Casual Representation and Explainability

The artificial intelligence (AI) systems do not incorporate high-level reasoning and understanding about causality. As a result, the systems do not form the kinds of semantic representations and inferences that humans can. Enriching the AI systems to incorporate causality enabled with counterfactual reasoning is a challenging task due to a lack of richer causality representation. My research focuses on developing, CausalKG, a richer representation for causality using knowledge graphs for better explainability with autonomous driving and healthcare applications. CausalKG can infuse existing KGs with causal knowledge of the domain to enable interventional and counterfactual reasoning. The advantage of constructing a CausalKG is the integration of causality in reasoning and prediction processes, such as the agent action understanding, planning, medical diagnosis process, etc. Such integration can improve the accuracy and reliability of existing AI algorithms by providing better causal and domain adaptable explainability of the outcome.

Team: Utkarshani Jaimini, Dr. Cory Henson

kHealth

Knowledge-enabled health care (kHealth) is an interdisciplinary project to help doctors determine precisely the cause, severity and control of asthma. It aims to alert patient/caregiver to seek clinical assistance in timely manner to better manage asthma and improve their quality of life. The project includes real-world evaluations (i.e. 200 children with asthma) to measure the effectiveness of the approach. It uses patient generated health data (PGHD) via mobile and sensor technologies. kHealth supports the AI-based reasoning approach of semantic cognitive perceptual computing and augmented personalized health (APH). kHealth research encompasses multiple funded projects, the most recent one being "SCH: kHealth: Semantic Multi-sensory Mobile Approach to Personalized Asthma Care", which was funded by the National Institutes of Health in 2016.

Team: Utkarshani Jaimini, Revathy Venkatraman, Joey Yip, Vaikunth Sridharan, Dipesh Kadaria, Quintin Oliver

1. Contextual Multi-arm bandits for Personalized Treatment Recommendation

   Timely Treatment Recommendation can lead to better health outcome, reduce healthcare cost, and improved quality of health in pediatric patients. Develop a Treatment Recommendation system using Contextual multi-arm bandits to improve the patient health state of the pediatric asthma patients. Determine the contextual features of a patient which are most efficient for predicting patient’s health status. Explore the offline contextual bandit estimators for learning and propose a general framework for learning algorithms.

2. Personalized Causal Model and Dynamic Treatment Regimes for Pediatric Asthma Patients

   A Causal Model to determine the causal relationship between asthma symptoms, medication intake and asthma triggers in the pediatric asthma patient population. Evaluate the efficacy of treatment policies via counterfactual variable on the patient’s health state using the total, natural direct, and natural indirect effect estimation.

3. Personalized Bayesian Inference and Prediction Model

   A personalized knowledge-based Bayesian inference model to predict symptoms for different outdoor environmental factors utilizing the real-world knowledge from the pediatric asthma patient’s data collected from continuous monitoring framework: kHealth-Asthma. Analyzed multimodal data for correlation between the asthma symptoms and various asthma triggers.

4. Health Coefficient for environmental susceptibility detection

   A Health Coefficient metric to determine the health of the pediatric asthma patient with the onset of the outdoor environmental triggers. Utilizing the prediction from the personalized Bayesian inference model, performed Weibull analysis to discern the susceptibility level of a patient to various outdoor environmental triggers.

5. Digital Phenotype Score for Pediatric Asthma Patients

   A Digital Phenotype score (DPS) as an abstraction of the multimodal digital phenotypes collected using the sensors and mobile applications. The DPS is clinically relevant to the Asthma Control Test (ACT) score taken at the clinic. The DPS provides an opportunity for early clinical intervention by the healthcare provider, leading to better management, and monitoring of asthma.

6. Activity detection using an Indoor Air Quality Sensor for Asthma Management in Children

   Data-driven approach to develop a continuous monitoring-activity (such as cooking and smoking) detection system aimed at understanding and improving indoor air quality in asthma management. High concentration of particulate matter, volatile organic compounds, and carbon dioxide was detected during cooking and smoking activities. The activity detection system can allow clinicians to correlate potential asthma symptoms and exacerbation reports from patients with environmental factors without having to personally be present

7. Integration of multimodal data for Asthma Management using kHealth Asthma Ontology (kAO)

   Explored the use of semantic web technologies for the integration of multimodal data collected from the continuous monitoring of pediatric asthma patients with the kHealth Asthma Ontology. Improved the existing BioPortal Asthma Ontology to add meaningful relationship information between the ontology concepts. The queries demonstrated that the data integration enabled us to extract useful information and performed hypothesis validation across different data modality.

Analyzed the Wikipedia page text to determine the use of gender specific language using Naïve Bayes and Natural Language Processing techniques. The proposed work can serve as a pre-cursor in monitoring and moderating abusive language online by providing the gender context.