<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="reichenpfader-denecke-2024-simulating">
    <titleInfo>
        <title>Simulating Diverse Patient Populations Using Patient Vignettes and Large Language Models</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Daniel</namePart>
        <namePart type="family">Reichenpfader</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Kerstin</namePart>
        <namePart type="family">Denecke</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Dina</namePart>
            <namePart type="family">Demner-Fushman</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sophia</namePart>
            <namePart type="family">Ananiadou</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Paul</namePart>
            <namePart type="family">Thompson</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Brian</namePart>
            <namePart type="family">Ondov</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>ELRA and ICCL</publisher>
            <place>
                <placeTerm type="text">Torino, Italia</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Ensuring equitable access to digital therapeutics (DTx) is essential to avoid healthcare inequalities in an era of increasing digitization. This requires DTx to be tested with users from diverse populations, which is often not realistic due to time and resource constraints. In this paper, we propose the use of large language models (LLMs) to simulate diverse patients. Specifically, we manually create a patient vignette that characterizes a specific population group. Variations of this vignette are used for role-prompting a commercial LLM, GPT-4, instructing the LLM to take on the role described in the patient vignette and act accordingly. We investigate if the LLM stays in its given role. To do this, we simulate a medical anamnesis interview with the role-prompted LLM and analyze its responses for compliance, coherence, correctness, containment, and clarification. Our results show that GPT-4 generates compliant, coherent and clinically valid responses, including information that is not explicitly stated in the provided patient vignette.</abstract>
    <identifier type="citekey">reichenpfader-denecke-2024-simulating</identifier>
    <location>
        <url>https://aclanthology.org/2024.cl4health-1.3</url>
    </location>
    <part>
        <date>2024-05</date>
        <extent unit="page">
            <start>20</start>
            <end>25</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Simulating Diverse Patient Populations Using Patient Vignettes and Large Language Models
%A Reichenpfader, Daniel
%A Denecke, Kerstin
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Thompson, Paul
%Y Ondov, Brian
%S Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F reichenpfader-denecke-2024-simulating
%X Ensuring equitable access to digital therapeutics (DTx) is essential to avoid healthcare inequalities in an era of increasing digitization. This requires DTx to be tested with users from diverse populations, which is often not realistic due to time and resource constraints. In this paper, we propose the use of large language models (LLMs) to simulate diverse patients. Specifically, we manually create a patient vignette that characterizes a specific population group. Variations of this vignette are used for role-prompting a commercial LLM, GPT-4, instructing the LLM to take on the role described in the patient vignette and act accordingly. We investigate if the LLM stays in its given role. To do this, we simulate a medical anamnesis interview with the role-prompted LLM and analyze its responses for compliance, coherence, correctness, containment, and clarification. Our results show that GPT-4 generates compliant, coherent and clinically valid responses, including information that is not explicitly stated in the provided patient vignette.
%U https://aclanthology.org/2024.cl4health-1.3
%P 20-25

Markdown (Informal)

[Simulating Diverse Patient Populations Using Patient Vignettes and Large Language Models](https://aclanthology.org/2024.cl4health-1.3) (Reichenpfader & Denecke, CL4Health-WS 2024)

• Simulating Diverse Patient Populations Using Patient Vignettes and Large Language Models (Reichenpfader & Denecke, CL4Health-WS 2024)

ACL

• Daniel Reichenpfader and Kerstin Denecke. 2024. Simulating Diverse Patient Populations Using Patient Vignettes and Large Language Models. In Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024, pages 20–25, Torino, Italia. ELRA and ICCL.