Enabling the use of hereditary information from pedigree tools in medical knowledge-based systems

The use of family information is a key issue to deal with inheritance illnesses. This kind of information use to come in the form of pedigree files, which contain structured information as tree or graphs, which explains the family relationships. Knowledge-based systems should incorporate the information gathered by pedigree tools to assess medical decision making. In this paper, we propose a method to achieve such a goal, which consists on the definition of new indicators, and methods and rules to compute them from family trees. The method is illustrated with several case studies. We provide information about its implementation and integration on a case-based reasoning tool. The method has been experimentally tested with breast cancer diagnosis data. The results show the feasibility of our methodology.

[1]  N. Morton.  Genetic epidemiology , 1997, International Journal of Obesity.

[2]  John B. Cole,et al.  Application note: PyPedal: A computer program for pedigree analysis , 2007 .

[3]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[4]  G. Malécot,et al.  Les mathématiques de l'hérédité , 1948 .

[5]  B. Achinstein,et al.  Journal of the National Cancer Institute, Vol. 29, 1962: Action of bacterial polysaccharide on tumors. II. Damage of sarcoma 37 by serum of mice treated with Serratia marcescens polysaccharide, and induced tolerance. , 2009, Nutrition reviews.

[6]  D. Boichard,et al.  The value of using probabilities of gene origin to measure genetic variability in a population , 1997, Genetics Selection Evolution.

[7]  Palestrini Clara.  Membro dell’Editorial Board di Journal of Veterinary Behavior: Clinical Applications and Research , 2005 .

[8]  Wylie Burke,et al.  Genetic risk in context: calculating the penetrance of BRCA1 and BRCA2 mutations. , 2002, Journal of the National Cancer Institute.

[9]  D. Easton,et al.  The BOADICEA model of genetic susceptibility to breast and ovarian cancer , 2004, British Journal of Cancer.

[10]  Terryl L. Givens.  The Church of Jesus Christ of Latter-day Saints , 2006 .

[11]  R. Pearl.  Biometrics , 1914, The American Naturalist.

[12]  Geoffrey J. Gordon,et al.  Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings , 2019, Lecture Notes in Computer Science.

[13]  Academic Press,et al.  Computers and biomedical research , 1967 .

[14]  M. Gail,et al.  Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. , 1989, Journal of the National Cancer Institute.

[15]  Beatriz López,et al.  eXiT*CBR: A framework for case-based medical diagnosis development and experimentation , 2008, Industrial Conference on Data Mining - Posters and Workshops.

[16]  D. Berry,et al.  Probability of carrying a mutation of breast-ovarian cancer gene BRCA1 based on family history. , 1997, Journal of the National Cancer Institute.

[17]  Vicenç Torra,et al.  Modeling decisions - information fusion and aggregation operators , 2007 .

[18]  R. Kerber,et al.  Method for calculating risk associated with family history of a disease , 1995, Genetic epidemiology.

[19]  L. Rumble,et al.  The Church of Jesus Christ of Latter Day Saints , 1967 .

[20]  Daniel Schmoldt,et al.  Computers and Electronics in Agriculture , 2017 .

[21]  O. Aalen.  Modelling the influence of risk factors on familial aggregation of disease. , 1991, Biometrics.

[22]  Beatriz Lpez,et al.  Case-Based Reasoning: A Concise Introduction , 2013, Case-Based Reasoning.

[23]  Jack Cuzick,et al.  A breast cancer prediction model incorporating familial and personal risk factors , 2012 .

[24]  M H Skolnick,et al.  Calculation of risk factors and likelihoods for familial diseases. , 1976, Computers and biomedical research, an international journal.

[25]  Vicenç Torra,et al.  Modeling Decisions: Information Fusion and Aggregation Operators (Cognitive Technologies) , 2006 .