Sensing, Inference, and Intervention in Support of Mental Health

Sensing, Inference, and Intervention in Support of Mental Health Eric Horvitz Microsoft Research CHI Workshop on Computing in Mental Health May 2016

HCI + AI: Growth in Resources & Competencies Data & computation Learning, inference, representation Causal inference * Perception LV FAIL ANAPHYLAXIS PULMONARY EMBOLUS NLP HYPOVOL ANESTHESIA INSUFFICIENT PAP SHUNT INTUBATION KINKED TUBE DISCONNECTION LVED VOLUME HISTORY LV FAILURE STROKE VOLUME CATECHOLAMINE VENT ALV VENT MACHINE CVP PCWP TPR CARDIAC OUTPUT SAO2 VENT LUNG VENT TUBE BLOOD PRESSURE HEART RATE PA SAT MV SETTING ERROR LOW OUTPUT ERROR CAUTER FIO2 PRESSURE MINUTE VENTILATION HR BP HR EKG HR SAT ARTERIAL CO2 EXPIRED CO2

Data to Predictions to Interventions Sensed data Predictive model Decision model Predictions Actions

Data to Predictions to Interventions Sensed data Active learning Experience sampling Ideal experimentation Predictive model Decision model Predictions Actions

Revolution Brewing Computational social science Data Devices Life-centricity of web Representations & models Learning & inference Perception, predictions, decisions

Revolutions Brewing Computational social science Public health & epidemiology Data Devices Life-centricity of web Representations & models Learning & inference Perception, predictions, decisions

Revolutions Brewing Computational social science Public health & epidemiology Social psychology & personality Clinical psychology & psychiatry Data Devices Life-centricity of web Representations & models Learning & inference Perception, predictions, decisions

Revolution Brewing Mental health & wellbeing Data Devices Life-centricity of web Representations & models Learning & inference Perception, predictions, decisions

Evidential Streams & Inferences from Populations e.g., Search, Twitter, Facebook, Reddit, TalkLife, Crisis Txt Line, Apps Health insights & diagnosis Mental health & wellness

Wrestling with (the Wild West of) Population Data Supervised learning: experts, crowd, participants Unsupervised learning: clustering, topic modeling Causal modeling: propensity, Neyman-Rubin

Wrestling with (the Wild West of) Population Data Sensitivity, robustness, error modeling Statistics of rare events Sequence alignment NL psych models: LIWC, ANEW, etc. NL topics/sentiment: Emolex, SentiWordNet, Empath

Wrestling with Large-Scale Population Data Experimental design Matched sets for studies - Control - Test/intervention Example: Rare serious adverse effects of medications

Wrestling with Large-Scale Population Data Detect rare adverse effects of drugs Self-controlled study Query rate ratio γ D First D Last C S C S C S C S C D First θ β α D D α D β γ S S D First + θ Query Timeline D: query for drug of interest C: query for condition of interest S: query for symptom of C ignored surveillance window post D First surveillance window pre D First C or S α = D Last D First β = 7 days γ = 60 days θ = (α + β + γ) White, Harpaz, Shah, DuMouchel, Horvitz, Nature CPT, 2014

Wrestling with Large-Scale Population Data White, Harpaz, Shah, DuMouchel, Horvitz, Nature CPT, 2014

Wrestling with Large-Scale Population Data Alignment Machine learning to align Example: Pregnancy info needs Fourney, White, Horvitz, CHI 2015

Wrestling with Large-Scale Population Data Alignment Machine learning to align Example: Pregnancy info needs A. Fourney, R. White, E. Horvitz, CHI 2015 Fourney, White, Horvitz, CHI 2015

Wrestling with Large-Scale Population Data Alignment Episodic structure Synthetic (per privacy), yet representative set of queries Example: Breast cancer Paul, White, Horvitz, TWEB 2016

Wrestling with Large-Scale Population Data Alignment Episodic structure Key pivot points Diagnosis date Screening Surgery Chemotherapy Example: Breast cancer Paul, White, Horvitz, TWEB 2016

Wrestling with Large-Scale Population Data Alignment Episodic structure Example: Breast cancer Paul, White, Horvitz, TWEB 2016

Wrestling with Large-Scale Population Data Alignment Episodic structure Key pivot points Diagnosis date Screening Surgery Chemotherapy Example: Breast cancer Paul, White, Horvitz, TWEB 2016

Propensity: Normalizing cohorts to understand influences u 1 timeline u 2 u 3 Learning outcome of action Kiciman and Richardson, KDD 2015

Propensity: Normalizing cohorts to understand influences timeline Kiciman and Richardson, KDD 2015

Propensity: Normalizing cohorts to understand influences timeline

Propensity: Normalizing cohorts to understand influences timeline p p

Kiciman and Richardson, KDD 2015

Opportunity: Major Life Changes Understand, support through difficult life changes Major illness Loss Death Divorce Birth Example: Birth of child Postpartum depression (PPD) CDC: ~15% 50% cases unreported De Choudhury, Counts, Horvitz. CSCW 2013, CHI 2013

Identifying Multiple Dimensions of a Life Change Identify tweets about births: Twitter Firehose 2,929 new mother candidates Gender classifier 10 tweets per candidate & profile 376 new mothers Birth! -3 months * + 3 months 1 st person pronoun

Structure & Dynamics of Engagement on Social Graph Engagement. Volume: mean normalized number of posts per day Replies, retweets, questions, shared links Ego network #inlinks; #outlinks De Choudhury, Counts, Horvitz. CSCW 2013, CHI 2013

Linguistic Analysis De Choudhury, Counts, Horvitz. CSCW 2013, CHI 2013

Linguistic analysis 3 o 1 o Neg. Affect

Patterns and Outcomes ~15% of new mothers: severe changes - Activity level down, - Language usage: 1 st person up, 3 rd person down, - Negative affect up, positive affect down

Predicting Postnatal Outcomes Predicting postpartum changes with data drawn before birth. Exciting family of results Human subjects + online: New mothers w/ FB timeline, Twitter Major depressive episodes De Choudhury, Gamon, Counts, Horvitz. ICWSM 2013 De Choudhury, Counts, Horvitz, Hoff. ICWSM 2014

Major Life Challenges: Major Illness Grappling with diagnosis & treatment Queries before & after inferred breast CA diagnoses Proxy for understanding if, how, and when life may return to normal? Paul, White, Horvitz, TWEB 2016 Paul, White, Horvitz, WWW 2015

Major Life Challenges: Grieving

Major Life Challenges: Grieving It is remarkable that this painful unpleasure is taken as a matter of course by us.

Major Life Challenges: Grieving It is remarkable that this painful unpleasure is taken as a matter of course by us. Normally, respect for reality gains the day.

Major Life Challenges: Grieving It is remarkable that this painful unpleasure is taken as a matter of course by us. Normally, respect for reality gains the day. In reality, however, this presentation is made up of innumerable single impressions (or unconscious traces of them), and this withdrawal of libido is not a process that can be accomplished in a moment, but most certainly, as in mourning, be one in which progress is long-drawn-out and gradual.

Opportunity: Assist with Complicated Grief Unusually severe & prolonged grieving that impairs function 2 to 3% of population worldwide Death: Child or life partner, sudden violent death High rates of suicidal ideation, risk-taking Value of psychotherapy N Engl J Med 2015;372:153-60.

Opportunity: Assist with Complicated Grief

On Sensitivity, Ethics, Disclosure Yom-Tov, White, Horvitz. JMIR, 2014.

On Sensitivity, Ethics, Disclosure Horvitz & Mulligan, Science 2015