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A temporal prognostic model based on dynamic Bayesian networks: mining medical insurance data
A prognostic model is a formal combination of multiple predictors from which risk probability of a specific diagnosis can be modelled for patients. Prognostic models have become essential instruments in medicine. The models are used for prediction purposes of guiding doctors to make a smart diagnosi...
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| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2021
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| _version_ | 1867613923208855552 |
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| access_status_str | Open Access |
| author | Mbaka, Sarah Kerubo |
| author2 | Ngwenya, Mzabalazo |
| author_browse | Mbaka, Sarah Kerubo Ngwenya, Mzabalazo |
| author_facet | Ngwenya, Mzabalazo Mbaka, Sarah Kerubo |
| author_sort | Mbaka, Sarah Kerubo |
| collection | Thesis |
| description | A prognostic model is a formal combination of multiple predictors from which risk probability of a specific diagnosis can be modelled for patients. Prognostic models have become essential instruments in medicine. The models are used for prediction purposes of guiding doctors to make a smart diagnosis, patient-specific decisions or help in planning the utilization of resources for patient groups who have similar prognostic paths. Dynamic Bayesian networks theoretically provide a very expressive and flexible model to solve temporal problems in medicine. However, this involves various challenges due both to the nature of the clinical domain, and the nature of the DBN modelling and inference process itself. The challenges from the clinical domain include insufficient knowledge of temporal interactions of processes in the medical literature, the sparse nature and variability of medical data collection, and the difficulty in preparing and abstracting clinical data in a suitable format without losing valuable information in the process. Challenges about the DBN methodology and implementation include the lack of tools that allow easy modelling of temporal processes. Overcoming this challenge will help to solve various clinical temporal reasoning problems. In this thesis, we addressed these challenges while building a temporal network with explanations of the effects of predisposing factors, such as age and gender, and the progression information of all diagnoses using claims data from an insurance company in Kenya. We showed that our network could differentiate the possible probability exposure to a diagnosis given the age and gender and possible paths given a patient's history. We also presented evidence that the more patient history is provided, the better the prediction of future diagnosis. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/33856 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:43:51.527Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/33856 A temporal prognostic model based on dynamic Bayesian networks: mining medical insurance data Mbaka, Sarah Kerubo Ngwenya, Mzabalazo Data Science A prognostic model is a formal combination of multiple predictors from which risk probability of a specific diagnosis can be modelled for patients. Prognostic models have become essential instruments in medicine. The models are used for prediction purposes of guiding doctors to make a smart diagnosis, patient-specific decisions or help in planning the utilization of resources for patient groups who have similar prognostic paths. Dynamic Bayesian networks theoretically provide a very expressive and flexible model to solve temporal problems in medicine. However, this involves various challenges due both to the nature of the clinical domain, and the nature of the DBN modelling and inference process itself. The challenges from the clinical domain include insufficient knowledge of temporal interactions of processes in the medical literature, the sparse nature and variability of medical data collection, and the difficulty in preparing and abstracting clinical data in a suitable format without losing valuable information in the process. Challenges about the DBN methodology and implementation include the lack of tools that allow easy modelling of temporal processes. Overcoming this challenge will help to solve various clinical temporal reasoning problems. In this thesis, we addressed these challenges while building a temporal network with explanations of the effects of predisposing factors, such as age and gender, and the progression information of all diagnoses using claims data from an insurance company in Kenya. We showed that our network could differentiate the possible probability exposure to a diagnosis given the age and gender and possible paths given a patient's history. We also presented evidence that the more patient history is provided, the better the prediction of future diagnosis. 2021-09-13T08:30:34Z 2021-09-13T08:30:34Z 2021 2021-09-10T07:25:25Z Master Thesis Masters MSc http://hdl.handle.net/11427/33856 eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | Data Science Mbaka, Sarah Kerubo A temporal prognostic model based on dynamic Bayesian networks: mining medical insurance data |
| thesis_degree_str | Master's |
| title | A temporal prognostic model based on dynamic Bayesian networks: mining medical insurance data |
| title_full | A temporal prognostic model based on dynamic Bayesian networks: mining medical insurance data |
| title_fullStr | A temporal prognostic model based on dynamic Bayesian networks: mining medical insurance data |
| title_full_unstemmed | A temporal prognostic model based on dynamic Bayesian networks: mining medical insurance data |
| title_short | A temporal prognostic model based on dynamic Bayesian networks: mining medical insurance data |
| title_sort | temporal prognostic model based on dynamic bayesian networks mining medical insurance data |
| topic | Data Science |
| url | http://hdl.handle.net/11427/33856 |
| work_keys_str_mv | AT mbakasarahkerubo atemporalprognosticmodelbasedondynamicbayesiannetworksminingmedicalinsurancedata AT mbakasarahkerubo temporalprognosticmodelbasedondynamicbayesiannetworksminingmedicalinsurancedata |