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  "author": "Hao Huang",
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  "author": "Hao Huang",
            
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  "name": 
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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) 
            
53
has become an invaluable tool in health-care for disease prediction 
53
has become an invaluable tool in health-care for disease prediction 
            
54
and diagnosis. Despite their predictive accuracy, AI models may ignore 
54
and diagnosis. Despite their predictive accuracy, AI models may ignore 
            
55
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 
            
58
characteristics limits their ability to perform counterfactual 
58
characteristics limits their ability to perform counterfactual 
            
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reasoning about the predicted characteristics of a patient when 
59
reasoning about the predicted characteristics of a patient when 
            
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dependent characteristics change. This limitation of AI models can 
60
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 
            
62
hybrid AI methods that combine symbolic reasoning over knowledge 
62
hybrid AI methods that combine symbolic reasoning over knowledge 
            
63
graphs (KGs), large language models (LLMs), and causal reasoning 
63
graphs (KGs), large language models (LLMs), and causal reasoning 
            
64
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 
            
66
prediction to support clinical decisions. We apply these AI methods to 
66
prediction to support clinical decisions. We apply these AI methods to 
            
67
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 
            
71
proposed methods. The observed results suggest that our methods are 
71
proposed methods. The observed results suggest that our methods are 
            
72
competitive with baseline methods in causal relationship discovery and 
72
competitive with baseline methods in causal relationship discovery and 
            
73
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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  "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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  "type": "dataset",
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nterfactual_Prediction_over_Knowledge_Graphs_for_Personal_Healthcare",
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