2016년 11월 11일 대한불안의학회에서 발표한 자료입니다.

1. Professor, SAHIST, Sungkyunkwan University
Director, Digital Healthcare Institute
Yoon Sup Choi, Ph.D.
Recent advances and challenges of digital mental healthcare

2. “It's in Apple's DNA that technology alone is not enough.
It's technology married with liberal arts.”

3. The Convergence of IT, BT and Medicine

6. What is most important factor in digital medicine?

7. “Data! Data! Data!” he cried.“I can’t
make bricks without clay!”
- Sherlock Holmes,“The Adventure of the Copper Beeches”

9. 새로운 데이터가
새로운 방식으로
새로운 주체에 의해
측정, 저장, 통합, 분석된다.
데이터의 종류
데이터의 질적/양적 측면
웨어러블 기기
스마트폰
유전 정보 분석
인공지능
SNS
사용자/환자
대중

10. Smartphone: the origin of healthcare innovation

11. 2013?
The election of Pope Benedict
The Election of Pope Francis

12. The Election of Pope Francis
The Election of Pope Benedict

13. SummerTanThese Days

16. the manifestations of disease by providing a
more comprehensive and nuanced view of the
experience of illness. Through the lens of the
digital phenotype, an individual’s interaction
The digital phenotype
Sachin H Jain, Brian W Powers, Jared B Hawkins & John S Brownstein
In the coming years, patient phenotypes captured to enhance health and wellness will extend to human interactions with
digital technology.
In 1982, the evolutionary biologist Richard
Dawkins introduced the concept of the
“extended phenotype”1, the idea that pheno-
types should not be limited just to biological
processes, such as protein biosynthesis or tissue
growth, but extended to include all effects that
a gene has on its environment inside or outside
ofthebodyoftheindividualorganism.Dawkins
stressed that many delineations of phenotypes
are arbitrary. Animals and humans can modify
their environments, and these modifications
andassociatedbehaviorsareexpressionsofone’s
genome and, thus, part of their extended phe-
notype. In the animal kingdom, he cites damn
buildingbybeaversasanexampleofthebeaver’s
extended phenotype1.
Aspersonaltechnologybecomesincreasingly
embedded in human lives, we think there is an
important extension of Dawkins’s theory—the
notion of a ‘digital phenotype’. Can aspects of
ourinterfacewithtechnologybesomehowdiag-
nosticand/orprognosticforcertainconditions?
Can one’s clinical data be linked and analyzed
together with online activity and behavior data
to create a unified, nuanced view of human dis-
ease?Here,wedescribetheconceptofthedigital
phenotype. Although several disparate studies
have touched on this notion, the framework for
medicine has yet to be described. We attempt to
define digital phenotype and further describe
the opportunities and challenges in incorporat-
ing these data into healthcare.
Jan. 2013
0.000
0.002
0.004
Density
0.006
July 2013 Jan. 2014 July 2014
User 1
User 2
User 3
User 4
User 5
User 6
User 7
Date
Figure 1 Timeline of insomnia-related tweets from representative individuals. Density distributions
(probability density functions) are shown for seven individual users over a two-year period. Density on
the y axis highlights periods of relative activity for each user. A representative tweet from each user is
shown as an example.
npg©2015NatureAmerica,Inc.Allrightsreserved.
http://www.nature.com/nbt/journal/v33/n5/full/nbt.3223.html

19. • 아이폰의 센서로 측정한 자신의 의료/건강 데이터를 플랫폼에 공유 가능
• 가속도계, 마이크, 자이로스코프, GPS 센서 등을 이용
• 걸음, 운동량, 기억력, 목소리 떨림 등등
• 기존의 의학연구의 문제를 해결: 충분한 의료 데이터의 확보
• 연구 참여자 등록에 물리적, 시간적 장벽을 제거 (1번/3개월 ➞ 1번/1초)
• 대중의 의료 연구 참여 장려: 연구 참여자의 수 증가
• 발표 후 24시간 내에 수만명의 연구 참여자들이 지원
• 사용자 본인의 동의 하에 진행
Research Kit

20. •초기 버전으로, 5가지 질환에 대한 앱 5개를 소개
ResearchKit

21. ResearchKit

22. ResearchKit

23. http://www.roche.com/media/store/roche_stories/roche-stories-2015-08-10.htm

24. http://www.roche.com/media/store/roche_stories/roche-stories-2015-08-10.htm
pRED app to track Parkinson’s symptoms in drug trial

25. Autism and Beyond EpiWatchMole Mapper
measuring facial expressions of young
patients having autism
measuring morphological changes
of moles
measuring behavioral data
of epilepsy patients

26. Digital Phenotype:
Your smartphone knows if you are depressed
Ginger.io

27. Ginger.io
•문자를 얼마나 자주 하는지
•통화를 얼마나 오래하는지
•누구와 통화를 하는지
•얼마나 거리를 많이 이동했는지
•얼마나 많이 움직였는지
• UCSF, McLean Hospital: 정신질환 연구
• Novant Health: 당뇨병, 산후 우울증 연구
• UCSF, Duke: 수술 후 회복 모니터링

28. Digital Phenotype:
Your smartphone knows if you are depressed
J Med Internet Res. 2015 Jul 15;17(7):e175.
The correlation analysis between the features and the PHQ-9 scores revealed that 6 of the 10
features were significantly correlated to the scores:
• strong correlation: circadian movement, normalized entropy, location variance
• correlation: phone usage features, usage duration and usage frequency

29. Digital Phenotype:
Your smartphone knows if you are depressed
J Med Internet Res. 2015 Jul 15;17(7):e175.
Comparison of location and usage feature statistics between participants with no symptoms of depression (blue) and the
ones with (red). (ENT, entropy; ENTN, normalized entropy; LV, location variance; HS, home stay;TT, transition time;TD,
total distance; CM, circadian movement; NC, number of clusters; UF, usage frequency; UD, usage duration).
Figure 4. Comparison of location and usage feature statistics between participants with no symptoms of depression (blue) and the ones with (red).
Feature values are scaled between 0 and 1 for easier comparison. Boxes extend between 25th and 75th percentiles, and whiskers show the range.
Horizontal solid lines inside the boxes are medians. One, two, and three asterisks show significant differences at P<.05, P<.01, and P<.001 levels,
respectively (ENT, entropy; ENTN, normalized entropy; LV, location variance; HS, home stay; TT, transition time; TD, total distance; CM, circadian
movement; NC, number of clusters; UF, usage frequency; UD, usage duration).
Figure 5. Coefficients of correlation between location features. One, two, and three asterisks indicate significant correlation levels at P<.05, P<.01,
and P<.001, respectively (ENT, entropy; ENTN, normalized entropy; LV, location variance; HS, home stay; TT, transition time; TD, total distance;
CM, circadian movement; NC, number of clusters).
Saeb et alJOURNAL OF MEDICAL INTERNET RESEARCH
the variability of the time
the participant spent at
the location clusters
what extent the participants’
sequence of locations followed a
circadian rhythm.

30. Submitted 23 June 2016
Accepted 7 September 2016
Published 29 September 2016
Corresponding author
David C. Mohr,
d-mohr@northwestern.edu
Academic editor
Anthony Jorm
Additional Information and
Declarations can be found on
page 12
DOI 10.7717/peerj.2537
Copyright
2016 Saeb et al.
Distributed under
Creative Commons CC-BY 4.0
OPEN ACCESS
The relationship between mobile phone
location sensor data and depressive
symptom severity
Sohrab Saeb1,2
, Emily G. Lattie1
, Stephen M. Schueller1
,
Konrad P. Kording2
and David C. Mohr1
1
Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
2
Rehabilitation Institute of Chicago, Department of Physical Medicine and Rehabilitation,
Northwestern University, Chicago, IL, United States
ABSTRACT
Background. Smartphones offer the hope that depression can be detected using
passively collected data from the phone sensors. The aim of this study was to replicate
andextendpreviousworkusinggeographiclocation(GPS)sensorstoidentifydepressive
symptom severity.
Methods. We used a dataset collected from 48 college students over a 10-week period,
which included GPS phone sensor data and the Patient Health Questionnaire 9-item
(PHQ-9) to evaluate depressive symptom severity at baseline and end-of-study. GPS
featureswerecalculatedovertheentirestudy,forweekdaysandweekends,andin2-week
blocks.
Results. The results of this study replicated our previous findings that a number of
GPS features, including location variance, entropy, and circadian movement, were
significantly correlated with PHQ-9 scores (r’s ranging from 0.43 to 0.46, p-values
< .05). We also found that these relationships were stronger when GPS features were
calculatedfromweekend,comparedtoweekday,data.Althoughthecorrelationbetween
baseline PHQ-9 scores with 2-week GPS features diminished as we moved further from
baseline, correlations with the end-of-study scores remained significant regardless of the
time point used to calculate the features.
Discussion. Our findings were consistent with past research demonstrating that GPS
features may be an important and reliable predictor of depressive symptom severity.
The varying strength of these relationships on weekends and weekdays suggests the role
of weekend/weekday as a moderating variable. The finding that GPS features predict
depressive symptom severity up to 10 weeks prior to assessment suggests that GPS
features may have the potential as early warning signals of depression.
Subjects Bioinformatics, Psychiatry and Psychology, Public Health, Computational Science
Keywords Mobile phone, Depression, Depressive symptoms, Geographic locations, Students
INTRODUCTION
Depression is common and debilitating, taking an enormous toll in terms of cost,
morbidity, and mortality (Ferrari et al., 2013; Greenberg et al., 2015). The 12-month
prevalence of major depressive disorder among adults in the US is 6.9% (Kessler et al.,
2005), and an additional 2–5% have subsyndromal symptoms that warrant treatment
Saeb et al. (2016), PeerJ, DOI 10.7717/peerj.2537

31. The relationship between mobile phone location sensor
data and depressive symptom severity
Linear correlation coefficients (r) between individual 10-week features and PHQ-9 scores, and their 95% confidence
intervals. Features indicated with stars (∗) are replicated from our previous study (Saeb et al., 2015a.). Bold values indicate
significant correlations.
Table 2 Linear correlation coefficients (r) between individual 10-week features and PHQ-9 scores, and
their 95% confidence intervals. Features indicated with stars (⇤) are replicated from our previous study
(Saeb et al., 2015a.). Bold values indicate significant correlations.
Feature Baseline (n = 46) Follow-up (n = 38) Change (n = 38)
Location variance⇤
0.29 ± 0.008 0.43 ± 0.007 0.34 ± 0.008
Circadian movement⇤
0.34 ± 0.006 0.48 ± 0.006 0.33 ± 0.009
Speed mean 0.03 ± 0.007 0.06 ± 0.005 0.04 ± 0.008
Speed variance 0.07 ± 0.007 0.06 ± 0.005 0.06 ±0.007
Total distance⇤
0.23 ± 0.004 0.18 ± 0.006 0.03 ± 0.006
Number of clusters⇤
0.38 ± 0.005 0.44 ± 0.004 0.24 ± 0.007
Entropy⇤
0.31 ± 0.007 0.46 ± 0.005 0.28 ± 0.008
Normalized entropy⇤
0.26 ± 0.007 0.44 ± 0.005 0.30 ± 0.009
Raw entropy 0.17 ± 0.009 0.22 ± 0.008 0.15 ± 0.010
Home stay⇤
0.22 ± 0.008 0.43 ± 0.005 0.30 ± 0.009
Transition time⇤
0.30 ± 0.006 0.32 ± 0.005 0.12 ± 0.009
Data analysis
We evaluated the relationship between each set of features (10-week and 2-week, each for all
days, weekends, or weekdays) and depressive symptoms severity as measured by the PHQ-9.
We used linear correlation coefficient (r) and considered p < 0.05 as the significance level.
In order to reduce the possibility that results were generated by chance, we created 1,000
bootstrap subsamples (Efron & Tibshirani, 1993) to estimate these correlation coefficientsSaeb et al. (2016), PeerJ, DOI 10.7717/peerj.2537

32. Table 3 Linear correlation coefficients (r) between individual weekend and weekday features and PHQ-9 scores, and their 95% confidence in-
tervals. Bold values indicate significant correlations (see ‘Data Analysis’).
Feature Weekday Weekend
Baseline (n = 46) Follow-up (n = 38) Change (n = 38) Baseline (n = 46) Follow-up (n = 38) Change (n = 38)
Location variance 0.15 ± 0.008 0.20 ± 0.008 0.22 ± 0.009 0.31 ± 0.008 0.47 ±0.007 0.39 ± 0.008
Circadian movement 0.22 ± 0.007 0.28 ± 0.008 0.25 ± 0.009 0.35 ± 0.007 0.51 ±0.006 0.36 ± 0.008
Speed mean 0.00 ± 0.008 0.06 ± 0.005 0.03 ± 0.008 0.13 ± 0.005 0.06 ± 0.006 0.05 ± 0.009
Speed variance 0.05 ± 0.008 0.07 ± 0.005 0.02 ± 0.007 0.13 ± 0.004 0.05 ± 0.006 0.10 ± 0.008
Total distance 0.20 ± 0.004 0.15 ± 0.005 0.01 ± 0.006 0.25 ± 0.004 0.20 ± 0.005 0.03 ± 0.006
Number of clusters 0.19 ± 0.006 0.25 ± 0.005 0.14 ± 0.008 0.34 ± 0.006 0.46 ±0.004 0.32 ± 0.007
Entropy 0.21 ± 0.007 0.34 ± 0.006 0.20 ± 0.009 0.30 ± 0.008 0.55 ±0.004 0.38 ± 0.008
Normalized entropy 0.21 ± 0.008 0.39 ± 0.006 0.24 ± 0.009 0.28 ± 0.008 0.54 ± 0.004 0.41 ± 0.009
Raw entropy 0.05 ± 0.008 0.04 ± 0.008 0.01 ± 0.010 0.04 ± 0.008 0.01 ± 0.008 0.03 ± 0.009
Home stay 0.19 ± 0.008 0.37 ± 0.006 0.23 ± 0.009 0.23 ± 0.007 0.50 ± 0.004 0.35 ± 0.008
Transition time 0.27 ± 0.006 0.29 ± 0.006 0.14 ± 0.010 0.36 ± 0.006 0.32 ± 0.008 0.06 ± 0.009
only normalized entropy was significantly related to the scores as a weekday feature. The
magnitude of the relationship between weekend features and PHQ-9 scores was larger than
the magnitude of the relationship between 10-week features and PHQ-9 scores. However,
given the small sample size, we were not adequately powered to test if these differences were
significant.
2-week features
Finally, we examined how 2-week GPS features obtained at different times during the study
Linear correlation coefficients (r) between individual weekend and weekday features and PHQ-9 scores, and their 95%
confidence intervals. Bold values indicate significant correlations.All of those 10-week features that were significantly
related to PHQ-9 scores (seeTable 2) were also significant when calculated from weekends, whereas only normalized
entropy was significantly related to the scores as a weekday feature
Saeb et al. (2016), PeerJ, DOI 10.7717/peerj.2537
The relationship between mobile phone location sensor
data and depressive symptom severity

33. Saeb et al. (2016), PeerJ, DOI 10.7717/peerj.2537
The relationship between mobile phone location sensor
data and depressive symptom severity

34. Reece & Danforth, “Instagram photos reveal predictive markers of depression” (2016)
higher Hue (bluer)
lower Saturation (grayer)
lower Brightness (darker)

35. Digital Phenotype:
Your Instagram knows if you are depressed
Rao (MVR) (24) .
Results
Both All­data and Pre­diagnosis models were decisively superior to a null model
. All­data predictors were significant with 99% probability.57.5;(KAll = 1 K 49.8) Pre = 1 7
Pre­diagnosis and All­data confidence levels were largely identical, with two exceptions:
Pre­diagnosis Brightness decreased to 90% confidence, and Pre­diagnosis posting frequency
dropped to 30% confidence, suggesting a null predictive value in the latter case.
Increased hue, along with decreased brightness and saturation, predicted depression. This
means that photos posted by depressed individuals tended to be bluer, darker, and grayer (see
Fig. 2). The more comments Instagram posts received, the more likely they were posted by
depressed participants, but the opposite was true for likes received. In the All­data model, higher
posting frequency was also associated with depression. Depressed participants were more likely
to post photos with faces, but had a lower average face count per photograph than healthy
participants. Finally, depressed participants were less likely to apply Instagram filters to their
posted photos.
Fig. 2. Magnitude and direction of regression coefficients in All­data (N=24,713) and Pre­diagnosis (N=18,513)
models. X­axis values represent the adjustment in odds of an observation belonging to depressed individuals, per
Reece & Danforth, “Instagram photos reveal predictive markers of depression” (2016)
Fig. 1. Comparison of HSV values. Right photograph has higher Hue (bluer), lower Saturation (grayer), and lower
Brightness (darker) than left photograph. Instagram photos posted by depressed individuals had HSV values
shifted towards those in the right photograph, compared with photos posted by healthy individuals.
Units of observation
In determining the best time span for this analysis, we encountered a difficult question:
When and for how long does depression occur? A diagnosis of depression does not indicate the
persistence of a depressive state for every moment of every day, and to conduct analysis using an
individual’s entire posting history as a single unit of observation is therefore rather specious. At
the other extreme, to take each individual photograph as units of observation runs the risk of
being too granular. DeChoudhury et al. (5) looked at all of a given user’s posts in a single day,
and aggregated those data into per­person, per­day units of observation. We adopted this
precedent of “user­days” as a unit of analysis . 5
Statistical framework
We used Bayesian logistic regression with uninformative priors to determine the strength
of individual predictors. Two separate models were trained. The All­data model used all
collected data to address Hypothesis 1. The Pre­diagnosis model used all data collected from
higher Hue (bluer)
lower Saturation (grayer)
lower Brightness (darker)

36. Digital Phenotype:
Your Instagram knows if you are depressed
Reece & Danforth, “Instagram photos reveal predictive markers of depression” (2016)
. In particular, depressedχ2 07.84, p .17e 64;( All = 9 = 9 − 1 13.80, p .87e 44)χ2Pre = 8 = 2 − 1
participants were less likely than healthy participants to use any filters at all. When depressed
participants did employ filters, they most disproportionately favored the “Inkwell” filter, which
converts color photographs to black­and­white images. Conversely, healthy participants most
disproportionately favored the Valencia filter, which lightens the tint of photos. Examples of
filtered photographs are provided in SI Appendix VIII.
Fig. 3. Instagram filter usage among depressed and healthy participants. Bars indicate difference between observed
and expected usage frequencies, based on a Chi­squared analysis of independence. Blue bars indicate
disproportionate use of a filter by depressed compared to healthy participants, orange bars indicate the reverse.

37. Digital Phenotype:
Your Instagram knows if you are depressed
Reece & Danforth, “Instagram photos reveal predictive markers of depression” (2016)
VIII. Instagram filter examples
Fig. S8. Examples of Inkwell and Valencia Instagram filters. Inkwell converts
color photos to black­and­white, Valencia lightens tint. Depressed participants
most favored Inkwell compared to healthy participants, Healthy participants

38. Virtual Reality

39. PTSD

40. Prolonged Exposure Therapy
(지속 노출 치료)

41. 지속 노출 치료의 한계
• 환자들이 트라우마를 떠올리는 것에 거부감을 느끼거나, 효과적으로 상상하지 못함
• 사실 그 자체가 PTSD 의 증상의 하나
• 환자가 트라우마에 대한 기억을 생생하게 시각화하지 못하면 치료 효과 감소
어떻게 환자에게 실감나는 상황을 시각화 해줄 것인가

42. VirtualVietnam(1997)

43. VirtualVietnam
• VR은 PTSD의 치료를 위해 1990년대부터 활용
• 최초의 시도: 버추얼 베트남 (1997)
• 정글을 헤치고 나가는 시나리오 / 군용 헬리곱터가 날아가는 시나리오
• 그래픽 수준, 구현 효과 및 시나리오 등이 제한적
• 전통적 심리 치료에 효과 없던 환자 전원이 유의미한 개선 효과
“영상 속에서 베트남 사람들과 탱크를 보았어요”

44. VR: Virtual Iraq/Afganistan

45. Full Spectrum Warrior

46. Full Spectrum Warrior

47. Virtual Iraq 의 다양한 시나리오
•시가지: 황량한 거리에 낡은 건물과 금방 무너질 것만 같은
아파트, 창고, 모스크, 공장 등이 있는 상황. 인적이나 교통
량이 거의 없는 버전과, 사람과 교통량이 많은 두 가지 버전
•시가지 빌딩 내부: 시가지의 일부 빌딩은 환자가 내부로 들
어가볼 수 있도록 내부 구조가 모델링. 빌딩은 비어있게 할
수도 있고, 적거나 많은 거주자가 내부에 있도록 설정 가능
•검문소: 시가지 시나리오의 일부로, 차량이 도시로 진입하
기 위해 정지하는 검문소 상황.
•작은 시골 마을: 쓰러져가는 건물과 전투의 잔해들이 있는
작은 마을을 재현. 주변에 식물들이 많고, 건물들 사이로 멀
리 사막이 보임
•사막 기지: 군인들, 텐트, 군용 장비 등이 설치 되어 있는 사
막의 기지를 재현.
•사막 도로: 비포장 도로의 환경. 각각 도시, 작은 시골 마을,
사막 기지 시나리오로 이어짐. 사막의 사구, 식물들, 낡은
건물들, 전투 잔해, 길가의 사람 등으로 구성.
Fig. 1. Outskirts of Virtual Iraq City
Fig. 2. Center Area of Virtual Iraq City
Fig. 3. Car Bombing in Virtual Iraq City
User-Centered tests with the application were conducte
the Naval Medical CenteroSan Diego and within an Army
Combat Stress Control Team in Iraq (See Figure 8). This
d at
usability of the prototype system application that fed an
iterative design process. A clinical trial version of the
application built from this process is currently being tested
with PTSD-diagnosed personnel at a variety of sites. The
Fig. 4. Interior view from of Desert Road Humvee Scenario
Fig. 5. Turret view from of Desert Road Humvee Scenario
Fig. 6. IED Attack in Desert Road Humvee Scenario

49. 오즈의 마법사:
시각-촉각-청각-후각을 통한 전쟁의 재현
• 상담사는 환자가 처해있는 모든 상황을 실시간으로 컨트롤 (‘오즈의 마법사’)
• 환자가 실제 트라우마를 가진 상황을 최대한 비슷하게 재현
• 시각적, 청각적, 후각적, 촉각적 상황을 컨트롤
• 다양한 군용 차량 / 근처에 있는 건물, 차, 탱크 등을 폭파
• 비행기나 헬리콥터를 머리 위에 출현, 낮/밤, 비/안개
• 다양한 상황을 재현 가능
• 총격전이 벌어지거나, 매복에 당한 상황, 로켓포가 날아오는 상황
• 동료가 죽거나 부상을 입은 상황, 사람의 시체나 잔해를 본 상황
• 적군이나 민간인에게 총격을 가한 상황 등등

50. scores at baseline, post treatment and 3-month follow-up are in Fig
group, mean Beck Anxiety Inventory scores significantly decrea
(9.5) to 11.9 (13.6), (t=3.37, df=19, p < .003) and mean PHQ-9
decreased 49% from 13.3 (5.4) to 7.1 (6.7), (t=3.68, df=19, p < 0.00
Figure 4. PTSD Checklist scores across treatment Figure 5. BAI and PH
The average number of sessions for this sample was just under
successful treatment completers had documented mild and mode
injuries, which suggest that this form of exposure can be useful
PTSD Checklist scores across treatment
• 연구 결과 20명의 환자들은 전반적으로 유의미한 개선을 보임
• 환자들 전체의 PCL-M 수치가 평균 54.4에서 35.6으로 감소
• 20명 중 16명은 치료 직후에 더 이상 PTSD 를 가지지 않은 것으로 나타남
• 치료가 끝난지 3개월 후에 환자들의 상태는 유지
http://www.ncbi.nlm.nih.gov/pubmed/19377167

51. reatment and 3-month follow-up are in Figure 4. For this same
iety Inventory scores significantly decreased 33% from 18.6
=3.37, df=19, p < .003) and mean PHQ-9 (depression) scores
3 (5.4) to 7.1 (6.7), (t=3.68, df=19, p < 0.002) (see Figure 5).
ores across treatment Figure 5. BAI and PHQ-Depression scores
r of sessions for this sample was just under 11. Also, two of the
mpleters had documented mild and moderate traumatic brain
that this form of exposure can be usefully applied with this
BAI and PHQ-Depression scores
• 벡 불안 지수는 평균 18.6에서 11.9로 33% 감소
• PHQ-9 우울증 지수 역시 13.3에서 7.1로 49% 감소
• 경미한 외상성 뇌손상 (traumatic brain injury) 환자 2명에도 유의미한 효과
http://www.ncbi.nlm.nih.gov/pubmed/19377167

53. No choice but to bring AI into the medicine

54. Digital Oncologist

55. Digital Radiologist

56. Digital Pathologist

57. Digital Psychiatrist?

58. BeyondVerbal: Reading emotions from voices

59. http://www.wsj.com/articles/SB10001424052702303824204579421242295627138

60. BeyondVerbal
• 기계가 사람의 감정을 이해한다면?
• 헬스케어 분야에서도 응용도 높음: 슬픔/우울함/피로 등의 감정 파악
• 보험 회사에서는 가입자의 우울증 여부 파악을 위해 이미 사용 중
• Aetna 는 2012년 부터 고객의 우울증 여부를 전화 목소리 분석으로 파악
• 기존의 방식에 비해 우울증 환자 6배 파악
• 사생활 침해 여부 존재

61. • linguistic
• identification and extraction of
word instances (unigrams) and
word-pair instances (bi-grams)
from the transcriptions
• acoustic
• vocal dynamics
• voice quality
• vocal tract resonance frequencies
• pause lengths
A Machine Learning Approach to Identifying the
Thought Markers of Suicidal Subjects:A
Prospective Multicenter Trial
• “Do you have hope?”
• “Do you have any fear?”
• “Do you have any secrets?”
• “Are you angry?”
• “Does it hurt emotionally?”
Pestian, Suicide and Life-Threatening Behavior, 2016

62. A Machine Learning Approach to Identifying the
Thought Markers of Suicidal Subjects:A
Prospective Multicenter Trial
SensitivitySensitivity
1.00.00.20.40.60.81.00.00.20.40.60.81.0
1.0 0.8 0.6 0.4 0.2 0.0
Specificity
1.0 0.8 0.6 0.4 0.2 0.0
Specificity
SUICIDE THOUGHT MARKERS
SensitivitySensitivitySensitivity
0.00.20.40.60.81.00.00.20.40.60.81.00.00.20.40.60.81.0
1.0 0.8 0.6 0.4 0.2 0.0
Specificity
1.0 0.8 0.6 0.4 0.2 0.0
Specificity
1.0 0.8 0.6 0.4 0.2 0.0
Specificity
Figure 1. Receiver operator curve (ROC): suicide versus control (upper), suicide versus mentally ill (middle), and
SensitivitySensitivity
0.00.20.40.60.81.00.00.20.40.60.81.0
1.0 0.8 0.6 0.4 0.2 0.0
Specificity
1.0 0.8 0.6 0.4 0.2 0.0
Specificity
Figure 1. Receiver operator curve (ROC): suicide versus control (upper), suicide versus mentally
suicide versus mentally ill with control. The ROC curves for adolescents (blue), adults (red), and a
generated where the nonsuicidal population is controls (top), mentally ill (middle), and mentally
using linguistic and acoustic features. The gray line is the AROC curve for a baseline (random) cla
TABLE 2
The AROC for the Machine Learning Algorithm. The Nonsuicidal Group Comprises of Either Mentally Ill and Control Subjects. Classification
Performances are Shown for Adolescents, Adults, and the Combined Adolescent and Adult Cohorts
Suicidal versus Controls Suicidal versus Mentally Ill Suicidal versus Mentally Ill and Controls
Adolescents
ROC (SD)
Adults
ROC (SD)
Adolescents +
Adults
ROC (SD)
Adolescents
ROC (SD)
Adults
ROC (SD)
Adolescents +
Adults
ROC (SD)
Adolescents
ROC (SD)
Adults
ROC (SD)
Adolescents +
Adults
ROC (SD)
Linguistics 0.87 (0.04) 0.91 (0.02) 0.93 (0.02) 0.82 (0.05) 0.77 (0.04) 0.79 (0.03) 0.82 (0.04) 0.84 (0.03) 0.87 (0.02)
Acoustics 0.74 (0.05) 0.82 (0.03) 0.79 (0.03) 0.69 (0.06) 0.74 (0.04) 0.76 (0.03) 0.74 (0.05) 0.80 (0.03) 0.76 (0.03)
Linguistics +
Acoustics
0.83 (0.05) 0.93 (0.02) 0.92 (0.02) 0.80 (0.05) 0.77 (0.04) 0.82 (0.03) 0.81 (0.04) 0.84 (0.03) 0.87 (0.02)
PESTIANETAL.
Suicidal vs. Control Suicidal vs. Mentally Ill Suicidal vs. Mentally Ill and Controls
adolescents
adults
Pestian, Suicide and Life-Threatening Behavior, 2016

63. SimSensei/MultiSense (2014)

64. SimSensei/MultiSense (2014)

65. G.M. Lucas et al. / Computers in Human Behavior 37 (2014) 94–100
인간 의사와 인공지능 의사 중 누가 라뽀 형성을 더 잘 할까?

66. It’s only a computer:
Virtual humans increase willingness to disclose
G.M. Lucas et al. / Computers in Human Behavior 37 (2014) 94–100
인공지능이
상담한다고 믿음
(computer frame)
사람이 원격으로
상담한다고 믿음
(human frame)
실제로 인공지능이 상담
(AI)
실제로 사람이 상담
(Teleo-operated)
Method
Frame

67. It’s only a computer:
Virtual humans increase willingness to disclose
G.M. Lucas et al. / Computers in Human Behavior 37 (2014) 94–100
‘‘How close are you to your family?’’
‘‘Tell me about a situation that you wish you had handled differently.’’
‘‘Tell me about an event, or something that you wish you could erase from your memory.’’
‘‘Tell me about the hardest decision you’ve ever had to make.’’
‘‘Tell me about the last time you felt really happy.’’
‘‘What are you most proud of in your life?’’
‘‘What’s something you feel guilty about?’’
‘‘When was the last time you argued with someone and what was it about?’’

68. It’s only a computer:
Virtual humans increase willingness to disclose
G.M. Lucas et al. / Computers in Human Behavior 37 (2014) 94–100
0
5
10
15
20
Computer frame Human frame
0
15.25
30.5
45.75
61
Computer frame Human frame
0
0.033
0.065
0.098
0.13
Computer frame Human frame
0
0.3
0.6
0.9
1.2
Computer frame Human frame
Fear of Self-disclosure Impression Management Sadness Displays Willingness to Disclosure

69. It’s only a computer:
Virtual humans increase willingness to disclose
G.M. Lucas et al. / Computers in Human Behavior 37 (2014) 94–100
‘‘This is way better than talking to a person. I don’t really feel
comfortable talking about personal stuff to other people.’’
‘‘A human being would be judgmental. I shared a lot of
personal things, and it was because of that.’’

70. “A lot of Syrian refugees have trauma and maybe
this can help them overcome that.” However, he
points out that there is a stigma around
psychotherapy, saying people feel shame about
seeking out psychologists.As a result he thinks
people might feel more comfortable knowing they
are talking to a “robot” than to a human.

71. On-line Therapy: TalkSpace

72. Disclosure: Conflict of Interest with Souling
On-line Therapy: Souling

73. 해결해야 할 숙제들

74. 모바일 헬스에 대한 FDA의 가이드라인
•2011년 7월: 모바일 의료 어플리케이션 가이드라인 초안
•2013년 10월: 업데이트 된 최종 가이드라인 제시
•2015년 1월: 웰니스 목적의 위험도가 낮은 기기에 대한 가이드라인

75. 모바일 헬스에 대한 FDA의 가이드라인
모든 앱과 기기들이 FDA 규제를 적용 받아야 하는 것은 아니나,
그 기능이 제대로 작동하지 않을 경우 소비자들의 건강을 위협할 수도 있는 앱과 기기는
기존의 의료용 기기가 받았던 것과 같은 엄격한 수준의 규제를 적용한다.
의료 기기/앱의 경우에도 리스크가 높지 않으면
규제 받지 않을 수 있다.

76. http://www.nature.com/news/mental-health-there-s-an-app-for-that-1.19694
• 특히 정신 건강 관리 앱은 잠재적인 리스크 정도를 쉽게 판단하기 어려운 경우가 많다.
• 의료기기 / 웰니스의 불명확한 경계
• 질병을 예방, 치료, 진단해주는 앱
• ‘기분을 좋게 해준다’ 거나, ‘코칭’ 을 해주는 앱?

77. • 2013년 연구
• 43%의 앱만이 프라이버시 정책을 가지고 있음
• 72%의 앱이 개인 프라이버시에 대한 중간 (32%), 혹은 높은 (40%) 위험도

78. RECEIVED 22 December 2013
REVISED 7 July 2014
ACCEPTED 3 August 2014
PUBLISHED ONLINE FIRST 21 August 2014
Availability and quality of mobile health app
privacy policies
Ali Sunyaev1
, Tobias Dehling1
, Patrick L Taylor2
, Kenneth D Mandl3
ABSTRACT
....................................................................................................................................................
Mobile health (mHealth) customers shopping for applications (apps) should be aware of app privacy practices so they
can make informed decisions about purchase and use. We sought to assess the availability, scope, and transparency of
mHealth app privacy policies on iOS and Android. Over 35 000 mHealth apps are available for iOS and Android. Of the
600 most commonly used apps, only 183 (30.5%) had privacy policies. Average policy length was 1755 (SD 1301)
words with a reading grade level of 16 (SD 2.9). Two thirds (66.1%) of privacy policies did not specifically address the
app itself. Our findings show that currently mHealth developers often fail to provide app privacy policies. The privacy pol-
icies that are available do not make information privacy practices transparent to users, require college-level literacy, and
are often not focused on the app itself. Further research is warranted to address why privacy policies are often absent,
opaque, or irrelevant, and to find a remedy.
....................................................................................................................................................
INTRODUCTION
Apple’s iOS and Google’s Android operating systems and asso-
ciated application (app) stores, itunes.apple.com and play.goo-
gle.com, are becoming the de facto global platforms for mobile
health (mHealth).1,2
Recently, both platforms additionally
announced the roll out of their own apps fostering app interop-
erability and offering central storage for all mHealth apps and
sensors of users’ devices.3,4
mHealth apps leverage a wide
range of embedded technology in iOS and Android devices for
collecting and storing personal data, including contacts and
calendars, and patient-reported data as well as information col-
lected with cameras and sensors, including location, accelera-
tion, audio, or orientation.
5–7
Although patients value control of
their personally identifiable data8,9
and the Federal Trade
Commission10
recommends provision of privacy policies for
mobile apps, little attention has been paid to the information
security and privacy policies and practices of mHealth app ven-
dors. Although both app stores retain the right to remove apps
for infringements of privacy, neither has explicit policies
addressing the information security and privacy of medical in-
formation. Users choose among an ecosystem of substitutable
mHealth apps11
and should have transparency as to which
apps have privacy practices best aligned with their individual
preferences. We sought to assess mHealth apps for the pres-
ence and scope of privacy policies, and what information they
offer.
METHODS
We surveyed (figure 1) the most frequently rated and thus pop-
ular English language mHealth apps in the Apple iTunes Store
and the Google Play Store. App stores organize their offerings
in categories (eg, Books, Games, and News). We selected apps
from the Medical and Health and Fitness categories offered in
both stores in May 2013. The iOS app store lists all apps by
category and offers the desired information in plain hypertext
markup language (HTML), enabling us to automatically parse
app information to extract data. On the other hand, the Android
app store uses dynamically generated HTML pages so that the
HTML texts displayed in the browser do not contain much use-
ful information, which is dynamically loaded from an underlying
database. Hence, we used a third-party open-source interface,
the android-market-api (http://code.google.com/p/android-
market-api), for retrieving app information.
Upon initial review, many apps were not available in
English, did not have an English description, or were not
health-related, despite being offered in the categories Medical
or Health and Fitness (eg, apps offering wallpapers). In order to
exclude such apps from further assessment, we tagged all app
descriptions with descriptive terms. The tags characterize
health-related app functionality, access to information, and
handling of information. We manually tagged 200 apps (100
Health and Fitness, 100 Medical) establishing an initial tag cor-
pus and employed string matching12
to automatically tag the
remaining apps. Apps not matched by at least four distinct tags
were excluded from further assessment.
Discovery and evaluation of privacy policies
We used a three-step manual procedure for privacy policy dis-
covery looking at typical locations for privacy policies. Privacy
policies were abstracted from March 2013 to June 2013. First,
we checked for a privacy policy on the app store web site for
the particular app. Then we checked the web page maintained
Correspondence to Professor Ali Sunyaev, Faculty of Management, Economics and Social Sciences, University of Cologne, Albertus-Magnus-Platz, Cologne 50923,
Germany; sunyaev@wiso.uni-koeln.de
BRIEFCOMMUNICATION
Sunyaev A, et al. J Am Med Inform Assoc 2015;22:e28–e33. doi:10.1136/amiajnl-2013-002605, Brief Communication
byguestonApril17,2016http://jamia.oxfordjournals.org/Downloadedfrom
• 2014년 연구
• 600개의 앱 중에 183 개 (약 30%) 만이 프라이버시 정책을 가지고 있었음

79. Letters
RESEARCH LETTER
Privacy Policies of Android Diabetes Apps
and Sharing of Health Information
Mobile health apps can help individuals manage chronic
health conditions.1
One-fifth of smartphone owners had
health apps in 2012,2
and 7% of primary care physicians rec-
ommended a health app.3
The US Food and Drug Adminis-
tration has approved the prescription of some apps.4
Health
apps can transmit sensitive medical data, including disease
status and medication compliance. Privacy risks and the
relationship between privacy disclosures and practices of
health apps are understudied.
Methods | On January 3, 2014, we identified all Android dia-
betes apps by searching Google Play using the term diabetes.
Android is the most popular mobile operating system
worldwide with 82.8% market share (compared with Apple
iOS’s 13.9%).5
We collected and analyzed privacy policies
and permissions (disclosures of what apps can access or
control on the device) for apps that remained 6 months after
our initial search. Because consumers may want to know
about privacy protections before choosing an app, we deter-
mined which apps had policies available predownload and
what the policies protected. Then we installed a random
subset of apps to determine whether data were transmitted
to third parties, defined as any website not directly under
the developer’s control, such as data aggregators or adver-
tising networks.
We performed χ2
tests of independence (Excel 2010,
Microsoft) to determine whether apps with privacy policies
were more likely to protect personal information than apps
without privacy policies. A 2-sided P value less than .05 was
considered significant.
Results | We identified 271 diabetes apps and chose a random
sample of 75 for the transmission analysis. Within 6
months, 60 apps became unavailable, leaving 211 apps in
the sample and 65 apps in the subset. Most of the 211 apps
(81%) did not have privacy policies. Of the 41 apps (19%)
with privacy policies, not all of the provisions actually pro-
tected privacy (eg, 80.5% collected user data and 48.8%
shared data) (Table 1). Only 4 policies said they would ask
users for permission to share data.
Permissions, which users must accept to download an
app, authorized collection and modification of sensitive
information, including tracking location (17.5%), activating
the camera (11.4%), activating the microphone (3.8%), and
modifying or deleting information (64.0%) (Table 2).
In the transmission analysis, sensitive health informa-
tion from diabetes apps (eg, insulin and blood glucose lev-
els) was routinely collected and shared with third parties,
with 56 of 65 apps (86.2%) placing tracking cookies; 31 of
the 41 apps (76%) without privacy policies, and 19 of 24
apps (79%) with privacy policies shared user information,
which was not statistically significantly different (N = 65;
Table 1. Privacy Policy Provisions for the 41 Apps With Privacy Policies
(19% of the 211 Apps)a
Type of Privacy Policy Provision
Apps,
No. (%)
Personal Information
Shared if required by law 25 (61.0)
Collected when the app is used 21 (51.2)
Collected when a user registers
through an online account
21 (51.2)
Stored in the developer’s system 18 (43.9)
Only disclosed with the user’s consent 12 (29.3)
Shared to improve service 11 (26.8)
Shared with business partners 10 (24.4)
Not sold 9 (22.0)
No personal information
from children collected
6 (14.6)
User Data
Collected 33 (80.5)
Shared with partners and/or third parties 20 (48.8)
May be used for advertisement purposes 16 (39.0)
May be transferred to various countries
around the world
11 (26.8)
Electronic safeguards for data protection
are used
22 (53.7)
Cookies will be used 20 (48.8)
Log files will be collected 7 (17.1)
Aggregated User Data
Does not contain personal information 19 (46.3)
May be used to create statistics 15 (36.6)
May be disclosed to advertisers 7 (17.1)
User Options
Can opt out of cookies 13 (31.7)
Can opt out of receiving emails 9 (22.0)
Can opt out of receiving marketing materials 6 (14.6)
a
Provisions reflect the language used in privacy policies. Less common
provisions (n Յ5) were the following: no personal information from children
under 13 years of age is collected without the consent of a parent; personal
information will not be disclosed to third parties for direct marketing
purposes; data will be shared only with permission from the user; data will not
be shared; personal information will not be shared; health information is
treated differently than other types of information; data will not be sold;
information is collected during the app download process; personal
information will be shared with advertisers; user can opt out of information
transfer to third parties for marketing purposes; no data will be stored;
aggregated user data may be disclosed to analytics and search engine
providers; personal information will be shared with research organizations.
jama.com (Reprinted) JAMA March 8, 2016 Volume 315, Number 10 1051
• 2015년 연구
• 총 211개의 앱 중에 41개 (19%)만 프라이버시 정책이 있음
• 이 41개 앱의 프라이버시 정책을 살펴보면,
• 48%는 써드 파티에게 사용자 데이터를 공유함
• 61%는 법으로 필요한 경우는 데이터가 공개될 수 있음
• 43%는 개발자 시스템에 데이터가 저장
• 즉, 프라이버시 정책을 가지고 있는 경우에도, ‘보호하지 않는’ 정책인 경우가 상당수

81. Feedback/Questions
• E-mail: yoonsup.choi@gmail.com
• Blog: http://www.yoonsupchoi.com
• Facebook: 최윤섭 디지털 헬스케어 연구소