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Mattias Ohlsson

Mattias Ohlsson


Mattias Ohlsson

Risk predictions for individual patients from logistic regression were visualized with bar-line charts.


  • Jonas Björk
  • Ulf Ekelund
  • Mattias Ohlsson

Summary, in English

OBJECTIVE: The interface of a computerized decision support system is crucial for its acceptance among end users. We demonstrate how combined bar-line charts can be used to visualize predictions for individual patients from logistic regression models. STUDY DESIGN AND SETTING: Data from a previous diagnostic study aiming at predicting the immediate risk of acute coronary syndrome (ACS) among 634 patients presenting to an emergency department with chest pain were used. Risk predictions from the logistic regression model were presented for four hypothetical patients in bar-line charts with bars representing empirical Bayes adjusted likelihood ratios (LRs) and the line representing the estimated probability of ACS, sequentially updated from left to right after assessment of each risk factor. RESULTS: Two patients had similar low risk for ACS but quite different risk profiles according to the bar-line charts. Such differences in risk profiles could not be detected from the estimated ACS risk alone. The bar-line charts also highlighted important but counteracted risk factors in cases where the overall LR was less informative (close to one). CONCLUSION: The proposed graphical technique conveys additional information from the logistic model that can be important for correct diagnosis and classification of patients and appropriate medical management.


  • Division of Occupational and Environmental Medicine, Lund University
  • Medicine, Lund
  • Computational Biology and Biological Physics
  • EpiHealth: Epidemiology for Health

Publishing year







Journal of Clinical Epidemiology



Document type

Journal article




  • Public Health, Global Health, Social Medicine and Epidemiology




  • AIR Lund Chest pain - More efficient and equal emergency care with advanced medical decision support tools


  • ISSN: 1878-5921