predicting the spatio-temporal evolution of chronic ... · predicting the spatio-temporal evolution...

Predicting the Spatio-Temporal Evolution of Chronic Diseases in Population withHuman Mobility Data

Yingzi Wang1,2∗, Xiao Zhou2, Anastasios Noulas3, Cecilia Mascolo2, Xing Xie1, Enhong Chen1

1 University of Science and Technology of China2 University of Cambridge

3 Center for Data Science of New York [email protected], [email protected], [email protected],[email protected], [email protected], [email protected]

AbstractChronic diseases like cancer and diabetes are ma-jor threats to human life. Understanding the dis-tribution and progression of chronic diseases of apopulation is important in assisting the allocation ofmedical resources as well as the design of policiesin preemptive healthcare. Traditional methods toobtain large scale indicators on population health,e.g., surveys and statistical analysis, can be costlyand time-consuming and often lead to a coarsespatio-temporal picture. In this paper, we leveragea dataset describing the human mobility patternsof citizens in a large metropolitan area. By view-ing local human lifestyles we predict the evolutionrate of several chronic diseases at the level of a cityneighborhood. We apply the combination of a col-laborative topic modeling (CTM) and a Gaussianmixture method (GMM) to tackle the data spar-sity challenge and achieve robust predictions onhealth conditions simultaneously. Our method en-ables the analysis and prediction of disease rateevolution at fine spatio-temporal scales and demon-strates the potential of incorporating datasets frommobile web sources to improve population healthmonitoring. Evaluations using real-world check-inand chronic disease morbidity datasets in the cityof London show that the proposed CTM+GMMmodel outperforms various baseline methods.

1 IntroductionRecent studies show that many chronic and malignant dis-eases, e.g., heart disease, diabetes, and cancer, are extremelyprevalent in our society [Siegel et al., 2015]. To estimate dis-ease penetration in a population and to be then able to im-plement appropriate health-care and preventative measures,healthcare administrators often perform statistical analysisfrom the records of hospital visits or conduct survey amonga sample of residents. The analysis and survey usually incur∗This work was done while Yingzi Wang was a visitor at the

University of Cambridge

high labor costs and are time-consuming. In addition, theyoften lead to coarse estimates both spatially and temporally.

Although chronic diseases are, to some extent, related topatients’ genetics, recent studies have shown that 70% to 90%of chronic diseases can be attributed to other factors [Rap-paport and Smith, 2010]. For example, there is a causal re-lationship between chronic diseases and our daily habits,such as diet [McCullough et al., 2002] and alcohol con-sumption [Martınez, 2005], which reflect different aspects ofhumans’ lifestyles. Lifestyle depicts typical routine lives ofpeople. Large-scale human mobility data collected throughlocation-based social network services can act as a proxy forhuman lifestyle [Yuan et al., 2013]. For instance, regular vis-its to college libraries, gyms, and lecture theaters, may cor-respond to the lifestyle of a college student, while a recordof constant visits to meeting rooms and restaurants may indi-cate the lifestyle of a white-collar employee. These lifestylesare correlated with, and may reveal, certain chronic diseaseconditions of populations.

The connection between human mobility and diseases hasbeen explored before. Several previous studies have investi-gated the outbreaks of infectious diseases via social ties andhuman mobility patterns [Meyers, 2007]. For chronic dis-eases, researchers have explored the association between hu-man online activities and obesity [Mejova et al., 2015]. How-ever, little attention has been paid to lifestyes as a bridgebetween human mobility and chronic diseases nor exploringsuch correlation together with diseases-location similarity topredict residents’ health.

In this work, we leverage the correlations between healthand lifestyle reflected in human mobility to predict humanhealth progression in populations. Using such data for theseestimates gives unprecedented spatial and temporal granular-ity to the analysis but has also the potential to lower the costsof these studies and enable them to be applicable to regionsfor which other techniques would be deemed impractical ortoo expensive (e.g. developing regions). Here, we use humanvisitation patterns (check-ins) estracted from Foursquare (alocation intelligence application) and the statistics of chronicdisease morbidity in the London metropolitan area (presentedon the government opening data website of the UK). We cap-ture regional lifestyles as reflected in Foursquare mobility

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18)

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data, and apply a hybrid model to improve the prediction ofpublic health conditions over simply using historic dataset. Insummary, this paper offers the following contributions:• We explore the correlations between human mobility pat-

terns and health conditions and apply a method which com-bines Gaussian mixture models (GMM) with collaborativetopic modeling (CTM) to predict the health levels of a pop-ulation, i.e., leveraging “where they go” to help predict“how healthy they are”.• We get clues about human lifestyles from mobility patterns

of residents, assuming that the groups of visited POIs areproxies for different lifestyles. We then exploit these in-puts to identify fine-grained spatio-temporal associationsbetween these lifestyles and chronic diseases for local pop-ulations.• We collect real-world chronic disease and check-in data to

evaluate our method and analyse the correlation betweenlifestyles and chronic diseases. Compared with methodsusing historic information solely, the proposed methodshows a 45.7% reduction in mean square erro (MSE) and a1.67 times increase in R-squared value (R2).

2 Related WorkDisease prediction. Many existing studies have tried to un-derstand the spread of infectious diseases and forecast theiroutbreaks. Some works have analysed the social or con-tact networks formed by connections among individuals andhuman mobility patterns to model the outbreaks of infec-tious diseases [Meyers, 2007], while some others utilise largeamounts of users’ status posts on social networks, such asTwitter, to analyse the public health on a large scale [Pauland Dredze, 2011]. Some other studies target on chronic dis-eases. Matic et al. [Matic and Oliver, 2016] seek help fromsmartphone-based health applications and wearable devicesto continuously record human behaviour to analyse mental-health condition of individuals. The work [Mejova et al.,2015] employed both Foursquare and Instagram data to as-sess the relationship between fast food and obesity. Mason etal. [Mason et al., 2018] found that people who lived far awayfrom fast-food resturants were more likely to have small waistcircumference, espetially for women. Howere, there is a no-ticeable lack of research about the effects of human mobilitypatterns on the development of chronic diseases in small ur-ban areas. To the best of our knowledge, no one has exploredthe similarities between chronic diseases and urban regionssimultaneously. Our work aims to fill this gap.

Human mobility analysis. Patterns of human mobility arepredictable and reflect how the residents of a certain area livein the physical world [Cho et al., 2011]. Many scholars havetried to learn such patterns in order to predict the movement ofindividuals [Gao et al., 2012]. Extensive research efforts havealso been focused on, e.g., finding typical travel sequencesby studying users’ check-in trajectories [Zheng et al., 2009],predicting users’ moving patterns by exploiting both the regu-larity of human mobility and influence of others [Wang et al.,2015], and making location recommendations with graphicalmodels that integrates users’ preferences with their sequentialmovement patterns [Wang et al., 2016]. Different from above

studies, in this paper we investigate the correlations betweenresidents’ chronic disease development and their lifestyles in-dicated by their frequently visited venues and mobility habits.

Topic and Gaussian mixture models. Our analysisutilises topic modeling [Blei et al., 2003] and Gaussian mix-ture models [Friedman and Russell, 1997]. Topic model-ing has been widely used generate latent topics of docu-ments [Wang and Blei, 2011] and learn human habits in dailylives [Yuan et al., 2013]. Gaussian mixture models are oftenused to describe the joint effect of multiple segments and fac-tors [Bilmes and others, 1998]. It is widely utilised on clusterproblems because of its unconstrained covariance structureand flexible application scenarios.

3 Disease Rate Evolution Prediction3.1 Problem DefinitionHuman mobility records, e.g., the check-ins, reflect people’smovements in the physical world and to some extent revealtheir lifestyles, which gradually affect their health conditions.Here we utilise the check-in dataset of Foursquare and thechronic disease dataset in London as an example for analysis.The Foursquare dataset contains check-in records from Dec.2010 to Dec. 2013 created at 18,018 POI venues in 426 cat-egories, e.g., fast food restaurant, gym, park, etc. There areover 4 million check-in transition between pairs of POIs, ineach of which we have check-in timestamp and venue id (nouser information). The chronic disease dataset contains themorbidity of 20 chronic diseases of 567 wards in London.

To exam the relationship between chronic diseases and hu-man mobility patterns extracted from location-based services,we employ Pearson correlation analysis. More specifically,the correlation results between evolution rate of 7 most com-mon chronic diseases and check-in amount of 17 categories ofPOIs from 2010 to 2013 in London are tested and presentedin Figure 1(a). Here, for each chronic disease, we sort the dis-ease evolution rates of 567 wards and split the ranking list intor segments averagely (we set r = 19, so there are 30 wards ineach of the first 18 segments and 27 wards in the last one). Wecalculate the mean value of disease evolution rate and meancheck-in volume of each category of POI in every segmentto get two r-length sequences. Then, through calculating thecorrelation coeficient between the two types of sequences, wefind that several categories, such as Malaysian restaurants,Chinese restaurants, and fast food restaurants, have high pos-itive correlations with most of the 7 diseases except cancer.Some diseases have similar correlations with all the 17 cate-gories of POIs, e.g. hypertension, heart failure, and obesity.

There are many confounding factors influencing popu-lation health across geographies. Our goal is not to drawcausal conclusions regarding health. Instead we mine geo-referenced data emerging in urban environments so as to in-form prediction models and attain better prediction results.Urban activities of users engaging with location intelligencesystems can be indicative of lifestyle choices, some of whichcould be linked to health, yet describing a causal link is notthe purpose of this paper.

People’s health conditions can be treated as dynamic fac-tors. A healthy lifestyle may not prevent illness but may re-


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(a) (b) (c)

Figure 1: (a) Correlation between the evolution rate of 7 chronic diseases and check-in amount of 17 POI categories. (b)Distribution of the amount of POIs in the 630 wards of London. (c) 3-D projection of embedding results for POI categories.

duce the risk. Therefore, we focus on how people’s lifestylesmay influence the development of these chronic diseases. As-sume there are W regions in an area, represented as R =r1, r2, ..., rW . Let D = d1, d2, ..., dS denote S chronicdiseases and H∈RW×S denote the evolution matrix of all theS diseases in W regions , where hw,s is the evolution rate ofdisease ds from last year to this year in region rw (a positivenumber denotes an increasing rate and a negative number de-notes an decreasing rate). In practical terms, the disease datafor some regions might be unavailable due to a tight budget.If we can only collect data in a subset R regions of R, whichfill in W rows in the region-disease matrix H and let oth-ers be zero (we denote H matrix with zero rows by H), ourgoal is to predict the chronic disease development of the restW = W − W regions in set R.

3.2 OverviewWe adapt a prediction method which systematically integratescollaborative topic modeling and Gaussian mixture approach.Specifically, we firstly leverage an embedding method and aGaussian mixture approach to aggregate categories of venuesinto several clusters according to their check-in patterns.Through that, we obtain a denser region-cluster-of-venuesmatrix. Then we apply a collaborative topic modeling methodto extract lifestyle patterns of each region from human’scheck-in mobility. We hypothesise that lifestyle patterns inall regions and chronic disease evolution rates in some of theregions can be alble to help exploring the chronic disease con-ditions in the missing regions.

3.3 MethodVenue Aggregation. Assuming there are N categories ofvenues, denoted as V = v1, v2, ...vN, we can collect thecheck-in amount of each category in V and each region in R(mentioned in Section 3.1), and build a region-category ma-trix Y∈RW×N . However, the spatial distribution of POIs isusually unbalanced in the city. In Figure 1(b), the amountsof Foursquare POIs in the 630 wards are presented. We canclearly see that central London and the region containingHeathrow Airport (the deep blue region on the left) havedenser POI distribution. However, in some other regions theamount of POIs are considerably sparse, which leads to thesparsity of matrix Y. To address this problem, we extractusers’ similar check-in preferences for some categories of

POIs and aggregate these categories into a cluster. For exam-ple, if users often go shopping after having French or Italianfood, we will cluster French and Italian food into a group.

To aggregate venues based on check-in patterns, we firstlyrepresent each category of POI as a feature vector. Those cat-egories with similar check-in patterns will have smaller vec-tor distances from each other. This is similar to the embed-ding task in natural language processing, where each cate-gory of POI can be seen as a word, and users’ transitions be-tween categories of POIs is analogous to a sentence. Here weembed each category of POI into a P -length vector throughword2vec method [Rehurek and Sojka, 2010]. Figure 1(c)shows a part of the 3-D projection1 of embedding result for allthe 426 categories of POIs when P is set to 100. Each circledenotes one category. The colored circles are the categorieshaving shortest cosine distances to the category “women’sstores”. Redder colors represent closer relationships. The sizeof a circle indicates it’s “depth” from the surface of screen(foreshortening effects). We can observe that shoe stores,clothing stores, lingerie stores, and men’s stores, have themost similar check-in patterns with women’s stores.

We aggregate the N categories of POIs into C clusters byadopting Gaussian mixture method (GMM) [Friedman andRussell, 1997]. Compared with other cluster methods, e.g.,k-means, GMM provides unconstrained covariance structurefor each cluster, making the method more flexible. As illus-trated in Figure 2, φ∈RN×P is the venue-embedding matrix.The GMM can be described as follows:

πn ∼ Dirichlet(γ),

σc ∼ Γ(τ, σ0), µc ∼ N (µ0, νσc), c = 1, ..., C,

gn ∼ Categorical(πn), φn ∼ N (µgn ,σgn), n = 1, ..., N,

where πn and gn are the parameter of categorical distributionand the component of the nth observation respectively. µc, σc

are the parameters of Gaussian distribution of component c.γ, τ, σ0, µ0, ν are the shared hyperparameters. Let X∈RW×C

represent the region-cluster matrix, which can be estimatedthrough parameter π and check-in matrix Y:

X = Y · π, (1)

where xi,j denotes the check-in amount of venue cluster j inregion ri. Figure 2 presents the association from Y and π tomatrix X through dotted lines.

1http://projector.tensorflow.org/


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Collaborative Topic Model. Until now, we have ob-tained the region-cluster matrix X. Our objective is to ex-tract lifestyle information from X helping to predict chronicdisease conditions in missing regions. Traditional probabilis-tic matrix factorization (PMF) is a perfect method for rec-ommendation tasks, leveraging the similarity among dif-ferent users and items to complement the user-item ma-trix [Salakhutdinov and Mnih, 2007]. Similarly, as pre-sented in Figure 1(a), similarities exit among diseases whenanalysing the correlation between diseases and check-ins. Ifwe assume that similarities could also be found among re-gions, we can in the same way factorize regions’ disease evo-lution rate matrix H into two low dimensional latent matri-ces L and Λ, both with dimension K. We denote K as thenumber of latent lifestyles, vector Li as the weight of latentlifestyles in region ri, and vector Λj as the influence fromlatent lifestyles on chronic disease dj . Thus we can generateH through the distribution:

hi,j ∼ N (Li ·Λ>j , ςi,jλ2H), (2)

where ςi,j is 0 if the data of hi,j is missing, and 1 otherwise.The distribution of region and disease vectors are:

Li ∼ N (0, λ2LIK), Λj ∼ N (0, λ2

ΛIK),

where IK is aK-dimensional identity matrix. A common wayto optimize parameters L and Λ is to minimize the squared-errors objective function with regularization terms:

Ω = I ‖H− LΛ>‖2F +λ2H

λ2L

‖L‖2F +λ2H

λ2Λ

‖Λ‖2F , (3)

where ‖ · ‖F denotes the Frobenius norm and denotes theHadamard product operator [Kolda and Bader, 2009].

However, as only the data of R regions are avaliable, in-formation for leveraging similarities among the missing rowsand the existing rows in matrix H is lacking. Hence we usethe region-cluster matrix X obtained in the last section. Weassume each region is characterized by a particular set oflifestyles, and each cluster of categories of POIs may re-flect human’s various lifestyles in different probabilities. Itis similar to a topic structure: if we regard all the regionsas documents, the check-in patterns of various POI clustersin a region can be considered as the words in a document.In analogy with the assumption that each topic is describedby several representative words, each lifestyle is reflected indifferent check-in patterns. Intuitively, a typical topic model,LDA [Blei et al., 2003], can be applied here to model thelifestyles in different regions.

However, LDA does not tackle the main problem in thiswork: how to leverage the lifestyles extracted from check-in mobility to fill the missing parts in matrix H. We adoptthe collaborative topic model (CTM) proposed in [Wangand Blei, 2011] here to combine probabilistic matrix fac-torization and topic modeling. Different from the assump-tion in [Wang and Blei, 2011], that offsets exist betweenthe document-topic factor factorized from document-user partand document-word part respectively, we propose that peo-ple’s general lifestyles, reflecting in check-in patterns andhealth conditions, are relatively consistent. POIs’ visiting andchronic disease condition are just two views of lifestyles,

X

𝜦

𝛽T

ZL C

W

S

𝜇0, 𝜐 𝜏,𝜎0

C

H

𝝁𝑐 𝝈𝑐

GN

𝛾 𝜋Y

𝜆𝐿

𝜆𝛬

𝜆𝐻

ϕ

known dataunknown (latent) data

A

A

A parameter

Figure 2: Illustration of CTM+GMM method.

asthma chronic obstructive heart failureatrial pulmonary learning

fibrillation (AF) disease (COPD) disabilities (LD)cancer diabetes mental health

chronic kidney epilepsy obesitydisease (CKD) hypertension palliative carecoronary heart hypothyroidism smokingdisease (CHD) heart failure stroke or transient

dementia due to LVD ischaemic attacksdepression (HFLVD) (stroke or TIA)

Table 1: 20 chronic diseases and their abbreviations.

where the former one is the perspective in people’s daily lifeactivities, and the latter one is how these lifestyles influencepeople’s health status. Therefore, we leverage the same factorL, to represent region-lifestyle interactions in topic modeling.

Specifically, the generative process of the hybrid method isas follows (illustrated in the bottom part of Figure 2):1. For each chronic disease j, draw disease latent factor

Λj ∼ N (0, λ2ΛI).2. For each region i:

(a) Draw lifestyle factor Li ∼ Dirichlet(λL).(b) For each category of venues xi,c:

(i) Choose lifestyle assignment zi,c ∼Mult(Li).(ii) Choose a category of venues xi,c ∼Mult(βzi,c ).

3. For each region-disease pair hi,j , draw hi,j ∼ N (Li ·Λ>j , ii,jλ

2H),

where βzt is the distribution of POI clusters in lifestyle zt.Optimization. We apply EM method [Bilmes and others,

1998] to estimate the parameters µc,σ2c(c = 1, ..., C) and

π in GMM part. For the CTM part, we need to estimate theparameters L,Λ, where factor L is employed both in PMFand topic modeling. We iteratively optimize parameters bytwo steps. Firstly, for topic modeling, we estimate L throughEM method, which is a typical optimization method for topicmodeling. Then in the second step, we apply Gradient De-scent method to estimate L,Λ (we use the L factor in thefirst step as the initialization here). We then go back to step 1and set L as the prior of region lifestyle distribution.

4 Evaluation4.1 Set-UpData. We use three datasets in our experiment:


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Cluster 1 Cluster 2gardens, Russian r, malls, hotels, jazz clubs, skate parks,office supplies stores, banks, tattoo parlors, nightlife spots,art galleries, music stores, Latin American r, social clubs,bowling alleys, theme park, cupcake shops, smoke shops,dessert shops, comedy clubs, beer stores, laundry services,Taiwanese r, college libraries, dinners, modern European r,bookstores, souvenir shops, convenience stores, bridgessnack places, cosmetics shops

Table 2: Categories of POIs in 2 clusters (r: restaurants).

Foursquare dataset: The check-in records from Dec. 2010to Dec. 2013 and the POI information in London. The detailsof Foursquare dataset are introduced in Section 3.1. In August2015, Foursquare had more than 50 million active users andmore than 10 billion check-ins already. The dataset used inthis research is shared directly by Foursquare under a researchcontract agreement.

Boundary-line dataset of London: The dataset is collectedfrom UK government websites2, which contain the shapefilesof ward-level (electoral districts at sub-national level) bound-ary lines in London. In total, there are 630 wards. This spatialdata contains the shape line, name, and id of each ward (shapelines are shown in Figure 1(b)).

Disease dataset: We collect the data from a governmentopen data website of UK3. They publish the population, theannual morbidity (patients/population in an area) of 19 preva-lence diseases (Table1), and the utilization rate of “palliativecare” from year 2005 to 2015. Since we consider palliativecare 4 as an indicator for the morbidity of malignant dis-eases (it is a specialized medical care for people with life-threatening illness), we call it “disease” here. The data foreach year is from Apr. of one year to Mar. of the next year.For convenience, we use dataset “2013”, for example, to rep-resent the annual dataset from Apr. 2013 to Mar. 2014 in therest of the paper. We collected the data from 2009 to 2013,consistent with the period of check-in data. The dataset con-tains the popularity and morbidity data of 567 wards in Lon-don (no data for the rest 63 wards).

Metrics. We apply two metrics here to evaluate the predic-tion performance: (MSE) and R2 score:

MSE(H, H′)R =

1

W · S

∑ri∈R

S∑j=1

(hri,j− h

′ri,j

)2, (4)

R2(H, H

′)R =

1

W

∑ri∈R

(1−

∑Sj=1(hri,j

− h′ri,j

)2∑Sj=1(hri,j

− hri)2

), (5)

where h′

ri,jis the prediction result of item hri,j and hri =

1S

∑Sj=1 hri,j . W is the amount of regions in testing set. R2

score reflects how well the model performs in the prediction,considering the error and the mean of true values simultane-ously. Here lower MSE and higher R2 represent better result.

Baselines. We compare the hybrid method with 4 methods:

2https://www.ordnancesurvey.co.uk/opendatadownload/products.html#BDLINE

3https://data.gov.uk/dataset/quality and outcomes framework achievement prevalence and exceptions data

4https://en.wikipedia.org/wiki/Palliative care

0

0.02

0.04

0.06

0.08

70 80

MSE

Training Size (%)

BRSVRPMF

CTMCTM+GMM

(a) MSE

0

0.1

0.2

0.3

70 80

R2

Training Size (%)

BRSVRPMF

CTMCTM+GMM

(b) R2

Figure 3: Prediction performance of all the evaluated methodson (a) MSE and (b) R2, when training size is 70% or 80%.

• Regression: We apply two regression methods using his-tory data solely: boosting regression (BR) and support vec-tor regression (SVR). They utilise the data of chronic dis-ease evolution rate (2009 - 2012) to predict the evolutionrate from 2012 to 2013.• Probabilistic matrix factorization (PMF): In PMF, the

region-disease matrix H (with missing rows) is decom-posed through equation 3 (H is replaced with H).• Collaborative topic modeling (CTM): We compare our

method with CTM to emphasize the improvementachieved through venue aggregation.

4.2 ResultsWe compare the prediction performance of our methodwith baseline models in this section. Here we embed eachPOI category into a 100-length vector, and produce matrixφ∈R426×100. Each vector represents POI category’s featuresin check-in pattern space, where a shorter distance betweentwo categories indicates more similar check-in patterns. Weaggregate the 426 embedded vectors into C clusters througha GMM method. Table 2 shows POI categories in two typ-ical clusters when C=20. We can see that most POIs in thefirst cluster are more about nature (parks, gardens, etc.), art(art galleries, music stores, comedy clubs, etc), and reading(libraries and bookstores), while the second cluster mainlyincludes nightlife-related venues. The clustering results pro-vide us new perspectives on London citizens’ visitation toPOIs, which are not totally consistent with our experience.For instance, it is unexpected that bridges have similar check-in patterns with some clubs and nightlife spots. This may bebecause that the fantastic lighting systems of some famousbridges in London, such as Albert Bridge and Tower Bridge,attract a large number of viewers during the night hours.

Next, we predict the missing rows in matrix H, in whichitem hi,j is the evolution rate of disease dj in ward rifrom year 2012 to 2013. hi,j is generated from the equationhi,j = (m2013

i,j − m2012i,j )/m2012

i,j , where m represents mor-bidity. Firstly, we obtain the region-cluster matrix X throughEquation 1. Then, the latent lifestyle factor L and latentchronic disease factor Λ are estimated through CTM in Sec-tion 3.3. Finally, we predict the missing regions R in matrixH through Equation 2. We randomly select 70% and 80%rows in matrix H as training data and evaluate the predic-tion performance in the rest rows. Due to the space limit, forall the methods, we tune and select the parameters throughtraining set and show the prediction results on testing set. For


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Disease ER R2 S Disease ER R2 Sasthma -0.03 0.155 hypertension 0.07 0.156AF 3.17 0.166 hypothyroidism 2.94 0.129cancer 7.12 0.150 heart failure 0.59 0.160CKD -2.12 0.157 HFLVD -44.27 0.150CHD -1.17 0.159 LD 4.36 0.175COPD 2.94 0.166 mental health 2.37 0.170dementia 11.13 0.163 obesity -16.10 0.133depression 12.15 0.144 palliative care 21.92 0.140diabetes 3.10 0.141 smoking 1.18 0.181epilepsy 2.02 0.168 stroke or TIA 1.11 0.142

Table 3: Average evolution rates of the 20 diseases and theirR2 scores in prediction (ER: evolution rate (%), S: score).

each method, we run the analysis 20 times and show the av-erage results in Figure 3. The latent dimension length K andthe amount of lifestyles T (CTM part) are set to 50. C, theamount of component (GMM part), is set to 20. It is obvi-ous that CTM+GMM has the best performance on the twometrics. The two regression methods have the highest MSEresults and lowest R2 scores. We can infer that the historicevolutions of these chronic diseases cannot provide enoughregular patterns (e.g. linear or periodic patterns) for futureprediction. Compared with traditional PMF, CTM has an av-erage improvement of 13% in MSE and 59% in R2 score.Moreover, when we add the GMM part to CTM, it furtherlyachieves a 7.6% improvement in MSE and a 3.7% improve-ment in R2 score.

We show the average evolution rate and theR2 scores of allthe diseases in predictions through CTM+GMM method from2012 to 2013 in Table 3 (different from the definition in Sec-tion 4.1, we here show theR2 scores along each disease). ThePearson correlation coefficient of evolution rate list and R2

list in the table is 0.11, which means that there is no obviouscorrelation between these two factors: the predictability of adisease is neither positively nor negatively related to its evo-lution rate. From this table, we can see a significant growth ofpalliative care during that year, indicating that the morbidityof malignant diseases, e.g., cancer, had increased. Also, themorbidity of the psychiatric and mental diseases, e.g. depres-sion, dementia, and mental health, increased dramatically inthat period. Actually, as illustrated in [Muliyala and Varghese,2010], depression and dementia have complex relationships:depression has been both a risk factor and a prodrome of de-mentia, which is also reflected in the close evolution ratesbetween them. Good news is that the obesity cases in Londondecreased in the same period, which has been a serious healthconcern in UK for a long time. Moreover, we observe thatobesity has a relatively low R2 score in the experiment forboth our method and baselines. This may be due to the factthat different from some incurable chronic diseases, e.g., dia-betes [Etuk and others, 2010], of which the morbidity is morestable, people may lose weight through various approaches,making the evolution of obesity more difficult to predict.

5 Lifestyle and Chronic DiseasesWe present the correlation between the 20 chronic diseasesand various lifestyles. We leverage the check-in pattern asa projection of lifestyle. Specifically, we use the lifestyle-cluster factor (β in Section 3.3), and the disease-lifestyle fac-

Check-in Lifestyles Correlated Disgovernment buildings, pubs, sushi r, plazas, heart failure,candy stores, steakhouses, burger joints, attacks, asthma,Subways, Chinese r, churches, bookstores, COPD,fast food r, coffee shops, nightclubs coronary heartIndian r, convention centers, fast food r, disease, diabetes,sandwich places, grocery stores, Thai r, hypertension,dim sum r, hotel bars, bakeries, hostels, obesity, HFLVD,nightclubs, fried chicken joints stroke or TIAdessert shops, organic groceries, gay bars, cancer, smokingmiddle eastern r, sports bars, cocktail bars, chronic kidney,whisky bars, nightclubs, offices, boutiques, depression,Chinese r, hotel bars, hotels, Italian r disease, obesity,

Table 4: Typical check-in lifestyles and the highly-correlateddiseases (Dis: Diseases, r: restaurants).

tor Λ, to uncover the hidden relationship between chronicdiseases and POIs. Some of the observed correlations areconsistent with the research findings in clinical medicine andphysiology, while others provide new insights into some openproblems. We list 3 typical check-in lifestyles and their highlycorrelated diseases in Table 4.

The top two lifestyles are dominated by fast food venues:fast food restaurants, pizza places, fried chicken joints,and Asian food (Chinese and sushi restaurants). These twolifestyles show association with 10 chronic diseases listed onthe right. This is coherent with the statement that some dis-eases, like heart-related diseases, hypertension, obesity, anddiabetes, are correlated with diets high in sugar and fat. Ad-ditionally, asthma and chronic obstructive pulmonary diseaseare also highly correlated with these two lifestyles. This isconsistent with previous studies in physiology [Rosenkranzet al., 2010] claiming that a high-fat diet may contribute tochronic inflammatory diseases of airways and lungs.

The third lifestyle is alcohol-oriented, where 6 of the 14categories of POIs are bars or clubs. The induction of alcoholon some diseases, like cancer, depression, have been provedin previous studies [Martınez, 2005]. However, as illustratedin [White et al., 2009], evidence of an association betweenalcohol consumption and chronic kidney disease is conflict-ing. Here we draw inspiration from large-scale human mobil-ity data and provide an intuitive perspective on the positivecorrelation between lifestyles related to alcohol and chronickidney disease.

6 ConclusionIn this paper, we leverage human mobility data to study theevolution of human health conditions. We embed the POIsinto vectors according to human transition patterns to capturetheir semantic meanings. Then we combine Gaussian mixturemethods with collaborative topic modeling to predict healthconditions, which is able to deal with data sparsity and extracthuman lifestyles from check-in patterns. Experiments usingreal-world datasets indicated that CTM+GMM has a signifi-cant improvement on prediction tasks compared to baselines.

AcknowledgmentsWe would like to thank Foursquare for making the data avail-able to the University of Cambridge for this study.


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