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  "author": "Hao Huang",
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  "author": "Hao Huang",
            
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  "name": 
            
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nterfactual-prediction-over-knowledge-graphs-for-personal-healthcare",
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nterfactual-prediction-over-knowledge-graphs-for-personal-healthcare",
            
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  "notes": "https://youtu.be/vkF3fpCZLhk Artificial intelligence (AI) 
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  "notes": "https://youtu.be/vkF3fpCZLhk Artificial intelligence (AI) 
            
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has become an invaluable tool in health-care for disease prediction 
53
has become an invaluable tool in health-care for disease prediction 
            
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and diagnosis. Despite their predictive accuracy, AI models may ignore 
54
and diagnosis. Despite their predictive accuracy, AI models may ignore 
            
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causal relationships between patient characteristics (demographic or 
55
causal relationships between patient characteristics (demographic or 
            
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clinical). As a result, although AI models capture associative 
56
clinical). As a result, although AI models capture associative 
            
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patterns, ignoring the causal relationships of predicted 
57
patterns, ignoring the causal relationships of predicted 
            
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characteristics limits their ability to perform counterfactual 
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characteristics limits their ability to perform counterfactual 
            
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reasoning about the predicted characteristics of a patient when 
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reasoning about the predicted characteristics of a patient when 
            
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dependent characteristics change. This limitation of AI models can 
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dependent characteristics change. This limitation of AI models can 
            
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affect the understanding of predicted outcomes. We have proposed 
61
affect the understanding of predicted outcomes. We have proposed 
            
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hybrid AI methods that combine symbolic reasoning over knowledge 
62
hybrid AI methods that combine symbolic reasoning over knowledge 
            
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graphs (KGs), large language models (LLMs), and causal reasoning 
63
graphs (KGs), large language models (LLMs), and causal reasoning 
            
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techniques to infer causal relationships between patients' properties. 
64
techniques to infer causal relationships between patients' properties. 
            
65
As a result, a causal model is learned, enabling counterfactual 
65
As a result, a causal model is learned, enabling counterfactual 
            
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prediction to support clinical decisions. We apply these AI methods to 
66
prediction to support clinical decisions. We apply these AI methods to 
            
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predict the counterfactuals of biomarker results in non-small cell 
67
predict the counterfactuals of biomarker results in non-small cell 
            
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lung cancer (NSCLC) patients under hypothetical treatments with 
68
lung cancer (NSCLC) patients under hypothetical treatments with 
            
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different smoking habits. We have created synthetic datasets based on 
69
different smoking habits. We have created synthetic datasets based on 
            
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clinical records of NSCLC patients to evaluate the performance of the 
70
clinical records of NSCLC patients to evaluate the performance of the 
            
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proposed methods. The observed results suggest that our methods are 
71
proposed methods. The observed results suggest that our methods are 
            
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competitive with baseline methods in causal relationship discovery and 
72
competitive with baseline methods in causal relationship discovery and 
            
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counterfactual prediction. CCS CONCEPTS \u2022 Applied computing 
73
counterfactual prediction. CCS CONCEPTS \u2022 Applied computing 
            
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\u2192 Health care information systems; \u2022 Computing methodologies 
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\u2192 Health care information systems; \u2022 Computing methodologies 
            
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\u2192 Artificial intelligence.",
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\u2192 Artificial intelligence.",
            
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Technology, abbreviated TIB, is the national library of the Federal 
            
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Republic of Germany for all fields of engineering, technology, and the 
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Republic of Germany for all fields of engineering, technology, and the 
            
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  "title": "Hybrid AI Approach for Counterfactual Prediction over 
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Knowledge Graphs for Personal Healthcare",
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Knowledge Graphs for Personal Healthcare",
            
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nterfactual_Prediction_over_Knowledge_Graphs_for_Personal_Healthcare",
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