Multi-symptom Classification

Multi-Symptom Classification is a computational method in the domain of symptom recognition that identifies and categorizes multiple co-occurring symptoms from clinical or patient-reported data, distinguishing itself from single-symptom approaches by simultaneously modeling symptom interdependencies and their collective manifestation in disease states.

34

Publications

1.8K

Citations

177

Authors

99

Institutions

Publications per year

2017–2026

14

Authors

177

  1. HZ
    Heping Zhang

    Yale University, United States

    2 publications · 139 citations

    2

    h-index

  2. KI
    Khalida Ismail

    King's College London, United Kingdom

    2 publications · 79 citations

    2

    h-index

  3. KL
    Kun Li

    National University of Defense Technology, China

    1 publications · 16 citations

    1

    h-index

  4. JM
    Jonathan M. Monk

    University of California San Diego, United States

    1 publications · 49 citations

    1

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  5. HH
    H. Henry Lai

    Washington University in St. Louis, United States

    1 publications · 17 citations

    1

    h-index

1–5 of 177

Venues

  1. 2

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  2. PubMed

    Repository · 2 publications · 71 citations

    2

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  3. Information Sciences

    1 publications · 15 citations

    1

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  4. Expert Systems with Applications

    1 publications · 431 citations

    1

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  5. Reliability Engineering & System Safety

    1 publications · 19 citations

    1

    h-index

1–5