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Enhancing Precision Wellness with
Knowledge Graphs and Semantic Analytics
Or
Overcoming some key challenges in machine
lea...
Challenges to Machine Learning
• Powerful new analytic techniques are leading
to advances in precision medicine
– However,...
Personalized
HEALTH
EMPOWERMENT by
Putting the right information into the hands of patients
and clinicians when they need it the most
A...
Problem Objectives Technical Challenge
Relevant knowledge is
heterogeneous
Personal information must be aligned with disea...
Knowledge Graph for Personal Health
PERSONAL
DISEASE
DIET
ACTIVITIES
Web sources & online forums
Personal health data
AI/M...
I'm not losing weight, what else can I do?
I see that you're walking about 5,000 steps/day,
which is below the American He...
I usually get a battered fish sandwich and a side of
fries.
Ok, that’s 981 calories and 43g of fat for lunch. Guidelines
r...
Note: Work in progress
I'm not losing weight, what else can I do?
It looks like you have been active so let's look at your
diet.
Can you explain ...
Why is this a problem? I usually am not hungry
during the day so I just eat when I get home from
work.
According to WebMD,...
Action Plan
Creation
Action plan
Evaluation
Behavior
Sustainability
Coaching
Personalized information
Framework for Person...
Actionable
Hospital ED Readmission example
Identified factors, based on “Emergency Dept (ED) Log”,” In Patient” and “Out Patient”
dat...
Making the Outcomes Actionable
16
Domain experts (e.g. Hospital Administrators in this case) need to understand the result...
Actionable: Dynamic “cadre” identification
• 47% of revisits caused by
402 patients with
multiple visits
• 151 patients ca...
Infer risk factors dynamically for different “cadres”
Select Disease
Select Risk Factors
Select
confounders
Statistical an...
Collaborative
Collaboration: Cognitive and Immersive Systems Laboratory
CISL is a member of the IBM AI Horizons Network
Director: Hui Su...
Collaboration: The Rensselaer Campfire (IDEA)
Explainable
Explanation: Going beyond Recognition
Generating Triples with Adversarial Networks for Scene Graph
Construction (Klawonn &...
Conclusions
• Machine Learning is a great tool, but using the
results more widely will involve research into a
number of a...
Enhancing Precision Wellness with  Knowledge Graphs and Semantic Analytics: Overcoming some key challenges in machine lear...
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Enhancing Precision Wellness with Knowledge Graphs and Semantic Analytics: Overcoming some key challenges in machine learning for healthcare

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Talk presented at Bio-IT 2018 (machine learning track) - explores some approaches to overcoming challenges of using machine learning systems in healthcare applications.

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Enhancing Precision Wellness with Knowledge Graphs and Semantic Analytics: Overcoming some key challenges in machine learning for healthcare

  1. 1. Enhancing Precision Wellness with Knowledge Graphs and Semantic Analytics Or Overcoming some key challenges in machine learning for healthcare Professor James Hendler Director, Rensselaer Institute for Data Exploration and Analytics
  2. 2. Challenges to Machine Learning • Powerful new analytic techniques are leading to advances in precision medicine – However, there are a number of obstacles in bring these to the clinician/user level; results must be: • Personalized • Actionable • Collaborative* • Explainable * i.e. support ollaboration in decision making
  3. 3. Personalized
  4. 4. HEALTH EMPOWERMENT by Putting the right information into the hands of patients and clinicians when they need it the most ANALYTICS Using data for hypothesis formation and testing LEARNING & Improving knowledge continuously SEMANTICS Integrating health & medical knowledge from heterogeneous sources HEALS is a member of the IBM AI Horizons Network Rensselaer-IBM HEALS project
  5. 5. Problem Objectives Technical Challenge Relevant knowledge is heterogeneous Personal information must be aligned with disease-related information • Disease progression & risk factors • Treatment guidelines • Nutritional & physical activity habits and preferences • Other lifestyle constraints Resolving inconsistencies between disparate sources Relevant knowledge is dynamic Data sources (e.g., personal context, social media, web pages) are constantly changing and must be reconciled Keeping knowledge current Patients have unique needs Individual’s information needs are dependent on their specific micro- to macro-level contexts Deriving personalized insights Raw insights are not interpretable or actionable A system must educate on good practices, support planning for improved health, and track and evaluate performance Delivering explainable recommendations Personalized Information
  6. 6. Knowledge Graph for Personal Health PERSONAL DISEASE DIET ACTIVITIES Web sources & online forums Personal health data AI/ML Technologies - Data-mining / KDD - NLP - Semantic Search - Semantic Data Integration
  7. 7. I'm not losing weight, what else can I do? I see that you're walking about 5,000 steps/day, which is below the American Heart Association's recommendation. However, I can see from your calendar that you don't have a lot of free time to focus on physical activity. Maybe we should we talk about your diet even thought I don't have a lot of information about your eating habits? System recognizes concept of "weight" that is tied to diet and activity. System analyzes both activity and diet data in the personal KG (sourced from Apple HealthKit) for the last month: • Activity data: Compares Ed's activity with the recommended guidelines (10,000 steps per day AHA recommendation), and determines that he is not getting enough activity from walking (Ed averages only 5,124 steps per day). • Calendar data: Use iOS EventKit and finds Ed has very little free time during the day. • Diet data: Ed doesn't log his food, so system needs to ask him about his dietary habits. Sure. I see that you usually go to Applebee's for lunch during the week, what do you usually order? However, the system recognizes that he spends lunch time at Applebee's every day (sourcing location data from iOS CoreLocation API matched with restaurant listings from the Google Maps API ). Conversation Back-End System ED ED
  8. 8. I usually get a battered fish sandwich and a side of fries. Ok, that’s 981 calories and 43g of fat for lunch. Guidelines recommend that you should be consuming 2086 calories/day so your lunch is 47% of your daily calories. For more information, click here. System retrieves nutrition facts about Ed's "average" lunch from the nutrition KG and populates Ed's personal KG with the self-reported data about his diet habits. System calculates recommended daily caloric intake for Ed based on: • Ed's height 5'10'', current weight 200lb, age 57, caloric intake etc. from Ed's personal knowledge graph • Guidelines on recommended caloric intake • System computes recommended caloric intake from the Mifflin-St. Jeor equation (for BMR) & fat intake from Ed's personal KG , compares to his data What is a healthier option? Next time try a 6-oz top sirloin, baked potato, and fire- grilled veggies for lunch. This only has 540 calories and fewer than 22 grams of fat. I also found other restaurants near you with healthy options. Would you like more information? System retrieves the Applebee's menu with nutrition information from the Nutrition KG and recommends healthier food substitutes at the same restaurant that are in the similar categories to Ed's usual lunch. ED ED Conversation Back-End System
  9. 9. Note: Work in progress
  10. 10. I'm not losing weight, what else can I do? It looks like you have been active so let's look at your diet. Can you explain what the graph means? It looks like you usually have a big dinner and snack at night. You also don't eat lunch most days. Also, you tend to have more calories over weekends. System uses Watson NLU to recognize concept of "losing weight" that is tied to diet and activity. Then, system analyzes both activity and diet data stored in the personal KG (sourced from Apple HealthKit) for the last month: • Activity data: Compare Jessica's activity with the recommended guidelines (10,000 steps per day AHA recommendation), and determines that she is almost getting sufficient activity 9,356 steps per day) from walking. • Diet data: Jessica logs food in MyFitnessPal, which syncs with Apple Health. The system compares her recommended caloric intake (1700 kCal) and her average caloric intake. On weekdays her caloric intake is (2200 kCal), and weekends she consumes (3000 kCal)System mined pattern from personal KG: she has a large gap between breakfast and dinner and she typically consumes 2/3 of her daily calories at dinner in addition to snacks at night. She doesn't eat lunch and also eats more on the weekends. JE Conversation Back-End System JE
  11. 11. Why is this a problem? I usually am not hungry during the day so I just eat when I get home from work. According to WebMD, The longer the gap between dinner and the previous meal or snack, the larger the dinner. People who eat lightly at night end up eating fewer calories and grams of fat overall than people who eat big dinners and nighttime snacks. For more, click here. Do I really eat a lot of fat at night? According to AHA guidelines, You should be consuming 51 grams of fat/day. On average, you eat 70 grams at night. Here is a breakdown of your average fat content for breakfast, lunch, dinner, and snacks. For more, click here. System extracted explanation from WebMD, by searching for <late night snacks and big dinners>. System calculates recommended fat intake for Jessica based on: • Her height, current weight, caloric intake etc. from Jessica's personal KG • AHA guidelines on recommended fat intake • System computes recommended caloric intake from the Mifflin-St. Jeor equation (for BMR) & fat intake from Jessica's personal KG , compares to her data Conversation Back-End System JE JE
  12. 12. Action Plan Creation Action plan Evaluation Behavior Sustainability Coaching Personalized information Framework for Personal Health Empowerment Note: Personal health only one small part of overall HEALS effort…
  13. 13. Actionable
  14. 14. Hospital ED Readmission example Identified factors, based on “Emergency Dept (ED) Log”,” In Patient” and “Out Patient” datasets, that could improve our ability to predict if a patient would, or would not, return to the ED within 72 hours of discharge. What this entailed: • Derived and analyzed dependent variables for 72 hour readmissions – Examined over ~15000 existing variables. – Developed new variables based on roll-ups and historical data. • Used models that computed the combinations of sets of these variables – Identified the best set of predictive variables for the available ED data • 300 factors identified, reduced to 74 key variables – Weighted Logistic Regression Analysis Performed • Reported existing state of the art results for EDs: 73% to 85% • Result of our two month study: Accuracy 80.1% 15 Ryan et al, Big Data, 2015
  15. 15. Making the Outcomes Actionable 16 Domain experts (e.g. Hospital Administrators in this case) need to understand the results and can only take action on certain of them – the overall accuracy was not their key concern.
  16. 16. Actionable: Dynamic “cadre” identification • 47% of revisits caused by 402 patients with multiple visits • 151 patients cause 29% of all revisits • Patients with a past ED revisit are more than 3 times likely to revisit
  17. 17. Infer risk factors dynamically for different “cadres” Select Disease Select Risk Factors Select confounders Statistical analysis of detected risk factors A. New, C. Breneman, and K. P. Bennett. Cadre modeling: Simultaneously Discovering subpopulations and predictive models. In 2018 International Joint Conference on Neural Networks (IJCNN), 2018.
  18. 18. Collaborative
  19. 19. Collaboration: Cognitive and Immersive Systems Laboratory CISL is a member of the IBM AI Horizons Network Director: Hui Su (IBM/Rensselaer)
  20. 20. Collaboration: The Rensselaer Campfire (IDEA)
  21. 21. Explainable
  22. 22. Explanation: Going beyond Recognition Generating Triples with Adversarial Networks for Scene Graph Construction (Klawonn & Heims, AAAI, 2018)
  23. 23. Conclusions • Machine Learning is a great tool, but using the results more widely will involve research into a number of areas – Individuals vs. cohorts – “Best” vs. “most useful” ML results – Supporting collaborative human (and eventually human/AI) decision making – Making the results of deep ML explainable

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