C. DiMarco, G. Hirst, L. Wannet, and J. Wilkinson. Healthdoc: Customizing patient information and health education by medical condition and personal characteristics. In Proceedings of the Workshop on Patient Education, Glasgow, 1995.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....loosens, or fails. If replacement is needed, it will have to be removed. 3) Removal of redundancy (aggregation) In some instances, an implant wears out, loosens, or fails, and [ will have to be removed. In this paper we describe the sentence plan ner in the HealthDoc project. HealthDoc [DiMarco et al. 19951 was established in early 1995 with the goal of generating customized patient education documents. It combines existing generation technology the sentence gen erator K ML [Bateman, 1995] and its input notation srL [Kasper, 1989] and new systems, such as the sentence planner described here. The ....

C. DiMarco, G. Hirst, L. Wannet, and J. Wilkinson. Healthdoc: Customizing patient information and health education by medical condition and personal characteristics. In Proceedings of the Workshop on Patient Education, Glasgow, 1995.

....GridBagConstraints.NORTHWEST,0.0,1. 0,1,1,1,1) this.validate( this.repaint( public void cardsthis(CardPanel cardthis) cards = cardthis; PanelGoto(UserModel UModel, int[ cnter) UM = UModel; counter = cnter; gridbag = new GridBagLayout( cbg = new CheckboxGroup( cbox = new Checkbox[6]; The array has the total number of panels, 6 in this program. counter[0] 2; cbox[0] new Checkbox( Demographics ,cbg,false) cbox[1] new Checkbox( Immunizations ,cbg,false) cbox[2] new Checkbox( Illnesses ,cbg,false) cbox[3] new Checkbox( Illnesses of Blood Relatives ,cbg,false) ....

....of BPH. 10 percent of them have prostate cancer. else printf( 50 percent of men over 54 show symptoms of BPH. 10 percent of them have prostate cancer. printf( Would you like to read about A HREF=http: www.cs. uwm.edu alp LEAFCGI BPH def.html BPH A P ) else if ( strcmp(cgivars[6], sympbph ) printf( Would you like to read about A HREF=http: www.cs.uwm.edu alp LEAFCGI BPH def.html BPH A P ) printf( P You can find more medical information in these links: printf( UL ) printf( LI A HREF=http: www.slackinc.com matrix Medical Matrix A ) printf( LI A ....

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DiMarco, Chrysanne; Hirst, Graeme; Wanner, Leo; and Wilkinson, John. August 1995. HealthDoc: Customizing Patient Information and Health Education by Medical Condition and Personal Characteristics. Workshop on Artificial Intelligence in Patient Education. Glasgow.

....The most significant exception was Komet ( BT95, TB94] 3 See figures 2 and 3 for individual references to systems. System GIST [Con96, PC96] Drafter [PVF 95] Drafter2 [PS98, SPE98] Pat Claim [SN96] Joyce [KKR91, Ram] ModEx [LRR97, LR97] Exclass [CK94] HealthDoc [DHW95, HDHP97] Ghostwriter [MCM96, Cer96] Content Determination User interface Strategic planner User interface Strategic planner Authoring tool Authoring module Text planner Text planner Text planner Job desc builder Authoring tool Selection of content Text planner ....

C. DiMarco, G.and L. Wanner Hirst, and J. Wilkinson. Healthdoc: Customizing patient information and health education by medical condition and personal characteristics. In First International Workshop on Artificial Intelligence in Patient Education, Glasgow, August 1995.

.... Others such as (Meteer, 1991) Inui et al. 1992) de Rosis et al. 1997) and (Robin, 1994) have proposed a revision based approach to generation whereby a complete draft plan is created and then subsequently improved (this is similar to the select and repair method of the HealthDoc system (DiMarco et al. 1995), Hirst et al. 1997) Finally, work founded on systemic functional grammars should also be included, though as Hovy points out in (Cole et al. 1995) much of it is focused upon single sentence generation. Nevertheless, systems such as PENMAN (Mann, 1983) POPEL (Reithinger, 1991) and FUF ....

....above constituting good examples. Indeed, applications where the aim is to produce written text are generally going to use these techniques. Producing (written) health education materials tailored to particular clients is one major area of research activity: examples include the HealthDoc (DiMarco et al. 1995), Hirst et al. 1997) Smoking Letters (Reiter et al. 1997) and Goldfish and Piglit projects (Binstead et al. 1995) Grasso, 1997) which aim to influence patients decisions on smoking cessation, and the RAGs project (Cooper at al. 1996) which assists in genetic counselling. Another area ....

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"HealthDoc: Customizing patient information and health education by medical condition and personal charactersitics" in Working Notes of the Workshop on AI in Patient Education, Glasgow

.... Others such as (Meteer, 1991) Inui et al. 1992) de Rosis et al. 1997) and (Robin, 1994) have proposed a revision based approach to generation whereby a complete draft plan is created and then subsequently improved (this is similar to the select and repair method of the HealthDoc system (DiMarco et al. 1995), Hirst et al. 1997) Finally, work founded on systemic functional grammars should also be included, though as Hovy points out in (Cole et al. 1995) much of it is focused upon single sentence generation. Nevertheless, systems such as PENMAN (Mann, 1983) POPEL (Reithinger, 1991) and FUF ....

....above constituting good examples. Indeed, applications where the aim is to produce written text are generally going to use these techniques. Producing (written) health education materials tailored to particular clients is one major area of research activity: examples include the HealthDoc (DiMarco et al. 1995), Hirst et al. 1997) Smoking Letters (Reiter et al. 1997) and Goldfish and Piglit projects (Binstead et al. 1995) Grasso, 1997) which aim to influence patients decisions on smoking cessation, and the RAGs project (Cooper at al. 1996) which assists in genetic counselling. Another area ....

[Article contains additional citation context not shown here]

"HealthDoc: Customizing patient information and health education by medical condition and personal charactersitics" in Working Notes of the Workshop on AI in Patient Education, Glasgow

....WIP [WAF 93] and the follow up project PPP [AMR96] have their focus on the variability of the generated documents and on the integration of different media (primarily text and graphics) One parameter of variation is a possible choice between English and German as target languages. HEALTHDOC [DHWW95] a collaborative effort together with the ongoing TECHDOC project 3 is working with texts for individualised patient education and has a special focus on stylistic variation. DRAFTER [PVL96] works with software instruction and experiments with an authoring approach. 7 Future work The ....

Chrysanne DiMarco, Graeme Hirst, Leo Wanner, and John Wilkinson. HealthDoc: Customizing patient information and health education by medical condition and personal characteristics. In Workshop on Artificial Intelligence in Patient Education, Glasgow, August 1995, 1995. 3 funded as a Baden-Wurttemberg / Ontario cooperation.

....in the surface realiser. Referring expression generation is not performed in any real sense, all referring expressions being always generic or proper . There is no use of centering or salience. CD SP SR Job description builder ffl ffl Job description generator ffl 3. 6 HealthDoc HealthDoc ( DHW95] DHH97] HDHP97] HW96] Par97] WH96] Wil95] is an unusual system in that it does not do generation of language from another mode, but rather selects and repairs existing text parts. The input to HealthDoc is a full natural language text, which (allegedly) contains every piece of ....

C. DiMarco, G.and L. Wanner Hirst, and J. Wilkinson. Healthdoc: Customizing patient information and health education by medical condition and personal characteristics. In First International Workshop on Artificial Intelligence in Patient Education, Glasgow, August 1995.

....something closer in form or denotation to linguistic lexical forms. Caption Generation System [MMCRng] MRM 95] MP93] Proverb [HF96] HF97] Joyce [RK] Patent Claim Expert [SN96] Exclass [CK94] Gist [Con96] PC96] Drafter [PVF 95] Drafter2 [PSE98] PS98] SPE98] HealthDoc [DHW95], DHH97] HDHP97] PostGraphe [FL96] ModEx [LRR97] 2 Content words vs function words In the applied NLG systems in our survey, the choice of the actual words that appear in the output text may be performed at various places in various ways. Some systems include the use of canned and semi fixed ....

C. DiMarco, G.and L. Wanner Hirst, and J. Wilkinson. Healthdoc: Customizing patient information and health education by medical condition and personal characteristics. In First International Workshop on Artificial Intelligence in Patient Education, Glasgow, August 1995.

.... as well (Hovy 1990) Discourse based approaches to text generation have been used extensively in tutorial systems, e.g. McKeown 1985, Lester Porter 1996) for automated generation of software engineering reports, e.g. Korelsky et al. 1993) and for medical patient information reports (DiMarco et al. 1995). In the law and AI community, the primary focus of the study of discourse structure has been argument theory, the study of the relationships within multi sentential texts that are intended to persuade, justify, establish, or prove. An influential model of argument structure proposed by Toulmin ....

C. DiMarco, G. Hirst, and L. Wanner, Health Doc: Customizing Patient Information and Health Education by Medical Condition and Personal Characteristics' (1995) Working Notes of the Workshop on Artificial Intelligence in Patient Education.

....Only recently have they given more attention to issues and principles of domain modelling. 983 Roesner D. Grote B. Hartmann K. Hoefling B. From Natural Language Documents to . Multi purpose generation of personalised patient instruction and information is the aim of the healthdoc [DiMarco et al. 1995] project. They try to avoid domain modelling and start generation as a selection process from a so called master document already geared towards English as a target language. It is difficult to interpret this structure as formal knowledge representation and to imagine that it could serve as a ....

C. DiMarco, G. Hirst, L. Wanner, and J. Wilkinson. HealthDoc: Customizing Patient Information and Health Education by Medical Condition and Personal Characteristics. In Workshop on Artificial Intelligence in Patient Education, Glasgow, August 1995.

....track the discourse history is unique. This ability is part of the design for LEAF, but it is not part of the current prototype. 9 9 HealthDoc uses the patient s medical information, physical characteristics, and medical diagnosis to create a document with medical advice tailored to the user [13]. HealthDoc differs from LEAF in that it is intended to be used by health care professionals (rather than patients) and, like Migrainuer, it does not integrate the task of acquiring knowledge about the patient with the task of generating educational materials. The work of Jimison et al. like ....

DiMarco C, Hirst G, Wanner L, Wilkinson J. HealthDoc: Customizing Patient Information and Health Education by Medical Condition and Personal Characteristics. Workshop on Artificial Intelligence in Patient Education. Glasgow, August 1995.

....reports that are relevant to users of the application. This community has focused its efforts on deriving technical documentation from program traces generated during software development or use [KMR93, Joh94, MRK95] and on producing customized patient information reports for medical applications [DHW95]. 5 Discussion and Future Work In this paper we have presented a model of the illocutionary and rhetorical structures underlying a representative legal document jurisdictional show cause orders. We have shown how these structures can be used to form a document grammar that can be used to (1) ....

Chrysanne DiMarco, Graeme Hirst, and Leo Wanner. HealthDoc: Customizing patient information and health education by medical condition and personal characteristics. In Working Notes of the Workshop on Artificial Intelligence in Patient Education, 1995.

....by it is not tailored to the patient. LEAF starts with a general user model which dynamically changes as it gathers more information from the patient. Also, LEAF presents information tailored to the patient. HealthDoc offers customized information to patients and health education materials (DiMarco et al. 1995). It differs from LEAF in that patients can not ask the system for clarification on the material presented. The HRT Decision Making project at McMaster University is a decision support tool for perimenopausal women considering hormone replacement therapy. It asks patients a series of questions to ....

DiMarco, Chrysanne; Hirst, Graeme; Wanner, Leo; and Wilkinson, John. August 1995. HealthDoc: Customizing Patient Information and Health Education by Medical Condition and Personal Characteristics.

....theories of text composition in particular, computational models of rhetorical structure, lexical semantics, and fine grained meaning which are particular requirements for incorporating style and rhetoric in natural language systems. Our current focus of research is our HealthDoc project (DiMarco, Hirst, Wanner, and Wilkinson 1995), which is developing natural language generation systems for producing healthinformationand patient education documents that are customized to the personal and medical characteristics of the individual patient. The HealthDoc approach is applicable, we believe, to many kinds of situation in which ....

DiMarco, Chrysanne; Hirst, Graeme; Wanner, Leo; and Wilkinson, John (1995). "HealthDoc: Customizing patient information and health education by medical condition and personal characteristics." Workshop on Artificial Intelligence in Patient Education, Glasgow, August 1995.

.... Green and DiMarco 1996) We have also been working on the problem of style and lexical choice, with an emphasis on representing near synonymy in generation systems (DiMarco and Hirst 1993b, DiMarco, Hirst, and Stede 1993, Hirst 1995) This earlier work is now feeding in to our HealthDoc project (DiMarco, Hirst, Wanner, and Wilkinson 1995), which is developing natural language generation systems for producing health information and patient education documents that are customized to the personal and medical characteristics of the individual patient. The HealthDoc approach is applicable, we believe, to many kinds of situations in ....

DiMarco, Chrysanne; Hirst, Graeme; Wanner, Leo; and Wilkinson, John (1995). "HealthDoc: Customizing patient information and health education by medical condition and personal characteristics." Workshop on Artificial Intelligence in Patient Education, Glasgow, August 1995.

....project include locus of control (the degree to which the patient regards herself as being in charge of her health) ability or desire to read technical detail, and the degree to which appeals to authority in the presentation of information are persuasive to the patient. For more discussion, see DiMarco, et al. 1995). other parts of the system i.e. the user interface for the clinician, the software interface to the online medical records system, and the module for document layout, formatting, and printing are only simple demonstration prototypes. To avoid a commitment to any of the emerging standards ....

DiMarco, C., Hirst, G., Wanner, L., and Wilkinson, J. (1995). HealthDoc: Customizing patient information and health education by medical condition and personal characteristics. Workshop on Artificial Intelligence in Patient Education, Glasgow, August 1995.

....loosens, or fails. If replacement is needed, it will have to be removed. 3) Removal of redundancy (aggregation) In some instances, an implant wears out, loosens, or fails, and [ will have to be removed. In this paper we describe the sentence planner in the HealthDoc project. HealthDoc [ DiMarco et al. 1995 ] was established in early 1995 with the goal of generating customized patient education documents. It combines existing generation technology the sentence generator kpml [ Bateman, 1995 ] 1 and its input notation spl [ Kasper, 1989 ] and new systems, such as the sentence planner ....

C. DiMarco, G. Hirst, L. Wanner, and J. Wilkinson. Healthdoc: Customizing patient information and health education by medical condition and personal characteristics. In Proceedings of the Workshop on Patient Education, Glasgow, 1995.

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Chrysanne DiMarco, Graeme Hirst, Leo Wanner, and John Wilkinson. HealthDoc: Customizing patient information and health education by medical condition and personal characteristics. In Proceedings of the Workshop on Artificial Intelligence in Patient Education, Glasgow, Aug 1995.

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