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Sepsis Patient Detection and Monitor Based on Auto-BN

  • Patient Facing Systems
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Abstract

Sepsis is a life-threatening condition caused by an inappropriate immune response to infection, and is a leading cause of elderly death globally. Early recognition of patients and timely antibiotic therapy based on guidelines improve survival rate. Unfortunately, for those patients, it is often detected late because it is too expensive and impractical to perform frequent monitoring for all the elderly. In this paper, we present a risk driven sepsis screening and monitoring framework to shorten the time of onset detection without frequent monitoring of all the elderly. Within this framework, the sepsis ultimate risk of onset probability and mortality is calculated based on a novel temporal probabilistic model named Auto-BN, which consists of time dependent state, state dependent property, and state dependent inference structures. Then, different stages of a patient are encoded into different states, monitoring frequency is encoded into the state dependent property, and screening content is encoded into different state dependent inference structures. In this way, the screening and monitoring frequency and content can be automatically adjusted when encoding the sepsis ultimate risk into the guard of state transition. This allows for flexible manipulation of the tradeoff between screening accuracy and frequency. We evaluate its effectiveness through empirical study, and incorporate it into existing medical guidance system to improve medical healthcare.

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Notes

  1. 1 Noting that SIRS condition can also be triggered by an infection, but it can also arise from noninfectious sources such as trauma, hemorrhage, burns, surgery, adrenal insufficiency, pulmonary embolism, or drug overdose etc.

  2. 2 We only consider vital signs in this example, and the history diseases should also be considered in real applications.

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Acknowledgments

The authors thank Dr. Bobby and Dr. Hill at Carle Hospital, Urbana, IL for their help with the historical sepsis data set, and discussion on sepsis knowledge. This work is supported by NSF CNS 13-29886, NSFCNS15-45002, and NSFC 61303014.

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Authors and Affiliations

  1. Department of Computer Science, UIUC, Urbana, IL, USA

    Yu Jiang, Lui Sha, Maryam Rahmaniheris, Mohammad Hosseini & Richard B. Berlin Jr

  2. School of Software, NanChang HangKong University, NanChang, China

    Pengliu Tan

  3. First Hospital of Tsinghua University, Beijing, China

    Binhua Wan

Authors
  1. Yu Jiang
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  2. Lui Sha
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  3. Maryam Rahmaniheris
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  4. Binhua Wan
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  5. Mohammad Hosseini
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  6. Pengliu Tan
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  7. Richard B. Berlin Jr
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Corresponding authors

Correspondence to Yu Jiang or Binhua Wan.

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This article is part of the Topical Collection on Patient Facing Systems

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Jiang, Y., Sha, L., Rahmaniheris, M. et al. Sepsis Patient Detection and Monitor Based on Auto-BN. J Med Syst 40, 111 (2016). https://doi.org/10.1007/s10916-016-0444-2

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