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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Falls in the elderly were predicted opportunistically using a decision tree and systematically using a

Meena Rafiq1, Andrew McGovern1, Simon Jones1

  • 1Department of Health Care Management and Policy, University of Surrey, Guildford, Surrey, GU2 7XH, UK.

Journal of Clinical Epidemiology
|May 3, 2014
PubMed
Summary
This summary is machine-generated.

This study identified key risk factors for falls in older adults, developing screening tools for early detection. The tools predict falls without needing extra mobility assessments, aiding proactive care.

Keywords:
ComputerisedElderlyFalls riskFracturesGeneral practiceMedical records systemsScreening tool

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Area of Science:

  • Gerontology
  • Public Health
  • Epidemiology

Background:

  • Falls are a significant cause of injury and mortality in older adults.
  • Effective fall risk prediction is crucial for preventative interventions.
  • Current screening methods may lack efficiency or comprehensiveness.

Purpose of the Study:

  • To identify significant risk factors associated with falls in individuals aged 65 and above.
  • To develop two novel screening tools for falls risk assessment: one for opportunistic use and another for systematic database screening.
  • To enable proactive identification and management of individuals at high risk of falls.

Main Methods:

  • A multicenter cohort study involving 135,433 participants aged 65+ from 127 UK general practice databases.
  • Analysis of routine data including sociodemographic, physical, diagnostic, pharmaceutical, and lifestyle factors over 5 years.
  • Multilevel logistic regression used to identify predictors, followed by decision tree and risk score generation.

Main Results:

  • Eight percent (10,766) of participants experienced a fall or fracture.
  • Key predictors identified: age, female sex, previous fall, nocturia, antidepressant use, and urinary incontinence (AUC=0.72).
  • A risk score (range -7 to 56) with a cut-off of ≥9 demonstrated 68% sensitivity and 60% specificity.

Conclusions:

  • Developed and validated opportunistic and systematic screening tools for predicting falls risk.
  • These tools can identify at-risk individuals without requiring additional mobility assessments.
  • Facilitates targeted interventions to reduce fall incidence in the elderly population.