Using Modular Ontologies to Capture Causal Knowledge contained in Bayesian Networks

Hu, Hengyi; ElRafey, Amr; Kerschberg, Larry

doi:10.1145/3110025.3110136

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…In our research, we have captured patient symptoms in an ontology module [63], stored associations (non-causal) among symptoms as relationships in a modular ontology [64], created a CBN to demonstrate its compatibility and translation to an ontology [65], and proposed improvements to medical ontologies by analyzing causation in patient data [66]. We have also established a methodology that utilizes an AMO as prior causal knowledge for learning a CBN [67].…”

Section: H Prior Work In Causal Modeling Methodology and Generalizabi...mentioning

confidence: 99%

A Custom GPT Health Recommender System Informed by Causal Bayesian Networks and Authoritative Ontologies (Preprint)

Hu,

Kerschberg

2024

Preprint

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BACKGROUND ChatGPT and other large language models (LLMs) are trained on extensive text data; they learn patterns and associations within the training text without an inherent understanding of underlying causal mechanisms. Establishing causation necessitates controlled experiments and observations, and as of May 2024, ChatGPT lacks access to experimental data and the capacity to learn analytical models from data. Recent advancements from OpenAI enable the creation of custom Generative Pre-trained Transformers (GPT) models using their GPT Builder. These custom GPTs can be tailored with causal knowledge from Causal Bayesian Networks (CBN), thereby producing a knowledgeable, health-recommender system with causal expertise. This system is not only easily accessible for patients, clinicians and other users, but also has the potential to significantly improve healthcare outcomes. OBJECTIVE This paper presents a practical solution –a custom GPT model integrated with Causal Bayesian Networks (CBNs) informed by Authoritative Medical Ontologies (AMOs) as prior foundational knowledge. AMOs are robust biomedical ontologies that encapsulate the expert knowledge of their creators. By utilizing structured information contained within these ontologies, we can generate an informed CBN which can be used to improve a custom GPT as a health recommender system. These enhanced GPTs offer profound insights into cause-and-effect among co-morbid symptoms within the disease domain. METHODS To demonstrate our recommender system, we learn a CBN using NIMH data for patients of Alzheimer’s Disease and augment this with causal knowledge from the International Classification of Diseases Version 10 Clinical Modification (ICD-10-CM). We compute the CBN using the Max-Min Hill-Climbing (MMHC) algorithm. We generate two separate CBNs using MMHC to compare predictive accuracies between a baseline and a CBN modified with causal mechanisms from ICD-10-CM. Our previous research using this method has resulted in a modified CBN that reflects the causal claims in the AMOs and agrees with both the AMOs and the observational dataset. With this causal model, we build a custom GPT using OpenAI’s GPT Builder. RESULTS The custom GPT contains both potentially causal and correlations among symptoms, as well as conditional probabilities for these relationships. The GPT will also contain knowledge of causal mechanisms from ICD-10-CM, extended information regarding symptom variables, and references to existing literature regarding comorbid Alzheimer’s Disease symptoms. Furthermore, because the modified CBN model establishes potentially causal relationships among symptoms which can be verified in existing epidemiological research, we can verify that the custom GPT also establishes these causal relationships. This creates a GPT that agrees with the modified CBN, which is a representation of existing subject matter expertise in the disease domain. CONCLUSIONS To obtain our CBN, we’ve used a previous methodology which obtains ordered variable pairs from authoritative ontology ICD-10-CM as prior expertise. In our past research, a CBN that is learned using MMHC can be improved significantly by considering prior sources of knowledge, if the algorithm is modified appropriately. This source of prior knowledge can be validated in existing literature as a sequence of events in AD progression, specific causal mechanisms among comorbid symptoms, and conditional probabilities from a Bayesian Network. The resulting modified network provides insight into the causal relationships expressed in the AD data and takes advantage of the expertise and knowledge contained in the AMOs. Since inferring causality from a CBN does not exist in a vacuum, the relationships within the CBN, regardless of the strength of the conditional probabilities, must be explored further. A custom GPT extends the causal knowledge within a CBN with general knowledge of symptoms and diseases, providing a tool capable of suggesting causal inference based on the analysis of real patient data. With a LLM, uncertainty is present but reasoned with, as it is present but reasoned with in the prior, posterior, and likelihood in Bayes Theorem. Moving forward, we would like to explore using ChatGPT to produce ordered variable pairs and to automate the validation of potentially causal information.

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Section: H Prior Work In Causal Modeling Methodology and Generalizabi...mentioning

confidence: 99%

A Custom GPT Health Recommender System Informed by Causal Bayesian Networks and Authoritative Ontologies (Preprint)

Hu,

Kerschberg

2024

Preprint

Self Cite

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“…The work in (Hu et al, 2017) proposes a new methodology that uses an ontology to supply uncertain relationships among various random variables in the Bayesian network. For example for the patients of depression, the technique by learning Bayesian structures and probability tables from Treatment Alternatives patient datasets, was able to achieve high level of diagnosis accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Several cases are described as examples. The work in (Hu et al, 2017) describes a system for medical diagnosis that integrates ontology with Bayesian networks. Used ontologies are extended by domain experts to add uncertainty reasoning to them.…”

Section: Adopting Integrated Ontology/bayesian Algorithmsmentioning

confidence: 99%

Knowledge Based Bayesian Network Construction Algorithm for Medical Data Fusion to Enhance Services and Diagnosis

Sameh¹

2019

Journal of Computer Science

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Traditional Bayesian networks' algorithms are treating the network construction process as an isolated and autonomous data-driven trial-and-error process and completely ignoring the domain knowledge. In this work we are proposing a new 'Semantically Aware Ontology-Based Bayesian Network construction algorithm' that is knowledge centered instead of data centered. The objective of the new algorithm is to empower patients through improving their self-diagnosis and testing by automatically constructing a set of Ontology-Based Bayesian networks using combination of domain and expert knowledge. The exciting thing about the proposed algorithm is that it uses on 'Saudi-native training data' streamed from the "Unified Medical Record" server and authenticated domain and expert knowledge extracted from the "King Abdulla Encyclopedia" server. A proof-of-concept prototype based on open-source software "Netica" and "Protégé" is implemented and tested. It demonstrates learning of probabilities, network structure and mixes discrete and continuous variables. It imports "Diabetes" patient medical record steams from the "Unified Medical Record" server to be used as training and testing datasets. It also extracts Bayesian data variables from the "King Abdullah Encyclopedia" server to aid in constructing and learning the ontology-based Bayesian networks. The prototype is implemented on an Internet server and can be accessed from medical applications on Smartphones and PDAs. It currently deals with 60 positive "Diabetes" Saudi patients and 60 negative "Diabetes" training cases. The resulting Ontology Bayesian network was tested on another 100 test cases drawn randomly from the 'Unified Medical records' server. An accuracy of diagnosis of 100% was achieved on the test data.

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“…The use of a BbN in the risks analysis can provide explanations how the disadvantages of the opening of data are created. The BbN is able to capture the strength of causal links to define the cause under uncertainty variables [8,9], and to visualize the possible consequences of the risks [10]. The consequences of the risks are the disadvantages and the impact of our situation.…”

Section: Introductionmentioning

confidence: 99%

A Causal Explanatory Model of Bayesian-belief Networks for Analysing the Risks of Opening Data

Luthfi

Janssen

Crompvoets

2018

Lecture Notes in Business Information Processing

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Open government data initiatives result in the expectation of having open data available. Nevertheless, some potential risks like sensitivity, privacy, ownership, misinterpretation, and misuse of the data result in the reluctance of governments to 0understand the mechanisms resulting in risk when opening data. This study is aimed at developing a Bayesian-belief Networks (BbN) model to analyse the causal mechanism resulting in risks when opening data. An explanatory approach based on the four main steps is followed to develop a BbN. The model presents a better understanding of the causal relationship between data and risks and can help governments and other stakeholders in their decision to open data. We use the literature review base to quantify the probability of risk variables to give an illustration in the interrogating process. For the further study, we recommend using expert's judgment for quantifying the probability of the risk variables in opening data.

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Using Modular Ontologies to Capture Causal Knowledge contained in Bayesian Networks

Cited by 7 publications

References 13 publications

A Custom GPT Health Recommender System Informed by Causal Bayesian Networks and Authoritative Ontologies (Preprint)

A Custom GPT Health Recommender System Informed by Causal Bayesian Networks and Authoritative Ontologies (Preprint)

Knowledge Based Bayesian Network Construction Algorithm for Medical Data Fusion to Enhance Services and Diagnosis

A Causal Explanatory Model of Bayesian-belief Networks for Analysing the Risks of Opening Data

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