Survey on Knowledge Representation Models in Healthcare
Abstract
:1. Introduction
2. Background of Knowledge Concepts and Types
2.1. Data, Information, Knowledge and Wisdom
2.2. Knowledge Types
3. Knowledge Representation Models (KRMs)
3.1. Graphical Representation Models
3.2. Learning-Based Models
3.3. Rule-Based Models
3.4. Mathematical Models
3.5. Hybrid Models
4. Importance of Knowledge Representation Models in the Medical Domain
5. Requirements in the Medical Domain
6. Results and Discussion
7. Conclusions and Future Work
Funding
Conflicts of Interest
References
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Knowledge Types | Description | Examples |
---|---|---|
Explicit knowledge | A form of knowledge that can be expressed in various formats such as plain text, documents, spreadsheets, databases, images, etc., and can exist in structured or unstructured forms [10,11]. | Scientific formulas; recipes for a chef; books. For example: The capital of France is Paris. |
Implicit knowledge | Knowledge gained through incidental activities, or without awareness that learning is occurring [12]. | How to walk, run, ride a bicycle or swim. |
Tacit knowledge | This type of knowledge is subjective, based on personal experience, and often difficult to express. It resides in the human brain in an inexpressible form and is of an intellectual nature. This original form of knowledge is primarily obtained through learning and experiences [13]. | Doctor’s diagnosis; musician’s improvisation. |
Name | Age | Sex | Description | Temperature | Cough |
---|---|---|---|---|---|
Sami | 32 | Male | Patient | 38 °C | Yes |
Model | Advantages | Disadvantages |
---|---|---|
Naïve Bayes [39,40] | Simple; manages noisy data and missing values. | Inability to handle continuous features. |
Decision tree [41] | Intuitive interpretation; manages missing values. | Difficulty in designing large trees; low ability to manage noisy data. |
Random forest [42] | Ability to manage noisy data; suitable for large and heterogeneous data. | Requires more memory; difficult implementation and complicated in some cases; low ability to manage missing values. |
Bayesian network [43] | Handles missing or incomplete data; graphical structure facilitates interoperability. | Memory requirements increase with the number of variables. Large and complex networks may face scalability issues. |
Markov representation [44] | Ability to represent large data; robustness in handling noisy data. | Future transitions depend only on the current state, not on the entire history. This memoryless property can be limiting, especially when considering long-term effects or complex dependencies. |
UML [45,46] | Graphical representation for software design. | Becomes complex for large systems. |
Frame [47] | Hierarchical organization of knowledge; easy to construct. | Difficulty in representing complex relationships. |
Semantic network [48] | Effective when representing relationships between entities. | Faces challenges with large graphs. |
Ontology [49] | Structured representation of data; allows navigation of complex processes. Enables integration of domain knowledge for high-level reasoning. | Creating an ontology model can be time-consuming. |
First-order logic [50] | Expressive; handles complex relationships. | Faces challenges in representing certain types of data. |
Second-order logic [50] | Adds expressiveness over first-order logic. | Increased complexity in reasoning. |
Propositional logic [50] | Simple and easy to understand. | Limited expressiveness for complex relationships. |
Ref. | KRMs | Health Domain |
---|---|---|
[64] | Mathematical representation models | Disease diagnosis. |
[65] | Random forest | Treatment for rheumatoid arthritis. |
[66] | Random forest | Detection of inflammatory bowel disease. |
[67] | Decision tree | Patient and disease diagnosis. |
[68] | Ontology | Data representation in medical domain. |
[69] | K-nearest neighbours | Diagnosis and data in X-ray image. |
[70] | Neural network | Finding signs of diabetic retinopathy. |
[71,72] | Naïve Bayes | Data representation in congenital heart surgery. |
[73] | Decision tree | Evaluation of the risk of amputation in individuals with diabetic foot. |
[74] | Random forest | Antibody data representation in kidney transplantation. |
[75] | Naïve Bayes | Data representation from ultrasound images. |
[76] | Bayesian network | Used in treatment process. |
[77] | Decision tree | Specific details in the diagnosis process. |
[78] | K-nearest neighbours | Forecasting the risk of retinopathy. |
[79] | Random forest | Monitoring for school children. |
[80] | Decision tree | Detection of spread patterns in recent ischemic stroke. |
[81] | K-nearest neighbours | Detection of thyroid disease. |
[82] | Neural network | Used in cancer treatment using advanced therapy techniques. |
[83] | Markov model | Disease diagnosis using ECG. |
[84] | Decision tree | Patient monitoring. |
[85] | Neural network | Identification of coronary calcium. |
[86] | Fuzzy logic | Medical record representation for diagnosis prediction. |
[87] | Random forest | Diagnosing CKD disease. |
KRMs | Heterogeneity | Interpretability | Reasoning | Scalability |
---|---|---|---|---|
Bayesian network | Satisfy [93] | Satisfy [94] | Satisfy [93] | Partial satisfaction [94] |
Markov representation | Satisfy [75] | Partial satisfaction [95] | Satisfy [95] | Satisfy [96] |
Ontology | Satisfy [97] | Satisfy [97] | Satisfy [97] | Satisfy [98] |
UML | Satisfy [99] | Satisfy [99] | Partial satisfaction [100] | Partial satisfaction [99] |
Frame | Satisfy [101] | Satisfy [99] | Not satisfy [100] | Partial satisfaction [101] |
Tuple-based representation | Satisfy [102] | Satisfy [102] | Partial satisfaction [102] | Partial satisfaction [102] |
Semantic network | Satisfy [103] | Satisfy [103] | Partial satisfaction [104] | Partial satisfaction [104] |
First-order logic | Partial satisfaction [105] | Satisfy [105] | Satisfy [105] | Partial satisfaction [105] |
Second-order logic | Satisfy [106] | Satisfy [107] | Satisfy [106] | Partial satisfaction [106] |
Propositional logic | Partial satisfaction [106] | Satisfy [106] | Partial satisfaction [107] | Not satisfy [107] |
Rule-based system | Satisfy [108] | Satisfy [108] | Satisfy [109] | Partial satisfaction [108] |
Naïve Bayes | Partial satisfaction [110] | Partial satisfaction [110] | Satisfy [110] | Satisfy [110] |
Random forest | Satisfy [111] | Partial satisfaction [112] | Satisfy [112] | Satisfy [112] |
Decision tree | Satisfy [112] | Satisfy [111] | Satisfy [112] | Partial satisfaction [112] |
Neural network | Satisfy [113] | Not satisfy [114] | Satisfy [114] | Satisfy [114] |
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Msheik, B.; Adda, M.; Mcheick, H.; Dbouk, M. Survey on Knowledge Representation Models in Healthcare. Information 2024, 15, 435. https://doi.org/10.3390/info15080435
Msheik B, Adda M, Mcheick H, Dbouk M. Survey on Knowledge Representation Models in Healthcare. Information. 2024; 15(8):435. https://doi.org/10.3390/info15080435
Chicago/Turabian StyleMsheik, Batoul, Mehdi Adda, Hamid Mcheick, and Mohamed Dbouk. 2024. "Survey on Knowledge Representation Models in Healthcare" Information 15, no. 8: 435. https://doi.org/10.3390/info15080435