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Predictive Modeling for Pressure Ulcers from Intensive Care Unit Electronic Health Records.

Kaewprag P, Newton C, Vermillion B, Hyun S, Huang K, Machiraju R - AMIA Jt Summits Transl Sci Proc (2015)

Bottom Line: In an ICU setting it's known drawbacks include omission of important risk factors, use of subscale features not significantly associated with PU incidence, and yielding too many false positives.The best models combine Braden and diagnosis.Finally, we report the top diagnosis features which compared to Braden improve AUC by 10%.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, The Ohio State University.

ABSTRACT
Our goal in this study is to find risk factors associated with Pressure Ulcers (PUs) and to develop predictive models of PU incidence. We focus on Intensive Care Unit (ICU) patients since patients admitted to ICU have shown higher incidence of PUs. The most common PU incidence assessment tool is the Braden scale, which sums up six subscale features. In an ICU setting it's known drawbacks include omission of important risk factors, use of subscale features not significantly associated with PU incidence, and yielding too many false positives. To improve on this, we extract medication and diagnosis features from patient EHRs. Studying Braden, medication, and diagnosis features and combinations thereof, we evaluate six types of predictive models and find that diagnosis features significantly improve the models' predictive power. The best models combine Braden and diagnosis. Finally, we report the top diagnosis features which compared to Braden improve AUC by 10%.

No MeSH data available.


Related in: MedlinePlus

Our workflow for developing predictive model of PU among ICU patients using EHR data
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f1-2090176: Our workflow for developing predictive model of PU among ICU patients using EHR data

Mentions: Predictive modeling methods provide a framework by which clinicians can predict the likelihood that a patient will be diagnosed with a disease in the future. Accurate predictive models can help clinicians recommending preventive care to the patients. We evaluated six types of predictive models for PU incidence using five sets of features: 1) Braden, 2) Medication, 3) Diagnosis, 4) Braden & Diagnosis, and 5) Braden & Medication & Diagnosis. We find that using diagnosis features significantly improves the models’ predictive power. Finally, we report the top diagnosis features, which improve assessment quality over only Braden features (as measured by AUC) by 10%. The overall process of our study is shown in Figure 1.


Predictive Modeling for Pressure Ulcers from Intensive Care Unit Electronic Health Records.

Kaewprag P, Newton C, Vermillion B, Hyun S, Huang K, Machiraju R - AMIA Jt Summits Transl Sci Proc (2015)

Our workflow for developing predictive model of PU among ICU patients using EHR data
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4525237&req=5

f1-2090176: Our workflow for developing predictive model of PU among ICU patients using EHR data
Mentions: Predictive modeling methods provide a framework by which clinicians can predict the likelihood that a patient will be diagnosed with a disease in the future. Accurate predictive models can help clinicians recommending preventive care to the patients. We evaluated six types of predictive models for PU incidence using five sets of features: 1) Braden, 2) Medication, 3) Diagnosis, 4) Braden & Diagnosis, and 5) Braden & Medication & Diagnosis. We find that using diagnosis features significantly improves the models’ predictive power. Finally, we report the top diagnosis features, which improve assessment quality over only Braden features (as measured by AUC) by 10%. The overall process of our study is shown in Figure 1.

Bottom Line: In an ICU setting it's known drawbacks include omission of important risk factors, use of subscale features not significantly associated with PU incidence, and yielding too many false positives.The best models combine Braden and diagnosis.Finally, we report the top diagnosis features which compared to Braden improve AUC by 10%.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, The Ohio State University.

ABSTRACT
Our goal in this study is to find risk factors associated with Pressure Ulcers (PUs) and to develop predictive models of PU incidence. We focus on Intensive Care Unit (ICU) patients since patients admitted to ICU have shown higher incidence of PUs. The most common PU incidence assessment tool is the Braden scale, which sums up six subscale features. In an ICU setting it's known drawbacks include omission of important risk factors, use of subscale features not significantly associated with PU incidence, and yielding too many false positives. To improve on this, we extract medication and diagnosis features from patient EHRs. Studying Braden, medication, and diagnosis features and combinations thereof, we evaluate six types of predictive models and find that diagnosis features significantly improve the models' predictive power. The best models combine Braden and diagnosis. Finally, we report the top diagnosis features which compared to Braden improve AUC by 10%.

No MeSH data available.


Related in: MedlinePlus