The PredictAD project: development of novel biomarkers and analysis software for early diagnosis of the Alzheimer's disease

@article{Antila2013ThePP,
  title={The PredictAD project: development of novel biomarkers and analysis software for early diagnosis of the Alzheimer's disease},
  author={Kari Antila and Jyrki L{\"o}tj{\"o}nen and Lennart Thurfjell and Jarmo Laine and Marcello Massimini and Daniel Rueckert and Roman A. Zubarev and Matej Ore{\vs}ič and Mark van Gils and Jussi Mattila and Anja Hviid Simonsen and Gunhild Waldemar and Hilkka Soininen},
  journal={Interface Focus},
  year={2013},
  volume={3}
}
Alzheimer's disease (AD) is the most common cause of dementia affecting 36 million people worldwide. As the demographic transition in the developed countries progresses towards older population, the worsening ratio of workers per retirees and the growing number of patients with age-related illnesses such as AD will challenge the current healthcare systems and national economies. For these reasons AD has been identified as a health priority, and various methods for diagnosis and many candidates… 

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References

SHOWING 1-10 OF 47 REFERENCES
Software Tool for Improved Prediction of Alzheimer’s Disease
TLDR
A versatile and objective clinical decision support system that could reduce diagnostic errors and highlight early predictors of Alzheimer's disease based on interpretable visualizations of patient data is developed.
Optimizing the diagnosis of early Alzheimer's disease in mild cognitive impairment subjects.
TLDR
It is shown that the disease index values derived from heterogeneous patient data can be used for identifying groups of patients for whom the prediction accuracy reaches the previously set target level.
Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS–ADRDA criteria
A disease state fingerprint for evaluation of Alzheimer's disease.
TLDR
The results suggest that the DSF establishes an effective decision support and data visualization framework for improving AD diagnostics, allowing clinicians to rapidly analyze large quantities of diverse patient data.
Metabolome in progression to Alzheimer's disease
TLDR
The findings primarily implicate hypoxia, oxidative stress, as well as membrane lipid remodeling in progression to AD.
Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade
...
...