PMID- 36613150
OWN - NLM
STAT- MEDLINE
DCOM- 20230110
LR  - 20230220
IS  - 1660-4601 (Electronic)
IS  - 1661-7827 (Print)
IS  - 1660-4601 (Linking)
VI  - 20
IP  - 1
DP  - 2023 Jan 1
TI  - An Integrated System of Multifaceted Machine Learning Models to Predict If and 
      When Hospital-Acquired Pressure Injuries (Bedsores) Occur.
LID - 10.3390/ijerph20010828 [doi]
LID - 828
AB  - Hospital-Acquired Pressure Injury (HAPI), known as bedsore or decubitus ulcer, is 
      one of the most common health conditions in the United States. Machine learning 
      has been used to predict HAPI. This is insufficient information for the clinical 
      team because knowing who would develop HAPI in the future does not help 
      differentiate the severity of those predicted cases. This research develops an 
      integrated system of multifaceted machine learning models to predict if and when 
      HAPI occurs. Phase 1 integrates Genetic Algorithm with Cost-Sensitive Support 
      Vector Machine (GA-CS-SVM) to handle the high imbalance HAPI dataset to predict 
      if patients will develop HAPI. Phase 2 adopts Grid Search with SVM (GS-SVM) to 
      predict when HAPI will occur for at-risk patients. This helps to prioritize who 
      is at the highest risk and when that risk will be highest. The performance of the 
      developed models is compared with state-of-the-art models in the literature. 
      GA-CS-SVM achieved the best Area Under the Curve (AUC) (75.79 +/- 0.58) and G-mean 
      (75.73 +/- 0.59), while GS-SVM achieved the best AUC (75.06) and G-mean (75.06). 
      The research outcomes will help prioritize at-risk patients, allocate targeted 
      resources and aid with better medical staff planning to provide intervention to 
      those patients.
FAU - Dweekat, Odai Y
AU  - Dweekat OY
AUID- ORCID: 0000-0001-8710-2306
AD  - Department of Systems Science and Industrial Engineering, Binghamton University, 
      Binghamton, NY 13902, USA.
FAU - Lam, Sarah S
AU  - Lam SS
AD  - Department of Systems Science and Industrial Engineering, Binghamton University, 
      Binghamton, NY 13902, USA.
FAU - McGrath, Lindsay
AU  - McGrath L
AD  - Wound Ostomy Continence Nursing, ChristianaCare Health System, Newark, DE 19718, 
      USA.
LA  - eng
PT  - Journal Article
DEP - 20230101
PL  - Switzerland
TA  - Int J Environ Res Public Health
JT  - International journal of environmental research and public health
JID - 101238455
SB  - IM
MH  - Humans
MH  - *Pressure Ulcer/epidemiology/etiology
MH  - Machine Learning
MH  - Support Vector Machine
MH  - Area Under Curve
MH  - Hospitals
PMC - PMC9820011
OTO - NOTNLM
OT  - bedsores
OT  - cost-sensitive support vector machine
OT  - genetic algorithm
OT  - hospital-acquired pressure injuries
OT  - integrated system
OT  - predictive model
OT  - pressure injuries
OT  - pressure ulcer
COIS- The authors declare no conflict of interest.
EDAT- 2023/01/09 06:00
MHDA- 2023/01/11 06:00
PMCR- 2023/01/01
CRDT- 2023/01/08 01:16
PHST- 2022/11/13 00:00 [received]
PHST- 2022/12/21 00:00 [revised]
PHST- 2022/12/27 00:00 [accepted]
PHST- 2023/01/08 01:16 [entrez]
PHST- 2023/01/09 06:00 [pubmed]
PHST- 2023/01/11 06:00 [medline]
PHST- 2023/01/01 00:00 [pmc-release]
AID - ijerph20010828 [pii]
AID - ijerph-20-00828 [pii]
AID - 10.3390/ijerph20010828 [doi]
PST - epublish
SO  - Int J Environ Res Public Health. 2023 Jan 1;20(1):828. doi: 
      10.3390/ijerph20010828.