Table Of ContentHabits vs Environment: What Really Causes Asthma?
Mengfan Tang Pranav Agrawal Ramesh Jain
DepartmentofComputerScience DepartmentofComputer DepartmentofComputer
UniversityofCalifornia,Irvine Science Science
[email protected] UniversityofCalifornia,Irvine UniversityofCalifornia,Irvine
[email protected] [email protected]
ABSTRACT data, smart-phone and wearable sensor data, a data driven
6
solution holds the promise for helping solve this problem
1 Despite considerable number of studies on risk factors for
[12]. Thus, lots of studies have been performed using var-
0 asthma onset, very little is known about their relative im-
ious forms of data. [11] predicted asthma attacks by con-
2 portance. To have a full picture of these factors, both cat-
sideringbio-signalsofpatientsandenvironmentaldata. [10]
egories, personal and environmental data, have to be taken
n studied the association between spatial distribution of al-
into account simultaneously, which is missing in previous
a lergy prevalence and air pollutants such as PM2.5, as well
J studies. We propose a framework to rank the risk factors
as living distance from point of interests such as parks and
from heterogeneous data sources of the two categories. Es-
0 roads. [3] detected asthma risk from personal profiles. [5]
tablishedontopofEventShopandPersonalEventShop,this
2 designedtoolsfordiscoveringdynamicchangesinbodysen-
framework extracts about 400 features, and analyzes them
by employing a gradient boosting tree. The features come sor network data streams of asthma patients. [1] studied
] occupation as a factor in asthma. [6] researched associated
Y from sources including personal profile and life-event data,
risk factors of air pollution and weather, [7] revealed the
andenvironmentaldataonairpollution,weatherandPM2.5
C impact of lifestyle and behavior on asthma vulnerability.
emission sources. The top ranked risk factors derived from
. However,thesestudiesonlyfocusoneitherenvironmental
s our framework agree well with the general medical consen-
c sus. Thus, our framework is a reliable approach, and the factors or personal factors without a comprehensive study
[ covering both. Asthma patients vary in sensitivity to dif-
discoveredrankingsofrelativeimportanceofriskfactorscan
provide insights for the prevention of asthma. ferent environmental and personal factors, and their inter-
1
actions. A data driven solution can convert personal data
v
and environmental data into information and insights for
1 CategoriesandSubjectDescriptors
discoveringcomprehensiveriskfactors, aswellasaidinun-
4
H.4[InformationSystemsApplications]: Miscellaneous; derstanding of asthma.
1
D.2.8 [Software Engineering]: Metrics—complexity mea- To identify these risk factors within integrated diverse
5
sures, performance measures; J.3 [Life and Medical Sci- data sources, new approaches are required. We propose a
0
ences]: Health frameworkontopofPersonalEventShop[8]andEventShop
.
1 [4],tosolvethisproblembecauseoftheircapabilityofinte-
0 GeneralTerms gratingandanalyzingheterogeneousdatasources. EventShop
6 isagenericinfrastructureforapplicationdeveloperstoana-
1 Experimentation, Human Factors lyzevariedspatio-temporaldatastreams. PersonalEventShop
:
v Keywords isaunifiedframeworkforaggregatingpersonaldatastreams
i toanalyzelifeeventsandpersonalsituations. Detailsofthis
X
Asthma,Featureextraction,Asthmariskanalysis,Gradient framework are described in the next section.
r Boosting Tree
a
2. FRAMEWORK
1. INTRODUCTION
TheproposedframeworkisbasedonPersonalEventShop
Althoughasthmaisapotentiallylifethreateninglungdis- andEventShop. Thethreemainpartsoftheframeworkare
easeandhasbeenstudiedforalongtime,asthmacausesare briefly discussed.
stillunclear. Withincreasingamountofdata,suchasElec-
tronic Health Records, social media, environmental sensory Dataingestion
Permissiontomakedigitalorhardcopiesofallorpartofthisworkfor Personal EventShop receives data from wearable sensors,
personalorclassroomuseisgrantedwithoutfeeprovidedthatcopiesarenot
mobileappslikecalendar,smart-phonesensorslikeaccelerom-
madeordistributedforprofitorcommercialadvantageandthatcopiesbear
etersandGPS.Itcorrelatesthesedatastreams,determines
thisnoticeandthefullcitationonthefirstpage.Copyrightsforcomponents
ofthisworkownedbyothersthanACMmustbehonored.Abstractingwith user’sactivitylevels,lifeeventssuchasexercising,sleeping,
creditispermitted.Tocopyotherwise,orrepublish,topostonserversorto andworking. Thischronicleoflifeevents,called‘Personicle’
redistributetolists,requirespriorspecificpermissionand/orafee.Request is stored in a database with attributes of user-id, location,
[email protected]. timeandlifeevents. Italsocollectsdatafromglobalsources
WebSci’15June28-July01,2015,Oxford,UnitedKingdom
like EventShop, which can provide aggregated environmen-
(cid:13)c2015ACM978-1-4503-3672-7/15/06$15.00.
taldatafromdifferentsensors. Inthecontextofthispaper,
DOI:http://dx.doi.org/10.1145/2786451.2786481.
Figure 1: 1(a)-1(c) Maps showing expected county level emissions from agriculture, road dust, and mining
respectively 1(d)-1(f) Maps showing average maximum, mean, and minimum PM2.5 concentration values for
the month of June, for counties with data in CHAD
EventShop can provide interpolated PM2.5 values inferred the data is discussed in the section below.
fromsourceslikeairpollutionmonitoringsites,satelliteim-
agery and traffic [14]. All of this data is collected and sent ConsolidatedHumanActivityData
for aggregation and matching. Consolidated Human Activity Database Master (CHAD)
[13] is a collection of profile and activity information from
Dataaggregation
22 studies, and contains data for over 54,000 days com-
Ingesteddatahasawiderangeofgranularitiesinbothspace binedfromover700countiesacrosstheUnitedstates. Each
andtime. Forexample,inthecontextofthispaper,emission personal profile contains information like zip-code, county,
inventorydatawasreportedoncountylevelonceeverythree age, gender, race, history of asthma, cardiovascular illness,
years, activity data was reported over a period of several whether the person is a smoker, or lives with a smoker,
years,andvaryinglevelsofspatialgranularity,andpollution employment, education level, and income. Every person
datawascollectedatstationlocations,andwasreportedas has a set of diary entries. Each diary entry is an activity
daily values. Personal EventShop takes in all this data and record containing activity (like walking, exercising, sleep-
matchesittoacommonspatio-temporalscale. Thisdatais ing,leisure),location(home,office,traveling),startandend
sent to the Analysis Engine. times. Everydiaryentryalsocontainsflagsshowingwhether
the subject was breathing heavily and whether the subject
AnalysisEngine
was smoking during each activity.
Thispartusesaggregateddata,andperformsdesiredanaly-
EmissionFactors
sesonit. Forexample,theseanalysescanbe,co-occurrence
patterndetection,personalsituationrecognitionorlifeevent TheNationalEmissionInventorydatawascollectedin2011,
detection. Inthiscase,theaggregateddatafromthesources by the EPA. This is an estimate of PM2.5 contributions of
described in section 3 are transformed to features as de- all the sources of air pollution within the United States, in
scribedinsection4. Thefeatureswiththehighestdiscrimi- metric tonnes/year. Sources are categorized into classes
natorypoweraredetectedusingthemodeldescribedinsec- like Agricultural, Industrial, Dust, Fuel, and Mobile.
tion 5 and then used to rank the asthma risk factors.
HistoricalAirPollutionData
3. DATA
Historical air pollution data is the actual concentrations of
Our asthma risk analysis approach requires various per- pollutantsintheenvironment. Thisdataisavailableasdaily
sonal and environmental features at the same granularity. averagevaluesrecordedateachoftheindividualmonitoring
However, the data is at different resolutions, such as point stations across the United States. The stations also record
data and region data. Data sources are described in this thedailyaverageweatherconditions: temperature,pressure,
section, and the methodology to obtain the features from wind speed, and wind direction.
In the next section, we describe the features extracted
Table 2: Emission Factors
from these data sources, and how they are used.
Category Emission Factor
Mobile Aircraft, Marine Vessels, Locomo-
4. FEATUREANDDATAANALYSIS
tives, Equipment, Heavy Duty Vehi-
cles, Light Duty Vehicles
4.1 PersonalLifeEventsFeatures: FP Industrial Agricultural,Mining,Oil&GasPro-
WeusetheprofileinformationprovidedinCHADforpeo- duction, Storage & Transportation,
ple that had location information at the county level, and Other
hadansweredthequestionwhethertheyhadasthmaornot. Dust Construction,PavedRoadDust,Un-
The number of such people was 11,000, with 24,000 days of paved Road Dust
activity data. Of these, around 950 people reported having Fires Agricultural Field Burning, Pre-
asthma,whichisclosetotheasthmaprevalencerateof8.7% scribed Fires, Wildfires
in the United States [9]. For analysis, we use the asthmatic Fuel Biomass, Coal, Natural Gas, Oil,
subjects and an equal number of non-asthmatic subjects. Residential Wood, Other
Thenon-asthmaticsubjectsarerandomlychosentobalance Miscellaneous Waste Disposal, Agriculture, Com-
the data set. mercial Cooking
Features extracted and their justifications, as mentioned
in [9] are described in Table 1.
as ti :
hb
Table 1: Extracted Personal Profile Features ti =mean((cid:88)d ) {∀j,x:x∈Dj and hb =1}
Feature Comment hb j x x,j pi x,j
Age Children are more susceptible The average amount of time in the entries where p reports
i
than adults smoking, is also calculated as ti, in the same way.
s
Gender Femalesaremoresusceptiblethan Theaveragecountofthenumberoftimesp reportsheavy
i
males breathing, is recorded as (ni ):
hb
Active & Passive Smokers are more susceptible
Smoking nihb =mejan((cid:88)hbx,j) {∀j,x:x∈Dpji and hbx,j =1}
Occupation, Income, Lowerincomeandlowereducation x
& Education correlate with asthma Likewise, the average count of activities per day, where the
Hours of work & Em- Measure of stress smoking flag was 1, is calculated as ni. Figure 4.1 shows
s
ployment Status twenty five randomly chosen daily diaries from the data.
Gas stove ownership, Exposure to smoke
heating & fuel type 4.2 EmissionFactorsFeatures: FE
PM2.5emissionsourcesfromthenationalemissioninven-
tory were used at county level. Different kinds of PM2.5
Activity data is in the form of a set D of daily diary
emissions have different health effects on people. The emis-
entry tuples. Each tuple x for the day j contains, the ac-
sion amount by each type of pollution source, like mobile,
tivity’sstarttime,durationd ,activitycodea ,location
x,j x,j industrial, dust, fires, and fuel, was used as a feature. The
code l , flags for whether the person was smoking (s ),
x,j x,j various emission factors are listed in Table 2.
whetherthepersonwasbreathingheavily(hb )duringthe
x,j
activity . First, locations are classified into a set L of five 4.3 AirPollutionFeatures: F
A
non-exclusive categories:
Historical air pollution data records are in the form of
daily average pollutant concentration values at the measur-
L={work, travel, home, indoor, outdoor}
ingstationlevel. Since all of ouranalysisisatcountylevel,
ActivitiesareclassifiedintoasetAofsixnon-exclusivecat- we interpolate this point data to region data. This can be
egories: done using a lot of techniques, ranging from pure chemical
transportation models, to enhanced data based techniques
A={sleep, work, exercise, walking, cycling, leisure} [14]. For this experiment, linear interpolation was used.
The data used is between years 2001 and 2014. The pol-
For every person p for day j, in the set of people P, let
i lutants considered are: PM2.5, Ozone, Carbon Monoxide,
the set of corresponding diary entries be Dj . For every
pi Sulphur Dioxide, and Nitrogen Dioxide. The weather fac-
personp ,andeverylocationcategoryL ,theaveragetime
i k torsconsideredare: temperature,pressure,andwindspeed.
spentdailyatlocationsinthatcategoryti iscomputedfor
Lk For each pollutant and weather factor f, the following
diary entries x in Dj over all j:
pi features are extracted:
ti =mean((cid:88)d ) {∀j,x:x∈Dj and l ∈L } fm =mean(max(vm,y))
Lk j x,j pi x,j k max y d d,f
x
pSiemrfiolarmrlyin,gfoarctaicvtiitvieitsieisn, etahcehaavcetriavgietyticmateegsporeyntAda,ildyenboytepdi fmmean =meyan(medan(vdm,f,y))
k
by ti , is computed. Also, the average amount of time in
Ak fm =mean(min(vm,y)),
the entries where pi reports heavy breathing, is computed min y d d,f
Figure 2: 25 randomly chosen diary entries from CHAD. Each coloured row on the y-axis represents a day’s
activities for one person. The color marks the activities, and the x-axis shows time in 15 minute increments
where, f ∈{PM2.5, SO , NO , O , CO, temperature,
2 2 3
pressure, windspeed},yistheyearbetween2001and2014,
m is the month and d is the day of the month.
5. MODELS
Consider a feature vector x ∈ Rd in the d-dimensional
featurespace,y+isthelabelforsubjectswithasthma,y−is
the label for subjects without asthma. Our feature ranking
modelisbasedongradientboostingregressiontree[2]. Risk
factorsarerankedbyanalyzingimportanceoffeaturesfrom
this model. Gradient boosting tree framework generates an
ensembleofweakregressiontreemodelsandcombinesallthe
weak learners to produce a strong classifier. The training
algorithm performs gradient descent in function space to
minimizeadifferentiablelossfunction. SupposeF(x)isthe
model, we have,
M
(cid:88)
F(x)= λ f (x) (1)
i i
i=1
where f (x) is the ith weak learner, λ is the weight associ-
i i
ated with that weak learner.
One of the advantages of using this model for ranking
Figure 3: Relative importance of features as deter-
features in asthma study is that no normalization of data
mined by a gradient boosting tree
is needed, and thus it is better at handling categorical data
with discrete and continuous data.
The model hyper-parameters for the gradient boosting
the identified data sources.
classifier(depthandnumberoftrees)areselectedusingfive-
The most significant contribution of our paper is the de-
foldcrossvalidation. Thedepthwasvariedbetween1,2,and
termination of the relative importance of factors in causing
3,andthenumberoftreeswaschosenfrom50,100,and150.
asthma. The twenty most effective features out of around
Afterchoosingthebesthyper-parameters,thelearnedmodel
400extractedfeatures,andtheirrelativeimportanceisshown
was then applied to the remaining subset.
in Figure 5. The top five features correspond to personal
AKnearestneighborclassifierwasusedtopredictwhether
factorssuchasphysicalexertionandworkstress. Following
a person is asthmatic or not, using their features. The pa-
that, environmental features like forest fires and industrial
rameter K was, again, cross validated on the training data
fuel burning show up. Other features that appear in the
using five folds, to select the best K.
analysis are smoking habits, passive smoking, personal ex-
posure to smoke from domestic fuels and heating. Lastly
6. RESULTS
PM2.5 and SO concentration during summer (July) also
2
Inthissectionwepresentourresultsbasedonthefeatures appear on the list. These are all, independently, known to
extractedfromthedatasources,showingtheeffectivenessof be important factors affecting asthma susceptibility. Our
ordering suggests that personal factors like physical exer- [2] J. H. Friedman. Greedy function approximation: A
tionandstressarestrongindicatorsofasthmarisk,followed gradient boosting machine. Ann. Statist.,
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becausetheydon’tneedextensivediarykeepingbythesub- Jones, and S. Bassin. Predictive value of a
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There is no clear understanding of the causes of asthma
[10] Y. Kanani Sadat, F. Karimipour, and
andnodefinitecure,makingitdrawincreasingattentionin
A. Kanani Sadat. Investigating the Relation Between
healthcarestudies. Existingstudiesoftenareperformedon
Prevalence of Asthmatic Allergy with the
either personal data or environmental data. We proposed
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