Abstract
Personalized medicine (PM) aims to establish a new approach in clinical decision-making, based upon a patient's individual profile in order to tailor treatment to each patient's characteristics. Although this has become a focus of the discussion also in the psychiatric field, with evidence of its high potential coming from several proof-of-concept studies, nearly no tools have been developed by now that are ready to be applied in clinical practice. In this paper, we discuss recent technological advances that can make a shift toward a clinical application of the PM paradigm. We focus specifically on those technologies that allow both the collection of massive as much as real-time data, i.e., electronic medical records and smart wearable devices, and to achieve relevant predictions using these data, i.e. the application of machine learning techniques.
| Original language | English |
|---|---|
| Pages (from-to) | 705-713 |
| Number of pages | 9 |
| Journal | Psychological Medicine |
| Volume | 48 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 1 Apr 2018 |
Keywords
- Big data
- electronic medical records
- machine learning
- personalized medicine
- precision medicine
- wearable devices
- OBSESSIVE-COMPULSIVE DISORDER
- MACHINE LEARNING APPROACH
- LATE-LIFE DEPRESSION
- MENTAL-HEALTH
- ANTIDEPRESSANT TREATMENT
- TREATMENT RESPONSE
- BIPOLAR DISORDER
- MOOD DISORDERS
- PANIC DISORDER
- PREDICTION
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