Sorenson, Dean

Warner, Homer R.

English

The University of Utah: J. Willard Marriott Digital Library

GENERATION AND MAINTENANCE OF ILIADTm MEDICALKNOWLEDGE IN A HYPERCARD.m ENVIRONMENTDean Sorenson, Omar Bouhaddou, Wenshao Wang,Gerald Canfield, Lee-Shing Fu, & Homer R. WarnerDepartment of Medical InformaticsUniversity of UtahAbstractA comprehensive system has been con-structed to systematically and efficiently 1)design, translate, compile, and test newknowledge frames, and 2) update and facili-tate access to text frames, associated liter-ature citations, and statistical information.The availability of the knowledge buildingtools in a unified workstation environmenthas synergistically accelerated the process-of building, updating, and maintaining medi-cal knowledge bases.LntroductioQnAn unusual feature of the HELP system[1] as a hospital information system is itssignificant decision support component. Thisdecision support component (i.e., the knowl-edge base) is comprised of about 2000 deci-sion frames. Various versions of theseknowledge frames have been used on a dailybasis for more than 10 years as an integralpart of the medical decision making processat LDS hospital in Salt Lake City.In addition to the knowledge base asso-ciated with the HELP system (which ismaintained on a mainframe computer), an in-dependent knowledge base has more recentlybeen developed and implemented on Macin-tosh computers at the U. of Utah Dept. ofMedical Informatics. This knowledge basewas developed specifically for ILIAD [2] andis comprised of approximately 185 framesrepresenting relationships used for diagnos-ing diseases in four medical specialties(cardiovascular, pulmonary, gastrointestinal,and endocrine diseases). About 60% of theseframes are probabilistic (i.e., use Baysianinferencing) in nature. An average probabil-ity frame has about 20 findings, each with anassociated sensitivity (probability of thefinding being present in the disease popula-tion) and 1-specificity (probability of thefinding being present in the non-disease pop-ulation). These numbers are obtained fromthe patient data base, the literature, or fromexpert estimates.Two additional subknowlege bases innephrology and hematology will be completedby August 88. Several others are under de-velopment.The construction of knowledge frameshas now been formalized and partially semi-automated. The process involved is the sub-ject of this paper.Knowledge Frame ConstructionKnowledge frames are constructedthrough the process of knowledge engineer-ing. This process involves:* the identification of the most appro-priate subset of findings necessary and suf-ficient to make a diagnosis or other medicaldecision* a decision as to whether the framesshould be probabilistic or deterministic* identification of non-independence* a decision as to whether the framesshould be all individual items or whetherthere should be clusters [3] of findings(clusters are a strategem designed to abro-gate lack of independence and to groupsymptoms with common pathophysiologicalprocesses or mechanisms, thereby increasingthe educational value of the frames)* assignment of probabilities to the in-dividual items* testing item probabilities relative to* testing the medical logic of the framerelative to other diseases which share someof the findings of that frame3660195-4210/88/0000/0366$01.00 0 1988 SCAMC, Ihc.The knowledge engineering process hasbeen recently formalized at the University ofUtah. This process involves regular meetingsbetween domain experts, knowledge engi-neers, data base experts, library scientists,computer scientists, and graduate studentsin medical informatics. A dynamic record ofthe proceedings are maintained using a com-puter application called KESS previously de-scribed at the SCAMC87 meetings [4].Integration of the knowledge framesinto a specific educational or clinical envi-ronment requires input from additional ex-perts trained in statistics, sociology, andthe cognitive sciences as well as educatorsand clinical supervisers. This information isassimilated during weekly meetings.The knowledge frames can be updated atany time as deficiencies are discovered inthe logic, additional findings are added,better estimates of the probabilities areobtained, or findings are "elsed" or combinedinto clusters. However, adding findings to agiven frame can require changes in the datadictionary. Since the ILIAD data dictionaryhas a hierarchical structure (necessary tofacilitate the sophisticated inferencing ca-pabilities of ILIAD), the addition of newterms must adhere to certain rules. Also,when new frames are built, the user must beable to access the existing data dictionary.In addition, it is desirable in this regard, toprovide access for computer-naive medicalpractitioners as well as veteran users.To facilitate these logistic considera-tions, an integrated computer system hasbeen developed for the generation and main-tenance of computerized medical knowledge.This system is comprised of the followingcomponents (Figure 1): dictionary manager,translater, compiler, data base, and testingutilities. Each of these components are fur-ther described below:The Dictionary ManagerThe HELP system data dictionary (PTXT)is a hierarchically arranged dictionary ofmedical terminology (e.g., all SNOMED terms)that uses numerical codes to represent thehierarchy. The ILIAD data dictionary is asubset of the HELP system data dictionary.Figure 1. Overall design and flow ofInformation between the various com-ponents of the ILIAD/HgperCard knowl-edge frame generation and maintenancesystem.The smaller size of this dictionary(presently only about 1500 terms) has facil-itated a substantial restructuring of the hi-erarchy to optimize it's use in the ILIAD en-vironment. This restructuring includedelimination of redundancy, adding terminol-ogy which makes each item stand alone(necessary in the ILIAD environment whereisolated questions are presented to the stu-dent), elimination of hierarchies that are notuseful for inferencing, addition of structuresthat are useful for inferencing (e.g., high-level nodes with terms such as abnormal orincreasing), and the storage of frequency ofoccurrence with corresponding dictionaryterms. The storing of probabilities (i.e., to-tal probability of a finding across all dis-eases) within the data dictionary, in turn,facilitates additional functionality such asthe use of partial information (see below),consistency checking (e.g., is the sum of theprobabilities of all the children in a mutuallyexclusive set less than or equal to that ofthe parent) and transportability of theknowledge base to other locations.The dictionary manager has the follow-ing functions: editing (i.e.., adding, deleting,and changing dictionary terms), inferencing,and consistency checking. A set of data ma-nipulation algorithms have been constructedto facilitate 1) transfer of data from appli-cation programs, 2) the location of appropri-ate storage space, 3) storage of data items,4) deletion of data items, and 5) the abilityto change data items. A keyword retrievaland template query system has been designedto maintain and make optimal use of the ex-isting structures in the expert system and,367at the same time, to provide a convenientuser interface.The TranslaterThe translater is a computer programwhich converts a nearly free-text version ofa knowledge frame to a representation whichcan be inputted to the compiler (see Figure2). This program is comprised of a lexicalanalyzer, parser, dictionary retrieval compo-nent, and precompiler. The lexical analyzerbreaks up a nearly free-text document intoits component parts: title, type, findings,probabilities, and logical statements.The parser is comprised of a retrievalmodule, logic editor, and precompiler. Theretrieval module deletes meaningless wordsfrom each statement and then uses themeaningful terms to find the appropriate hi-erarchical data dictionary terms. This pro-cess involves the use of a morphology algo-rithm which sequentially removes lettersfrom the end of each word and matches themto a suffix list. The resulting word stemsare then compared to a list of synonyms andwords in the data dictionary. The most ap-propriate term or terms from the data dic-tionary are then presented to the user forapproval. Rules are being developed to dis-ambiguate the appropriate terms when mul-tiple hits are made. The user also has theoption of retrieving the entire list if de-sired; then choosing the most appropriateterm.The parser has a logical editor compo-nent which puts the statements in the framein the appropriate order based on informationcontent. The importance of a given term in aprobability frame is a function of the ratioof the sensitivity to (1-specificity). Whenstatements are not independent, they mustbe grouped by OR or ELSE statements. In aninterpretation frame, the parenthesis mustbe automatically placed appropriately sothat the AND and OR statements make sense.Also, the appropriate negative logic is auto-matically produced from the positive logic.The parser generates the following out-put: 1) a code which is understandable by thecomputer programmer which involves search,arithmetic, and probability statements con-cerning existence, value, and time variables,2) the production of a numerical code whichcan be inputted to the compiler.The user interface is menudriven andfollows the usual Macintosh conventions.The construction of new frames is facili-tated by an interactive environment with ex-amples and dialogue boxes which inform theuser what he/she can or cannot do. The usualfunctions of cut, copy, paste, save, print,etc, are supported. When the text version iscompleted, the user directs the computer toparse the frame to generate a pre-compiledversion of the frame (Figure 2). The frame issubsequently compiled and can then be im-mediately tested in the ILIAD environment.* File Edit SgntaH CompileFRRME TEHTTITLE Duodenal ulcerTYPE probabilityAPRIORI .0058no diseaseFINDINGS diseasea. age 0-29 .01 .0930-59 .54 .3260+ .45 .59b. male sex .72 .57c. 17.143.114 Peptic..ulcer.pain .85 .02d. else, recent epigastric pain .95 .15e. nausea or vomiting .05 .150O RESULTBLOCK ?7.143SECTOR 10 -.---DUODENAL ULCERSECTOR LOGIC:A ARITH: 0.0056B SEARCH: AGEMODIFIER: LAST IF: a"exIst FROM: $MOWTO: $1OW - 2 DAYS USE: aAvalC PROB: RPRIORI: RAVAL IF BVRIL>OUSE: B^AVL MIN(O, 30, 60)TRUE(O.0100, 0.5400, 0.4500) FALSE(O.0900,0.3200, 0.5900)D SERRCH: WHAT IS YOUR SEX ?MODIFIER: LAST IF: a'existFROM: $1OW - 2 DRYS TO: $1OW USE: a'valFigure 2. Free text version of aknowledge frame (in proper format)and the corresponding precompiledversion generated by the translater.The CompilerThe Macintosh compiler is a "C" programthat converts the precompiler version of theframes to machine-readable code (pcode).The following data files are generated: framefile, item file, code file, rank file, dictionary368file, and keyword file. The frame file con-tains the compiled version of the knowledgeframes and the dictionary file contains theILIAD data dictionary hierarchical codes.The item file contains a list of the items inthe frames and the links between items inthe same frame. The. keyword file allowsdictionary items to be accessed by the userduring runtime. The code file contains thelinks between a given finding and all theframes that use it. The rank file ordersitems by information content (i.e., a functionof sensitivity/(1-specificity)) and cost.Also, the system uses this file to keep trackof what questions have been asked duringruntime.The compiler also generates a truthtable which allows the status of every itementered while using ILIAD to be tracked. Thestatus is determined by the existence, value,time, and time limit of each item entered.HyLercardHypercardTN is a Macintosh applicationwith likens each record to a "card", with setsof cards being contained within "stacks"(analogous to files in a file drawer). Graph-ics and text can be freely mixed on cards.Cards and stacks are accessed by clicking a"button" or selecting a menu item on the"Home card" with the "mouse". Each stackhas a top directory card which facilitatesaccess to the other cards in the stack. Thepreviously described parts of the ILIADknowledge base generation and maintenancesystem (except for the KESS system which isin Hypercard7m) are "C" programs which areaccessed via a Hypercardml interface. Thedata base and evaluation tools for the sys-tem are all in Hypercard'm.The Data BaseThe data base is wholely containedwithin Hypercard'w. The data base is com-prised of the following stacks: text frames,findings, statistics, and literature. The textframes component contains each of theframes in scrollable fields on separatecards. These cards are accessed by selectinga title in a scrollable directory and clickinga button with the mouse. Clusters can be ac-cessed from each frame by the same hyper-text mechanism. Clicking another buttonreturns the user to the original frame card.The relevent contents of each probabilitytext frame can be transferred to a Baye'stesting card (see below) by clicking a button.Another option takes the user to a card in thestatistics stack delineating the source ofthe statistics used in the frame.The first card of the findings stackcontains a scrollable list of all of the find-ings contained in the ILIAD frames. Clickingthe name of a finding with the mouse bringsup a list of all of the frames which use thatparticular finding.The literature stack contains a scrol-lable list of keywords in the directory card.Buttons and menu selections then take theuser to the desired citations or retrieve alist of citations according to a set Booleancriteria.The Bae's CardIn the case of probabilistic frames, thesymptoms of a disease are assigned associ-ated numerical values (i.e., sensitivities andspecificities). ILIAD uses these numericalweights in a Bayesian framework to calcu-late a posterior probability of a given dis-ease. To help the knowledge engineering teamto intuitively comprehend the impact of eachsymptom's participation in the final poste-rior probability of a disease, the "Baye's cardapproach" was developed. This HyperCard7mstack lets the user "import" a free-text for-mat of an ILIAD knowledge frame and simu-late a status (e.g., present, absent, or un-known) for each manifestation of the illness.Another button allows the knowledge engi-neer to calculate the likelihood of the dis-easeO for the set of simulated patient obser-vations. If the posterior probability of thedisease is not in the range of that expectedby an expert, given the patient case, then theappropriate probability estimates are ad-justed until a satisfactory behavior evolves.Medical experts participating in the knowl-edge engineering sessions have found thistool very useful since it allows them to fo-cus on the relative contribution of an item,rather than on individual numerical weights.This is a method of adjusting the overall re-liability of the decision model in a manner369analogous to tuning a radio without having tobe concerned with the individual frequencies.An additional option of the Baye's card,found to be useful in terms of evaluation ofthe model, is a stochastic simulation mode.In this mode, the presence of a disease is as-sumed, findings are randomly generated, andthe corresponding posterior probabilities arecalculated. This process is repeated manytimes and the results plotted. When most ofthe results fall close to 1.0, it may be as-sumed that the disease is well described bythe corresponding set of findings and as-signed weights. However, there are caseswhere the interpretation is not straightfor-ward, because of a need to use nonspecificfindings (i.e., findings common to many dis-eases) or when there are few known findingsassociated with a disease.A version of the Baye's card has alsobeen constructed for the testing and evalua-tion of interpretation type knowledgeframes. This version is based on a newly de-veloped algorithm for ranking non-proba-bilistic findings [2].DiscusnOur previous experience with theknowledge engineering process has shownthat the process can be incredibly inefficientand prone to errors if done on a piecemealbasis. This is because of the multidisci-plinary nature of the process where a numberof individuals from different departments,with different time requirements, are con-tributing to the process. The frame transla-tion process is especially prone to errors,which are sometimes very difficult to trackdown, since the process is tedious and diffi-cult to standardize, and is likely to be doneby persons who wouldn't recognize certainmedical nuances. These considerationsprompted the design of a semi-automated,modularized, yet integrated, system to fa-cilitate the process. By automating this pro-cess, the turnaround time is greatly short-ened, since a frame can be designed, trans-lated, compiled, and tested entirely during asingle session while all of the pertinent in-formation fresh in the minds of the design-ers.k In order to ensure optimal design of thedata dictionary, we found it necessary toestablish formal fules for the structure ofthe data dictionary, and to assign a datadictionary manager for this purpose. Like-wise, a data base manager was assigned theresponsibility of maintaining the data baseand making all necessary changes.The establishment of a central database greatly reduces inefficiency, duplica-tion, and loss of time and information. Forexample, it is frequently necessary to referto previous frames when building newframes so as to prevent duplication of effortand to facilitate consistency in the assignedprobabilities. In terms of documentation,the statistics and literature parts of thedata base are essential.We have found that the testing of theprobability frames in the Bayes card envi-ronment invariable improves the perfor-mance and reliability of incipient frames. Itis substantially more efficient at this stagethan the succeeding one (in the ILIAD envi-ronment) where there are the additionalcomplexities of multiple frames present.References[1] Pryor, TA, Gardner RM, Clayton PD &Warner HR. The HELP system. J. MedicalSystems 7:87-102 (1983).[2] Warner HR, Haug P, Lincoln M, Warner Jr.H, Sorenson, D, Fan C, Bouhaddou 0, &Williamson J. ILIAD: A computer aided pro-gram for teaching differential diagnosis tomedical student. In press (SCAMC 1988).[3] Lincoln M, Turner C, Hesse B, Warner HR,& Miller R. Discovering clustered diseasefindings: prospects for enhancing expert[4] Ben Said M, Dougherty N, Anderson C,Altman SJ, Bouhaddou 0, Warner HR. KESS:Knowledge engineering support system. Pro-ceed. 11th Sympos. Computer Applications inMedicine 56-59 (1987).370

Generation and Maintenance of ILIAD Medical Knowledge in a Hypercard Environment

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Generation and Maintenance of ILIAD Medical Knowledge in a Hypercard Environment

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