How are you feeling?: A personalized methodology for predicting mental states from temporally observable physical and behavioral information

[1]  Andrew D. Back,et al.  Radial Basis Functions , 2001 .

[2]  Prasenjit Mitra,et al.  AlgorithmSeer: A System for Extracting and Searching for Algorithms in Scholarly Big Data , 2016, IEEE Transactions on Big Data.

[3]  Pouya Bashivan,et al.  Mental State Recognition via Wearable EEG , 2016, ArXiv.

[4]  Naomi S. Altman,et al.  Points of Significance: Simple linear regression , 2015, Nature Methods.

[5]  Marcel Salathé,et al.  Modeling Individual-Level Infection Dynamics Using Social Network Information , 2015, CIKM.

[6]  Inbal Nahum-Shani,et al.  Visualization of time-series sensor data to inform the design of just-in-time adaptive stress interventions , 2015, UbiComp.

[7]  Emre Ertin,et al.  cStress: towards a gold standard for continuous stress assessment in the mobile environment , 2015, UbiComp.

[8]  Xia Zhou,et al.  SmartGPA: how smartphones can assess and predict academic performance of college students , 2015, GETMBL.

[9]  Conrad S. Tucker,et al.  Quantifying Product Favorability and Extracting Notable Product Features Using Large Scale Social Media Data , 2015, J. Comput. Inf. Sci. Eng..

[10]  Conrad S. Tucker,et al.  A Product Feature Inference Model for Mining Implicit Customer Preferences Within Large Scale Social Media Networks , 2015 .

[11]  Konrad Paul Kording,et al.  Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study , 2015, Journal of medical Internet research.

[12]  Conrad S. Tucker,et al.  Automated Discovery of Lead Users and Latent Product Features by Mining Large Scale Social Media Networks , 2015 .

[13]  Syed Monowar Hossain,et al.  Continuous in-the-field measurement of heart rate: Correlates of drug use, craving, stress, and mood in polydrug users. , 2015, Drug and alcohol dependence.

[14]  Yuval Shahar,et al.  Classification-driven temporal discretization of multivariate time series , 2014, Data Mining and Knowledge Discovery.

[15]  Meerae Lim,et al.  Reasons for desiring death: examining causative factors of suicide attempters treated in emergency rooms in Korea. , 2014, Journal of affective disorders.

[16]  Fanglin Chen,et al.  StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones , 2014, UbiComp.

[17]  Conrad S. Tucker,et al.  Discovering Next Generation Product Innovations by Identifying Lead User Preferences Expressed Through Large Scale Social Media Data , 2014 .

[18]  Fanglin Chen,et al.  My smartphone knows i am hungry , 2014, WPA '14.

[19]  Marcel Salathé,et al.  An ensemble heterogeneous classification methodology for discovering health-related knowledge in social media messages , 2014, J. Biomed. Informatics.

[20]  Michael J. Roche,et al.  Examining the Interplay of Processes Across Multiple Time-Scales: Illustration With the Intraindividual Study of Affect, Health, and Interpersonal Behavior (iSAHIB) , 2014, Research in human development.

[21]  Ruey S. Tsay,et al.  Multivariate Time Series Analysis: With R and Financial Applications , 2013 .

[22]  Marcel Salathé,et al.  Discovering health-related knowledge in social media using ensembles of heterogeneous features , 2013, CIKM.

[23]  C. Lee Giles,et al.  Automatic Detection of Pseudocodes in Scholarly Documents Using Machine Learning , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[24]  Conrad S. Tucker Fad or Here to Stay: Predicting Product Market Adoption and Longevity Using Large Scale, Social Media Data DETC2013-12661 , 2013 .

[25]  R. Houtman,et al.  Understanding stress-effects in the brain via transcriptional signal transduction pathways , 2013, Neuroscience.

[26]  Eric Horvitz,et al.  Predicting Depression via Social Media , 2013, ICWSM.

[27]  Martin Binder,et al.  The Structure of Subjective Well‐Being: A Vector Autoregressive Approach , 2013 .

[28]  Gianluca Bontempi,et al.  Machine Learning Strategies for Time Series Forecasting , 2012, eBISS.

[29]  Martin Binder,et al.  The structure of happiness: A vector autoregressive approach , 2011 .

[30]  Emre Ertin,et al.  Continuous inference of psychological stress from sensory measurements collected in the natural environment , 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.

[31]  Alex Coad,et al.  An Examination of the Dynamics of Well-Being and Life Events using Vector Autoregressions , 2010 .

[32]  F. Collins,et al.  The path to personalized medicine. , 2010, The New England journal of medicine.

[33]  M. Eichler,et al.  A graphical vector autoregressive modelling approach to the analysis of electronic diary data , 2010, BMC medical research methodology.

[34]  D. Gerstorf,et al.  Time-structured and net intraindividual variability: tools for examining the development of dynamic characteristics and processes. , 2009, Psychology and aging.

[35]  M. Pinquart,et al.  Change of Leisure Satisfaction in the Transition to Retirement: A Latent-Class Analysis , 2009 .

[36]  M. Conner,et al.  Exploring the Benefits of Conscientiousness: An Investigation of the Role of Daily Stressors and Health Behaviors , 2009, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.

[37]  Peter J. Haug,et al.  A multivariate time series approach to modeling and forecasting demand in the emergency department , 2009, J. Biomed. Informatics.

[38]  P. Molenaar,et al.  Analyzing developmental processes on an individual level using nonstationary time series modeling. , 2009, Developmental psychology.

[39]  Jonathan E. Butner,et al.  Collaborative coping and daily mood in couples dealing with prostate cancer. , 2008, Psychology and aging.

[40]  Elizabeth Sheedy Why VAR Models Fail and What Can Be Done , 2008 .

[41]  Rhonira Latif,et al.  Classification of Elbow Electormyography Signals based on Directed Transfer Functions , 2008, 2008 International Conference on BioMedical Engineering and Informatics.

[42]  Li Xin-ran A Vector Autoregression Model of Hourly Wind Speed and Its Applications in Hourly Wind Speed Forecasting , 2008 .

[43]  Foster J. Provost,et al.  Handling Missing Values when Applying Classification Models , 2007, J. Mach. Learn. Res..

[44]  S. Kotsiantis Supervised Machine Learning: A Review of Classification Techniques , 2007, Informatica.

[45]  I. van Mechelen,et al.  Individual differences in core affect variability and their relationship to personality and psychological adjustment. , 2007, Emotion.

[46]  Jessica L. Tracy,et al.  PERSONALITY PROCESSES AND INDIVIDUAL DIFFERENCES The Psychological Structure of Pride : A Tale of Two Facets , 2007 .

[47]  S. Stevenson,et al.  Forecasting Housing Supply: Empirical Evidence from the Irish Market , 2007 .

[48]  O. John,et al.  Measuring personality in one minute or less: A 10-item short version of the Big Five Inventory in English and German , 2007 .

[49]  Rob J Hyndman,et al.  25 YEARS OF IIF TIME SERIES FORECASTING , 2006 .

[50]  Rob J Hyndman,et al.  25 years of time series forecasting , 2006 .

[51]  D. Zuroff,et al.  Assessing interpersonal perceptions using the interpersonal grid. , 2005, Psychological assessment.

[52]  R. Benedict,et al.  Reliable screening for neuropsychological impairment in multiple sclerosis , 2004, Multiple sclerosis.

[53]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[54]  Diane J. Litman,et al.  Predicting Student Emotions in Computer-Human Tutoring Dialogues , 2004, ACL.

[55]  Michelle Grevatt,et al.  Violence, mental disorder and risk assessment: can structured clinical assessments predict the short-term risk of inpatient violence? , 2004 .

[56]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[57]  Andrew W. Moore,et al.  Locally Weighted Learning for Control , 1997, Artificial Intelligence Review.

[58]  Andrew W. Moore,et al.  Locally Weighted Learning , 1997, Artificial Intelligence Review.

[59]  D. Kibler,et al.  Instance-based learning algorithms , 2004, Machine Learning.

[60]  O. John,et al.  Individual differences in two emotion regulation processes: implications for affect, relationships, and well-being. , 2003, Journal of personality and social psychology.

[61]  H. Stuart Violence and mental illness: an overview. , 2003, World psychiatry : official journal of the World Psychiatric Association.

[62]  A. Yung,et al.  Mapping the onset of psychosis: The comprehensive assessment of at risk mental states (CAARMS) , 2003, Schizophrenia Research.

[63]  R. Benedict,et al.  Screening for multiple sclerosis cognitive impairment using a self-administered 15-item questionnaire , 2003, Multiple sclerosis.

[64]  Bernhard Pfahringer,et al.  Locally Weighted Naive Bayes , 2002, UAI.

[65]  Jiahui Wang,et al.  Vector Autoregressive Models for Multivariate Time Series , 2003 .

[66]  W. Stephenson Simple Linear Regression , 2003 .

[67]  Eric R. Ziegel,et al.  Analysis of Financial Time Series , 2002, Technometrics.

[68]  F. Deane,et al.  Emotional intelligence moderates the relationship between stress and mental health , 2002 .

[69]  R. Leibbrand,et al.  Assessment of functional gastrointestinal disorders using the gastro-questionnaire , 2002, International journal of behavioral medicine.

[70]  Jo-Ann Tsang,et al.  The grateful disposition: a conceptual and empirical topography. , 2002, Journal of personality and social psychology.

[71]  V. Pentikäinen,et al.  TERVA: system for long-term monitoring of wellness at home. , 2001, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[72]  K. Silverman,et al.  A reinforcement-based therapeutic workplace for the treatment of drug abuse: six-month abstinence outcomes. , 2001, Experimental and clinical psychopharmacology.

[73]  S. Sathiya Keerthi,et al.  Improvements to the SMO algorithm for SVM regression , 2000, IEEE Trans. Neural Networks Learn. Syst..

[74]  S. Ramsay Violence , 2000, The Lancet.

[75]  Holger Kantz,et al.  Practical implementation of nonlinear time series methods: The TISEAN package. , 1998, Chaos.

[76]  C. Sims,et al.  Vector Autoregressions , 1999 .

[77]  Suzanne L. Weaver,et al.  Sociodemographic variations in the sense of control by domain: findings from the MacArthur studies of midlife. , 1998, Psychology and aging.

[78]  I. Korhonen,et al.  TERVA: wellness monitoring system , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

[79]  Suzanne L. Weaver,et al.  The sense of control as a moderator of social class differences in health and well-being. , 1998, Journal of personality and social psychology.

[80]  D. Roth,et al.  Predicting longitudinal changes in caregiver physical and mental health: a stress process model. , 1998, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[81]  M. Vasquez Latinos and violence: mental health implications and strategies for clinicians. , 1998, Cultural diversity and mental health.

[82]  J. Berkhof,et al.  Effects of stressful daily events on mood states: relationship to global perceived stress. , 1998, Journal of personality and social psychology.

[83]  John P. Robinson,et al.  Time for Life: The Surprising Ways Americans Use Their Time , 1998 .

[84]  Karl J. Friston,et al.  Human Brain Function , 1997 .

[85]  Luís Torgo,et al.  Regression by Classification , 1996, SBIA.

[86]  Ian H. Witten,et al.  Induction of model trees for predicting continuous classes , 1996 .

[87]  Carl E. Rasmussen,et al.  In Advances in Neural Information Processing Systems , 2011 .

[88]  M. Bradley,et al.  Measuring emotion: the Self-Assessment Manikin and the Semantic Differential. , 1994, Journal of behavior therapy and experimental psychiatry.

[89]  C. Sherbourne,et al.  The MOS 36-item Short-Form Health Survey (SF-36): III. Tests of data quality, scaling assumptions, and reliability across diverse patient groups. , 1994 .

[90]  David Watson,et al.  The PANAS-X manual for the positive and negative affect schedule , 1994 .

[91]  Jeffrey T. Mitchell,et al.  Evaluation of psychological debriefings , 1993 .

[92]  C. McHorney,et al.  The MOS 36‐Item Short‐Form Health Survey (SF‐36): II. Psychometric and Clinical Tests of Validity in Measuring Physical and Mental Health Constructs , 1993, Medical care.

[93]  K. Hsu Time series analysis of the interdependence among air pollutants , 1992 .

[94]  C. Sherbourne,et al.  The MOS 36-Item Short-Form Health Survey (SF-36) , 1992 .

[95]  J. R. Quinlan Learning With Continuous Classes , 1992 .

[96]  T. Heatherton,et al.  Development and validation of a scale for measuring state self-esteem. , 1991 .

[97]  M. Rowland,et al.  Self-reported weight and height. , 1990, The American journal of clinical nutrition.

[98]  R. Darlington,et al.  Regression and Linear Models , 1990 .

[99]  Daniel J Buysse,et al.  The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research , 1989, Psychiatry Research.

[100]  M. S. Kaylen,et al.  Vector Autoregression Forecasting Models: Recent Developments Applied to the U.S. Hog Market , 1988 .

[101]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[102]  I. Jolliffe Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.

[103]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[104]  R. Larsen,et al.  The Satisfaction with Life Scale , 1985, Journal of personality assessment.

[105]  R. Shephard,et al.  A simple method to assess exercise behavior in the community. , 1969, Canadian journal of applied sport sciences. Journal canadien des sciences appliquees au sport.

[106]  T. Kamarck,et al.  A global measure of perceived stress. , 1983, Journal of health and social behavior.

[107]  J. Douglas Faires,et al.  Numerical Analysis , 1981 .

[108]  R. Fauchet,et al.  Serum ferritin as a possible marker of the hemochromatosis allele. , 1979, The New England journal of medicine.

[109]  James H. Johnson,et al.  Assessing the impact of life changes: development of the Life Experiences Survey. , 1978, Journal of consulting and clinical psychology.

[110]  L. Radloff The CES-D Scale , 1977 .

[111]  T. H. Holmes,et al.  The Social Readjustment Rating Scale. , 1967, Journal of psychosomatic research.

[112]  J. Miller Numerical Analysis , 1966, Nature.

[113]  The mind of the murderer. , 1957 .