Public Health Intelligence Platform for Social Health Records (SHR)* - - PDF document

11/3/2017 1

Public Health Intelligence Platform for Social Health Records (SHR)*

City University of New York Soon Ae Chun In collaboration with Xiang Ji (NJIT PhD graduate, Bloomberg Inc.) James Geller (NJIT)

Introduction & Overview

• Online health-related social networks generate a big amount of health data,

e.g. – Twitter, PatientsLikeMe, Medhelp, etc.

• SHR (Social Health Records)

– Social Media-based Health-related Data – How can we leverage these for gaining health Intelligence & better healthcare?

• We present an integration and analytics framework of social health records (SHR)

to address three problems:

– Health Data Integration Problem – Population Analytics Problem – Predictive Analytics Problem

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Statistics: Social Media Use in Healthcare

consumers say that information found via social media affects the way they deal with their health (chronic disease, diet & exercise) 18 to 24 year olds are more than twice as likely as 45 to 54 year

• lds to use social

media for health- related discussions. from 18 to 24 years of age said they would trust medical information shared by

• thers on their social

media networks. healthcare

• rganizations have

specific social media guidelines in writing.

• f adults are likely to share

information about their health on social media sites with other patients, 47 percent with physicians, 43 percent with hospitals, 38 percent with a health insurance company and 32 percent with a drug company.

• f all hospitals in the

United States participate in social media.

• f smartphone owners

have at least one health app on their

• phone. Exercise, diet,

and weight apps are the most popular types. patients are very comfortable with their providers seeking advice from online communities to better treat their conditions.

• f healthcare

professionals use social media for professional networking.

• f people said social

media would affect their choice of a specific physician, hospital or medical facility.

26%

41% 31% 54% Youths 90% Youths 31% 19% >40% 31% 2/2015 Becker’s ASC Review

Most popular online resources for Health Information

• The most accessed online resources for health related information are:
• 56% searched WebMD,
• 31% on Wikipedia,
• 29% on health magazine websites,
• 17% used Facebook,
• 15% used YouTube,
• 13% used a blog or multiple blogs,
• 12% used patient communities
• 6% used Twitter and
• 27% used none of the above

(source: Mashable, 2012, referralmd.com)

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Social Media Health Data

• Twitter

– 230 million tweets posted per day in 2011 -> 317 million (2016) (statistica.com)

• PatientsLikeMe

– 17,835 patients –share profiles with public; 307,033 members –share only with members – 500+ health conditions (as of 2015)

• Medhelp

– 20 million monthly visitors – Track pain, weight, chronic diseases

• CureTogether – anonymously track and compare health data
• DailyStrength – emotional support groups
• Inspire – different communities to offer support and educate
• FacetoFaceHealth – algorithm to match people with similar diagnoses
• Meddik – empower patients to search health info and learn from experiences from others
• Doximity – medical doctors and students network to extend and build prof relationships

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Challenges Social Media for Health Care

• People do seek for Health Information from the social media to make personal health

care decisions

• However, the challenge is that they need to

– visit many different information sources. – synthesize, – reason, – compare – to make a reasonable decision on their health.

• In other words, social health data

– Vast amount of health data, Distributed (scattered) heterogeneous data, streaming data – DRIP syndrome: Data rich and Info Poor  core problems DS addresses – Data Science methodologies can provide necessary analytics to generate more “useful” knowledge for health care. – SOCIAL HEALTH Analytics Framework

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Patient’s Health Reports on Social Media

SHR (Social Health Records)

• Generated by patients

– Health status reports

• Headaches, experienced symptoms
• Diagnosis reports

– Healthcare practice data

• Actual medications, treatments
• Side effects from treatments

– Health-related behaviors/habits

• Drinking, smoking, etc.
• Exercises, fitbits
• Nutritional

EHR (Electronic Health Records)

• Entered by clinical professionals

– Clinical data

• medications, allergies, problems, procedures,

chart notes, clinical alert notes, lab results, and images

– Patient history – Orders – Medications/Allergies – Demographic data – Lab data

Social Health Records (SHR)

EHR

• Structured/unstructured
• Uses Medical expert language

– Myocardial infarction

• Comparatively precise

– ICD9 code for a disease

• Not easily accessible due to

HIPAA privacy law

• Localized/silo

– Hospital, provider/group – Application specific (lack of interoperability)

• Factual statement

SHR

• Mostly unstructured data
• Informal everyday language

– Hearattack

• Ambiguous, vague

– Diabetes (type 1 or 2), hepatitis (A or C?)

• Publicly available or more

readily available

• Can be access around the

world

– Web browser accessible

• Emotional
• Reviews, empatic statements, Annotations

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Social Health Records (SHR)

• Seems to exhibit potentials to investigate

– Major concerns or topics in health – Health risks, attitudes towards health – Identify trends – Personal feelings/views on treatments or conditions – What is desired outcomes – Track adverse drug events

• Can it serve as a knowledge source for clinical, policy related decision

making as well?

• the social health data as complimentary data source for research and clinical decisions,
• knowledge source for Health Intelligence to understand health behaviors or practices for

population?

SHR Integration and Analytics Framework

• A social health Integration and analytics framework uses Social Health

Records as the first class data to gain useful insights – Health Data Integration

• Scattered data sources

– Predictive Analytics Problem

• From similar individuals to predict a future disease of a person?
• Comorbidity trajectory model

– Public Health Analytics

• Public health issues e.g. epidemic outbreak detection, public sentiments
• Drug abuse detection

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• Users have to integrate health information from all these sources into one coherent

mental model.

Query: What are other patients’ symptoms and drug reviews for treating the top-10 conditions?

Research Issues & Approaches

• Research Issues
• How can we model and integrate the extracted data to satisfy the information

needs?

• How can we best present social health analytics and inference results to users?
• Approach-
• Health data integration for Analytics

– Designed semantic model & RDF storage to perform integration of data that can satisfy the information needs. – Developed context-aware social analytics and inferences. – Social InfoButton (knowledge from social data about population health behaviors, practices..)

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RDF-Based Storage

• Triple: <subject, predicate, object>.
• A patient “John” has a profile page as well as a health condition “Psoriasis”.

http://www.patientlikeme.com/patient#1050 (URI1) http://www.patientlikeme.com/ Members/232328/about_me (URI2) “John” hasName hasProfile http://www.patientlikeme.com/condition #154 (URI3) “Psoriasis” hasCondition hasConditionName

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Information Needs

User Information Need Examples Patient Pre-diagnosis What are the symptoms for diabetes? What are the treatment

• ptions for high blood sugar?

Post-diagnosis What are the new research findings about breast cancer? Are my symptoms indeed caused by the diagnosed condition? Community Support What patients or expert communities can provide support for a specific condition? Clinician Drug Choice What are the drug options used by other patients to treat a specific condition? Drug Dosage How many pills a day and how many times a day should the patients take a specific drug? Side Effect What are the possible adverse effects of a specific drug, and how severe are they? Organization Disease Surveillance Where are the current disease outbreaks? What is the trend of a specific condition? What are the online profile, # of posts, and # of replies for a specific condition?

SHR Analytics

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Data Source Patients Clinicians Government Support Community Pre- diagnosis Healthcare Providers Post- diagnosis Drug Choice Drug Dosage Adverse Effect Disease Surveillance PatientsLikeMe P P P P Twitter P MedHelp P P P WebMD P P P Mayo Clinic P P CDC P P PubMed P

Open Health Data Sources

Data Source Patient Condition Treatment Symptom Review Community Post State Prevalence PatientsLikeMe 17,407 1,228 5,608 2,176 n/a n/a n/a n/a MedHelp n/a n/a n/a n/a n/a 365 69,243 n/a WebMD n/a 647 180 n/a 86,715 n/a n/a n/a Mayo Clinic n/a 1,116 2,496 5,426 n/a n/a n/a n/a CDC n/a n/a n/a n/a n/a n/a n/a 52

Social InfoButtons

• Use case: A doctor is devising the best practice for a PTSD (Post Traumatic

Stress Disorder) patient.

Statistical Analytic

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Social InfoButtons (Cont.)

Twitter Tag Cloud Individual Tweets Geospatial Analytic

Social InfoButtons (Cont.)

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Asthma Map and Gender distribution Compare treatments for Fibromyalgia in Social InfoButtons and Authoritative Sources

Treatment Present in Social Present in Authority Duloxetine Yes (1058) Yes Pregabalin Yes (955) Yes Milnacipran Yes (357) Yes Gabapentin Yes (346) Yes Tramadol Yes (201) Yes Cyclobenzaprine Yes (188) No

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• Treatments of Major Depressive Disorder in Social Source completely overlap with

Authoritative Source (Authority)

Treatment in Social Source # of Patients in Social Source Appears in Authority Individual Therapy 185 Yes Bupropion 174 Yes Venlafaxine 160 Yes Duloxetine 146 Yes Fluoxetine 136 Yes Citalopram 123 Yes Sertraline 119 Yes Escitalopram 79 Yes Desvenlafaxine 30 Yes Mirtazapine 26 Yes Electroconvulsive-Therapy ECT 24 Yes

System Evaluation

• Symptoms of Major Depressive Disorder in Social Source partially overlap with Authoritative

Source (Authority)

Symptom in Social Source # of Patients in Social Source Appears in Authority Problems concentrating 8402 Yes Muscle tension 7325 No Headaches 7205 Yes Back pain 6337 Yes Dizziness 4900 No Stomach pain 4898 No Lack of motivation 4468 No Nausea 4453 No Low self-esteem 3847 No Inability to experience pleasure 3062 Yes Hyperventilation 2485 No

System Evaluation (Cont.)

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• Complement medical knowledge: When SI’s social source information differs with

information from authoritative source differs, SI proposes a second opinion to the human expert.

Added Value of Social InfoButtons (SI)

Condition Symptom in Social Source Symptom in Authoritative Source Multiple Sclerosis Stiffness/Spasticity Numbness or weakness in limbs Brain fog Optic neuritis Excessive daytime sleepiness Double vision or blurring of vision Mood swings Tingling or pain in parts of your body Bladder problems Electric-shock sensations Emotional lability Tremor, lack of coordination Sexual dysfunction Slurred speech Bowel problems Fatigue Epilepsy Memory problems Temporary confusion Problems concerntrating A staring spell Excessive daytime sleepiness Uncontrollable jerking movements of arms and legs Headaches Loss of consciousness or awareness

Application in Clinical Environment

• InfoButtons Cimino et al. [8, 9]

– meet the clinician’s information needs in the context of patient care, complement the EHR

• “Can drug x cause (adverse) finding y?”,
• “What are my patient’s data? ”,
• “How should I treat condition x (not limited to drug treatments)? ”,
• “What is the drug of choice for condition x? ”

– A point-of-care information retrieval application that automatically generates and sends queries to digital libraries using patient data extracted from the electronic medical record.

• simple links, concept-based links, simple search, concept-based search, intelligent

agents, and a calculator.

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Source [7]

Social InfoButton

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Comorbidity Study with Social Health Records

• Comorbidity Prediction: Current appearance of some conditions indicates the

future occurrence of other conditions. (e.g. diabetes and foot sores)

• Comorbidity prediction benefits

– reduced mortality, lower hospital stay, lower healthcare

• Examples:

– Diabetes

• Hypertension (high blood pressure)
• Dyslipidemia (Abnormal LDL, HDL, or triglycerides, increasing risk for heart attack)
• Nonalcholic fatty liver disease (NAFLD)
• Cardiovascular disease
• Kidney disease
• Obesity

Research Issues

• How to predict medical condition incidence for individual patient?

– e.g. John is diagnosed with condition X, what is the likelihood that he develops condition Y in the future?

• How to predict medical condition progression trajectory for population which

can provide insights for individual treatment planning

– e.g. Tom is diagnosed with condition X, what is the confidence value of developing condition trajectory XYZ in the future?

• What data is available for modeling comorbidities?

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Patient’s Social Medical Profile Comorbidity Trajectory Model

• In many situations it is more desirable to predict a medical condition progression

trajectory.

• A trajectory model is proposed to track the progression and infer the most probable

future trajectories.

• The model is constructed in three steps:
• Edge Discovery: Identifying directional edges of comorbidities, which co-occur for

individual patients.

• Linking: The generated edges are recursively linked to build the condition

trajectory tree T by recognizing the common node (condition) in two edges.

• Inference: The confidence value C of edge trajectory (e1e2e3,…,en) given

an observed condition c is calculated as a conditional probability.

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• By setting the root condition to C2, the tree below was built by Algorithm 1. (number

in parenthesis is the trajectory support)

• The confidence value of trajectory T given root condition c is defined as a conditional

probability: C(T|c) = support(T)/support(c) e.g., C(C2C8C7|C2) = 1/2 = 0.5

Trajectory Model (Continued) Progression Trajectory Analysis Results

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• The confidence value
• C(MDDGADPD|MDD) = 37/680 = 5.4%;
• C(MDDDysthymiaPD|MDD) = 3.4%;
• C(MDDPTSDPD|MDD) = 3.2%;
• C(MDDSocial Anxiety DisorderPD|MDD) = 2.5%.
• The likelihood going through GAD is higher than other paths.

Progression Trajectory Analysis Results

MDD: Major Depressive Disorder GAD: Generalized Anxiety Disorder PD: Panic Disorder PTSD: Post-Traumatic Stress Disorder

Evaluating Trajectory Model

• We selected three medical conditions with well-studied comorbidities*.

Condition Comorbidity Major Depressive Disorder (MDD) Dysthymia, Panic Disorder, Agoraphobia, Social Anxiety, Obsessive–Compulsive Disorder, Generalized Anxiety Disorder, and Post-Traumatic Stress Disorder, Alcohol Dependence, Psychotic Disorder, Antisocial personality, Eating Disorders, Borderline Personality Disorder Irritable Bowel Syndrome(IBS) Major Depression, Anxiety, Somatoform Disorders, Fibromyalgia, Chronic Fatigue Syndrome, Gastroesophageal Reflux Disease, Restless Legs Syndrome Eating Disorder (ED) Obsessive–Compulsive Disorder, Bipolar Disorder, Substance Abuse (Drug Addiction/Alcohol Abuse), Diabetes, Bone Disease, Cardiac Complications, Gastrointestinal Distress

*http://www.huffingtonpost.com/kenneth-l-weiner-md-faed-ceds/eating-disorders_b_1761513.html

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Evaluating Trajectory Model (Cont.)

• Trajectory starting from conditions (confidence in percentage/support); * indicates that the

comorbidity exists in medical literature.

Condition Trajectory Major Depressive Disorder (MDD) Major Depressive Disorder-> Post-Traumatic Stress Disorder (PTSD)* ->Panic Disorder* -> Social Anxiety Disorder* (1.3/9) MDD->Panic Disorder*->Social Anxiety Disorder*->Phobic Disorder (1.1/8) MDD->Generalized Anxiety Disorder (GAD)*-> Obsessive- Compulsory Disorder (OCD)* (3/23) MDD->Panic Disorder*->Obsessive- Compulsory Disorder* (2/19) MDD->Bipolar II (4/21) MDD->Borderline Personality Disorder* (3/21) Irritable Bowel Syndrome(IBS) IBS-> Gastroesophageal Reflux Disease (GERD)*-> Restless Legs Syndrome* (3/6) IBS->Fibromyalgia*-> Chronic Fatigue Syndrome (CFS)* (9/17) IBS->Restless Legs Syndrome* (12/23) IBS->Osteoarthritis (10/18) Eating Disorder (ED) ED->Tobacco Addiction->Drug Addiction*->Panic Disorder (4/5) ED->Obsessive- Compulsory Disorder*->Panic Disorder->Social Anxiety Disorder (4/5) ED->Bipolar II*->Drug Addiction (5/6) ED->Drug Addiction*->Alcohol Addiction* (6/7) ED->Postpartum Depression (13/15) ED->Alcohol Addiction* (13/16)

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Epidemics are a major threat for humanity

(killed 962. year 2003) (killed 18400. year 2009) (killed 30. year 2011)

SARS Swine Flu

Listeria

1918 flu pandemic (Spanish influenza)

(killed 50-100 million. year 1918-1920) Ebola

(17145 cases killed 6070 year 2014)

Epidemics Monitoring and Detection

Zika virus

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Research Issues

• Epidemic monitoring and surveillance

– Watch rapid and timely data streams to discover trends and patterns in health events

• Public Concern monitoring

– Active dissemination of medical myths and misinformation by self-interested propagandists. – Social media “storms” are able to cause and create shared public responses that may or may not be appropriate for the health event. – The verification of the shared health information, especially as it relates to fast-moving epidemics or heightened seasonal health concerns is crucial to keeping the public accurately informed. – The ability to respond publicly and in a timely manner to the spread of misinformation and health-related rumors during public health events, as the 2014 Ebola crisis illustrated. Health agencies need to have plans in place ahead of time to be able to respond to and counter misinformation or support accurate information shared via social media.

Twitter Data Collection

• Migrated from PHP-based 140dev library to Java-based Twitter4J.
• Collected 11.7+ million tweets across 14 diseases/disasters in DB.

Dataset Id Tweet Type Total number of Tweets 1 Listeria 43,646 2 Influenza 2,231,442 3 Swine Flu 121,208 4 Measles 276,282 5 Meningitis 189,886 6 Tuberculosis 245,639 7 Major Depression 3,209,413 8 Generalized Anxiety Disorder 386,262 9 Obsessive-compulsive Disorder 571,867 10 Bipolar Disorder 181,942 11 Air Disaster 22,946 12 Melanoma Experimental Drug 145,357 13 Natural Disaster 1,746,899 14 Ebola 2,385,275

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Distribution maps of Listeria Tweets (Sep 26)

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09-26-2011 absolute 09-26-2011 relative 09-27-2011 absolute 09-27-2011 relative

In September of 2011, there was a sudden outbreak of Listeria in US. CDC’s (US Government Center for Disease Control and Prevention) report, as of 11am EDT on September 29, 2011

[http://www.cdc.gov/listeria/outbreaks/cantaloupes-jensen-farms/093011/index.html, accessed on 4/1/2012]

• 84 persons were infected with listeria as reported by

CDC.

• The states with the largest numbers of infected persons

were: Colorado (17), Texas (14), New Mexico (13), Oklahoma (11), Nebraska (6), Kansas (5).

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Comparing Social data with CDC data

In the six most affected states indicated by CDC report (blue line), EOSDS result correlated well with CDC report in four states (cycled in red). There are two states (cycled in blue) showing differences between EOSDS results and CDC report, what happened?