title: [Self-instruction expert systems in medicine. Presentation of knowledge, acquisition of knowledge, prediction of suicide as an example].
abstract: OBJECTIVE
  1. Description of how self-learning expert systems work, and 2. comparison of various algorithms for the establishment of a data base for suicide prediction.
  from exemplary patient data, self-learning expert systems obtain their expert knowledge which they subsequently employ to establish the diagnosis in new patients. Various possibilities of storing knowledge may be employed, for example the decision tree, classes of rules or neuronal networks. The various forms of representing knowledge are also presented. Taking the prediction of suicidal risk as an example, the effectiveness of the algorithm is tested with the aid of a patient questionnaire.
  
  
  CONCLUSIONS
  Different fields of application require different systems. The diagnosis of such expert systems can at least prompt the care-providing physician to reconsider his own diagnosis.
authors.affiliation: Psychiatrische Klinik mit Poliklinik, Universität Erlangen-Nürnberg.
authors.forename: F
authors.initials: F
authors.lastname: Fischer
authors.forename: R
authors.initials: R
authors.lastname: Kalb
mesh_terms: D001185:Artificial Intelligence; D003194:Computer-Assisted Instruction; D005103:Expert Systems; D006801:Humans; D012307:Risk Factors; D013405:Suicide / Q000523:psychology*; D000092864:Suicide Prevention