reasoning about the future at the level
of the individual patient. Had the
computational capabilities available to
us at present existed in the 15th century,
would it have been possible for Leonardo
da Vinci to develop a machine-learning
algorithm to predict the emergence
of the 21st century’s new infectious
diseases, such as severe acute respiratory
syndrome (SARS)? Probably not. Like
humans who rely on their intelligence,
imagination, and observations of the
past to make predictions [ 5], even the
most advanced algorithms [ 6] share a
similar deficiency. Efficient machine-learning algorithms of the future must
incorporate elements unrestricted to
computer science or statistics into their
functionality, and at this stage such
elements are not yet known to science.
Hats off to the man who had all
15 chronic conditions, and hats off
to his heroic struggle for survival.
His unbreakable soul was enhanced
by care from clinicians equipped with
not only extensive experience but also
state-of-the-art tools and algorithms.
For now, technology combined with
human care can merely delay the
deterioration of his condition, at
least temporarily. I believe, however,
that some chronic conditions will
disappear in coming years, and we—
scientists, engineers, and clinicians—
are here to make that happen.
1. Kartoun, U., Kumar, V., Cheng, S.C., Yu,
S., Liao, K., Karlson, E., Ananthakrishnan,
A., Xia, Z., Gainer, V., Cagan, A., Savova,
G., Chen, P., Murphy, S., Churchill, S.,
Kohane, I., Szolovits, P., Cai, T., and Shaw,
S. Y. Demonstrating the advantages of
applying data mining techniques on time-dependent electronic medical records.
Proc. of American Medical Informatics
Association 2015 Annual Symposium. Nov.
2015, San Francisco, CA.
2. Ward, B. W., Schiller, J.S., and Goodman,
R. A. Multiple chronic conditions among
U. S. adults: A 2012 update. Prev Chronic
Dis 11 (2014), E62.
3. Obermeyer, Z. and Emanuel, E.J.
Predicting the future - big data, machine
learning, and clinical medicine. N Engl J
Med 375, 13 (2016), 1216–9.
4. Boll, S., Heuten, W., and Meyer, J.
From tracking to personal health. ACM
Interactions 23, 1 (2016), 72–75.
5. Kartoun, U. A user, an interface, or none.
ACM Interactions 24, 1 (2017), 20–21.
6. Beam, A.L. and Kohane, I.S. Translating
artificial intelligence into clinical care.
JAMA 316, 22 (2016), 2368–2369.
Uri Kartoun is a research staff member at
IBM Research in Cambridge, MA. Previously
he was a research fellow at Harvard Medical
School/Massachusetts General Hospital. His
Ph.D. from Ben-Gurion University of the Negev,
Israel, focused on human-robot collaboration.
INTERACTIONS.ACM.ORG JULY–AUGUST 2017 INTERACTIONS 23
DOI: 10.1145/3096966 COP YRIGH T HELD BY AUTHOR