Fuzzy-probabilistic multi agent system for breast cancer risk assessment and insurance premium assignment

Tatari, Farzaneh; Akbarzadeh-T, Mohammad-R.; Sabahi, Ahmad

doi:10.1016/j.jbi.2012.05.004

Cited by 26 publications

(19 citation statements)

References 57 publications

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“…Tatari et al 26 used multi-agent fuzzy systems to identify patients with a high risk of breast cancer. Lehman et al 64 carried out topic modeling, using hierarchical Dirichlet processes on unstructured notes of patients in an intensive care unit to group them into interesting categories such as ‘on ventilator’, ‘post-cardiac surgery’, and ‘trauma’, among others.…”

Section: Resultsmentioning

confidence: 99%

A review of approaches to identifying patient phenotype cohorts using electronic health records

Shivade

Raghavan

Fosler‐Lussier

et al. 2014

J Am Med Inform Assoc

427

319

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ObjectiveTo summarize literature describing approaches aimed at automatically identifying patients with a common phenotype.Materials and methodsWe performed a review of studies describing systems or reporting techniques developed for identifying cohorts of patients with specific phenotypes. Every full text article published in (1) Journal of American Medical Informatics Association, (2) Journal of Biomedical Informatics, (3) Proceedings of the Annual American Medical Informatics Association Symposium, and (4) Proceedings of Clinical Research Informatics Conference within the past 3 years was assessed for inclusion in the review. Only articles using automated techniques were included.ResultsNinety-seven articles met our inclusion criteria. Forty-six used natural language processing (NLP)-based techniques, 24 described rule-based systems, 41 used statistical analyses, data mining, or machine learning techniques, while 22 described hybrid systems. Nine articles described the architecture of large-scale systems developed for determining cohort eligibility of patients.DiscussionWe observe that there is a rise in the number of studies associated with cohort identification using electronic medical records. Statistical analyses or machine learning, followed by NLP techniques, are gaining popularity over the years in comparison with rule-based systems.ConclusionsThere are a variety of approaches for classifying patients into a particular phenotype. Different techniques and data sources are used, and good performance is reported on datasets at respective institutions. However, no system makes comprehensive use of electronic medical records addressing all of their known weaknesses.

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Section: Resultsmentioning

confidence: 99%

A review of approaches to identifying patient phenotype cohorts using electronic health records

Shivade

Raghavan

Fosler‐Lussier

et al. 2014

J Am Med Inform Assoc

427

319

View full text Add to dashboard Cite

show abstract

“…Researchers have experimented with several ML models including probability [61, 70, 71], decision tree [58, 72, 73], discriminant [54, 70] and other types of ML models [52, 74, 75] to build phenotype categorization systems. In each case, the models essentially approach phenotyping as a classification or categorization problem with a positive class (the target phenotype) and a negative class (everything else).…”

Section: 3 Phenotype Algorithm Developmentmentioning

confidence: 99%

Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health

Simmons

Singhal

2016

Advances in Experimental Medicine and Biology

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The key question of precision medicine is whether it is possible to find clinically actionable granularity in diagnosing disease and classifying patient risk. The advent of next generation sequencing and the widespread adoption of electronic health records (EHRs) have provided clinicians and researchers a wealth of data and made possible the precise characterization of individual patient genotypes and phenotypes. Unstructured text — found in biomedical publications and clinical notes — is an important component of genotype and phenotype knowledge. Publications in the biomedical literature provide essential information for interpreting genetic data. Likewise, clinical notes contain the richest source of phenotype information in EHRs. Text mining can render these texts computationally accessible and support information extraction and hypothesis generation. This chapter reviews the mechanics of text mining in precision medicine and discusses several specific use cases, including database curation for personalized cancer medicine, patient outcome prediction from EHR-derived cohorts, and pharmacogenomic research. Taken as a whole, these use cases demonstrate how text mining enables effective utilization of existing knowledge sources and thus promotes increased value for patients and healthcare systems. Text mining is an indispensable tool for translating genotype-phenotype data into effective clinical care that will undoubtedly play an important role in the eventual realization of precision medicine.

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“…Health condition parameters may be examined for mortality forecasting and assessing risks of serious diseases (e.g. breast cancer and cardiovascular diseases) using intelligent methods such as neural networks (Shah and Guez, ) and fuzzy approaches (Baser et al, ; Tatari et al, ). In (Byczkowska‐Lipińska et al, ), a rule‐based expert system evaluates (medical and life) insurability taking into consideration the health condition, profession and hobbies.…”

Section: Related Workmentioning

confidence: 99%

Assessment of life insurance applications: an approach integrating neuro‐symbolic rule‐based with case‐based reasoning

Prentzas

Hatzilygeroudis

2015

Expert Systems

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Assessment of applications for life insurance is an important task in the insurance sector that concerns estimation of potential risks underlying an application, if accepted. This task is accomplished by specialized personnel of insurance companies. Because of recent financial crises, this task is more demanding, and intelligent computer‐based methods could be employed to assist. In this paper, we present an intelligent approach to assessment of life insurance applications, which is based on an integration of neurule‐based with case‐based reasoning. Neurules are a type of neuro‐symbolic rules that combine a symbolic (production rules) and a connectionist (adaline unit) representation. A characteristic of neurules is that in contrast to other hybrid neuro‐symbolic approaches, they retain the naturalness and modularity of symbolic rules. Neurules are produced from available symbolic rules that represent general knowledge, which however do not completely cover the domain. We use health condition, age, gender, annual income, profession, insurance type and primary life insurance benefit as assessment parameters used in rule conditions. The integration of neurules and cases employs different types of indices for the cases according to different roles they play in neurule‐based reasoning. This results in its accuracy improvement. Experimental results demonstrate the effectiveness of the approach.

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Fuzzy-probabilistic multi agent system for breast cancer risk assessment and insurance premium assignment

Cited by 26 publications

References 57 publications

A review of approaches to identifying patient phenotype cohorts using electronic health records

A review of approaches to identifying patient phenotype cohorts using electronic health records

Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health

Assessment of life insurance applications: an approach integrating neuro‐symbolic rule‐based with case‐based reasoning

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