the aim of the invention is to give the patients controlled , individualised and situation - dependent medication advice , thereby to optimize the patient &# 39 ; s sense of well - being . the inventive method is adaptive , i . e the basic rules for the advice are altered if the relevant factors affecting the patient are changed . conveniently , the invention is implemented in portable &# 34 ; wallet - size &# 34 ; computers . as appears from the drawing , the method is founded on a knowledge - based computer system or expert system 6 communicates with , is associated with , and otherwise has access to a reminder function 1 , a recording and storage function 2 , 3 , as well as a function 4 for inductive rule generation . when the system is operating , the reminder function block 1 cause the computer to emit a signal when a pharmaceutical preparation is to be taken ( or some other activity is to be performed ), and which indicates the type of preparation to be taken as well as the dosage . the recording function block 2 of the system allows the patient to confirm that medicine has been taken , and to indicate his current state of health by responding to questions put to him by the computer . in addition , the patient records other relevant events , such as awakening , intake of food , exercise and stress , as they occur . these data are stored in a database 3 together with the point of time at issue . optionally , some of such data be automatically retrieved by sensors . the information stored in situation database 3 , is used to determine relevant relations in time between various events and to generate examples different symptoms are to be controlled . the examples consist of values for the relevant ( affecting factors ), as well as an associated symptom value ( conclusion ). the attributes used to determine each medication are , among others , &# 34 ; current dosage &# 34 ;, &# 34 ; time from intake &# 34 ;, and &# 34 ; remaining duration of action &# 34 ;. collections of examples are automatically created every day for each group of symptoms . these collections are accumulated in a &# 34 ; rolling &# 34 ; database , preferably including examples from the last 20 days . the examples are then used in each group of symptoms for spotting , with the aid of prior - art inductive data analysis 4 , the relationships that exist , and drafting rules for predicting the effect on the symptoms by various dosages of medications . inductive data analysis and rule generation are described in , inter alia , the following publications : quinlan , j . r . ( 1979 ), discovering rules by induction from large collections of examples , introductory readings in expert systems ( d . michie ), pp 33 - 46 , london ; gordon and breach 1979 . quinlan , j . r . ( 1983 ), learning from noisy data , proceedings of the international machine learning workshop , university of illinois , pp 58 - 64 , 1986 . the prediction rules are generated in the form of decision trees and / or so - called conditional rules of the type : if x1 & lt ; 0 . 12 and z3 =&# 34 ; protein b &# 34 ; and z1 & gt ; 0 . 34 and z2 = 2 then rigidity =&# 34 ; excessive agility &# 34 ;. the rules are validated at testing block 5 by comparing the proposed rules to known thresholds drawn from long - term experience . approved rules are transferred to the knowledge base of expert system 6 in known manner . the expert system may be a conventional rule - based system or be based on fuzzy logic . the approved rules stored in expert system 6 can predict a patient &# 39 ; s overall state of health with predictions of the various symptoms in a specific situation . when in use , the expert system 6 is automatically accessed in indicated at 8 at regular time intervals , preferably every five minutes . when called , the rules stored in system are consulted using the current values from the situation database 3 . during consultation , time data are preferably delayed by a expected elapsed time factor corresponding to the time from the intake of a medication to the attainment of its biophase . this enables the system to signal before any undesirable effects appear ( early warning system ). the consultation is performed in known manner via &# 34 ; backward chaining &# 34 ;, i . e . starting from a desirable conclusion ( state of health ). desirable conclusions can be predetermined for every group of symptoms . the expert system 6 operates in real time and can , in each specific situation , quickly determine the prerequisites and conditions necessary to achieve the desired state of health . if this state cannot be achieved in a specific situation ( e . g . because the patient has forgotten to act on previous advice ), the system looks for the prerequisites for attaining a &# 34 ; second - best &# 34 ; state of health . induction of rules and updating of the knowledge base are performed automatically by the system , conveniently once every twenty - fourth hours . because the expert system 6 detects patterns by inductive analysis of real situations , individualised medication recommendations can be provided without requiring explicit causal connections or pharmacological data . the prerequisite being that the attributes used directly or indirectly represent patterns that can be classified . according to the invention , the patient records the point of time when he takes the various medications , the dosage , as well as his current state of health . these data are used for generating , by inductive data analysis , the prediction rules stored in expert system 6 that are used to provide advice on the point of time and the dosage . for each medication , one starts from a patient - specific longest nominal duration of action . in the examples , the remaining duration of action as well as the dosage of the current and the previous dosages are determined . other relevant factors , such as the intake of food , state of stress and related time , are also included in the examples . the prediction rules generated by prior - art inductive data analysis block 4 and stored in expert system 6 are called at predetermined intervals , and delayed by a time delay , which preferably corresponds to the elapsed time from intake to the attainment of the biophase . the prediction rules are called by backward chain , based on the current situation and the desired state of health . if any of the conditions that can be influenced is not satisfied , e . g . the dosage of a preparation , the system will recommend that this be done . a mode of operation of the invention , intended for controlled , individualised adaptive medication in the treatment of parkinson &# 39 ; s disease , will be described below . in parkinson &# 39 ; s disease , the body cannot produce sufficient amounts of a neurotransmitter in the brain called dopamine , which results in limitations of movement , muscular cramp and tremor . this state can be counteracted by the supply of various preparations , such as l - dopa , which are converted to dopamine or the substance bromokriptin . in order to master undesirable side - effects , such as effects on the blood pressure , these preparations generally have to be combined with other medicines . thus , medication frequently involves at least three different preparations , each having a specific dosage and a specific time interval between administrations . owing to the interactions taking place , the time intervals and the doses have to be adapted to one another . also external factors , such as the intake of food , exercise and state of stress , affect the action of the preparations . as a result , patients that have been ill for a long time , and thus have had plenty of time to find which medication suits them best , are seldom perfectly untroubled for a whole day . frequently , there are two or more periods during the day in which they experience considerable discomfort in the form of tremor , rigidity and &# 34 ; dullness of mind &# 34 ;. too low a dosage typically results in tremor , rigidity and muscular cramp . discomfort caused by overdosage manifests itself in the form of uncontrolled excessive agility , among other things . the reminder function block 1 in the inventive system indicates when the preparations , such as pravidel , sinemet , inderal , madopark , and eldepryl , are to be taken , and further provides recommendations as to the intake of liquid , food and rest . the recording function block 2 includes compulsory input of the state of health expressed as degrees of the attributes &# 34 ; rigidity &# 34 ;, &# 34 ; agility &# 34 ;, &# 34 ; tremor &# 34 ; and &# 34 ; dullness of mind &# 34 ;. recordation is also performed when other important events take place , such as &# 34 ; awakening &# 34 ;, &# 34 ; falling asleep &# 34 ;, &# 34 ; eating &# 34 ; and &# 34 ; drinking &# 34 ;. in recordation , time is indicated in minutes from awakening . when generating examples , the times elapsing from the intake of the various preparations and events to the present time are determined . two times are preferably determined for each preparation , namely the time that has elapsed from the previous intake and the time that has elapsed from the current intake . a nominal duration of action from the intake is used for the various preparations . when the individual duration of action is known , this is indicated . at the end of each day , a collection of examples with attribute values determined as above is created and stored in database 3 . the recorded state of health is indicated as a conclusion for each example . a collection of examples is thus made for each of the indicated states of health , i . e . one collection of examples with the conclusion related to &# 34 ; rigidity &# 34 ;, one collection related to &# 34 ; tremor &# 34 ;, and one collection related to &# 34 ; dullness of mind &# 34 ;. then , rules are induced from the collections of examples by inductive analysis block 4 , and the rules are validated ( in testing block 5 ) by comparing their threshold values with indicated maximum and minimum limits as to dosage and time . if a rule falls outside these limits , the indicated limit values are used . thereafter , the rules are transferred to the knowledge base in the deductive expert system 6 . the generation of examples , the induction and the transfer to the database are automatically initiated at the end of each day . when using the system , the reminder function block 1 is activated when the patient starts the computer upon awakening . the reminder function block 1 calls the expert system 6 at intervals of preferably about 5 minutes , and the expert system 6 is thus consulted with respect to the data recorded . in consultation , an advance time corresponding to the average time elapsing from the intake of a preparation to its commenced action , is added to the real time . preferably , the advance time is 30 min . consequently , the system will be able to issue reminders in good time .