| scholarly article | Q13442814 |
| editorial | Q871232 |
| P356 | DOI | 10.1017/S0033291717002859 |
| P698 | PubMed publication ID | 28967349 |
| P50 | author | Giampaolo Perna | Q56816613 |
| P2093 | author name string | M Grassi | |
| C B Nemeroff | |||
| D Caldirola | |||
| P2860 | cites work | Identifying Important Risk Factors for Survival in Kidney Graft Failure Patients Using Random Survival Forests | Q22673963 |
| Genetics and genomics of psychiatric disease | Q26781213 | ||
| Use of machine learning to shorten observation-based screening and diagnosis of autism | Q28730340 | ||
| Personalized medicine in psychiatry: new technologies and approaches | Q30571101 | ||
| A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder | Q30627925 | ||
| Big data analysis using modern statistical and machine learning methods in medicine | Q30834684 | ||
| Use of large data sets and the future of personalized treatment | Q30870491 | ||
| Smartphone data as an electronic biomarker of illness activity in bipolar disorder | Q30995926 | ||
| Data science for mental health: a UK perspective on a global challenge | Q31134145 | ||
| Building a genetic risk model for bipolar disorder from genome-wide association data with random forest algorithm | Q31152380 | ||
| New measures of mental state and behavior based on data collected from sensors, smartphones, and the Internet | Q33441840 | ||
| There is an app for that! The current state of mobile applications (apps) for DSM-5 obsessive-compulsive disorder, posttraumatic stress disorder, anxiety and mood disorders. | Q33454789 | ||
| Prediction of remission in obsessive compulsive disorder using a novel machine learning strategy. | Q33784176 | ||
| The age of the "ome": genome, transcriptome and proteome data set collection and analysis | Q34092031 | ||
| Automated analysis of free speech predicts psychosis onset in high-risk youths | Q34677605 | ||
| Personalized medicine in psychiatry: problems and promises | Q34726454 | ||
| Predictors of treatment response in young people at ultra-high risk for psychosis who received long-chain omega-3 fatty acids | Q35034786 | ||
| Machine learning applications in cancer prognosis and prediction | Q35143671 | ||
| Predicting long-term outcome of Internet-delivered cognitive behavior therapy for social anxiety disorder using fMRI and support vector machine learning | Q35161407 | ||
| Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine | Q35238079 | ||
| Validation of electronic health record phenotyping of bipolar disorder cases and controls | Q35636581 | ||
| Electrocardiographic patch devices and contemporary wireless cardiac monitoring | Q35650227 | ||
| Mood instability is a common feature of mental health disorders and is associated with poor clinical outcomes | Q35675661 | ||
| Quantifying Risk for Anxiety Disorders in Preschool Children: A Machine Learning Approach. | Q36202552 | ||
| Plasma Metabolites Predict Severity of Depression and Suicidal Ideation in Psychiatric Patients-A Multicenter Pilot Analysis | Q36226766 | ||
| Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction | Q36383001 | ||
| Identifying a clinical signature of suicidality among patients with mood disorders: A pilot study using a machine learning approach | Q36553640 | ||
| Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model | Q37334485 | ||
| A study of wrist-worn activity measurement as a potential real-world biomarker for late-life depression. | Q37542106 | ||
| Smart wearable systems: current status and future challenges | Q38057490 | ||
| The potential of biomarkers in psychiatry: focus on proteomics | Q38172847 | ||
| The AmpliChip: A Review of its Analytic and Clinical Validity and Clinical Utility | Q38210248 | ||
| Electronic medical record: a balancing act of patient safety, privacy and health care delivery | Q38216232 | ||
| Altered anatomical patterns of depression in relation to antidepressant treatment: Evidence from a pattern recognition analysis on the topological organization of brain networks | Q38390533 | ||
| Rapid evidence review of the comparative effectiveness, harms, and cost-effectiveness of pharmacogenomics-guided antidepressant treatment versus usual care for major depressive disorder | Q38812608 | ||
| Mental disorders of known aetiology and precision medicine in psychiatry: a promising but neglected alliance. | Q39788584 | ||
| Cross-trial prediction of treatment outcome in depression: a machine learning approach | Q40069166 | ||
| Development and validation of a risk-prediction algorithm for the recurrence of panic disorder | Q41213247 | ||
| Predicting drug-target interactions using probabilistic matrix factorization | Q42778934 | ||
| Optimization of anemia treatment in hemodialysis patients via reinforcement learning. | Q45900927 | ||
| Artificial neural network model for the prediction of obsessive-compulsive disorder treatment response. | Q45932129 | ||
| Functional neuroimaging of high-risk 6-month-old infants predicts a diagnosis of autism at 24 months of age. | Q45947163 | ||
| Predicting treatment response to cognitive behavioral therapy in panic disorder with agoraphobia by integrating local neural information. | Q45955842 | ||
| A machine learning approach using auditory odd-ball responses to investigate the effect of Clozapine therapy. | Q45956266 | ||
| Brainstem abnormalities in attention deficit hyperactivity disorder support high accuracy individual diagnostic classification. | Q45957118 | ||
| Characteristics and profiles of bipolar I patients according to age-at-onset: findings from an admixture analysis | Q46836358 | ||
| A clinical perspective on the relevance of research domain criteria in electronic health records | Q48086926 | ||
| Person-centred medicine and mental health. | Q50749483 | ||
| An introduction to causal inference. | Q51712199 | ||
| Some Studies in Machine Learning Using the Game of Checkers / Arthur L. Samuel. - (1959) | Q55877812 | ||
| Arcing classifier (with discussion and a rejoinder by the author) | Q56114421 | ||
| Soft Microfluidic Assemblies of Sensors, Circuits, and Radios for the Skin | Q59751903 | ||
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | psychiatry | Q7867 |
| P304 | page(s) | 1-9 | |
| P577 | publication date | 2017-10-02 | |
| P1433 | published in | Psychological Medicine | Q7256364 |
| P1476 | title | The revolution of personalized psychiatry: will technology make it happen sooner? |
| Q92404020 | A Novel Ensemble-Based Machine Learning Algorithm to Predict the Conversion From Mild Cognitive Impairment to Alzheimer's Disease Using Socio-Demographic Characteristics, Clinical Information, and Neuropsychological Measures |
| Q58729511 | Can an Integrated Science Approach to Precision Medicine Research Improve Lithium Treatment in Bipolar Disorders? |
| Q96343192 | Computing schizophrenia: ethical challenges for machine learning in psychiatry |
| Q100490885 | Deep neural networks detect suicide risk from textual facebook posts |
| Q92536723 | Implementing Experience Sampling Technology for Functional Analysis in Family Medicine - A Design Thinking Approach |
| Q55075226 | Management of Treatment-Resistant Panic Disorder. |
| Q58570501 | Neural circuitry and precision medicines for mental disorders: are they compatible? |
| Q91637972 | New Technologies for the Understanding, Assessment, and Intervention of Emotion Regulation |
| Q100749189 | Obsessive-compulsive disorder-contamination fears, features and treatment: Novel smartphone therapies in light of global mental health and pandemics (COVID-19) |
| Q90240601 | Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables |
| Q90269656 | Prospective cohort study of early biosignatures of response to lithium in bipolar-I-disorders: overview of the H2020-funded R-LiNK initiative |
| Q92354394 | Toward a personalized therapy for panic disorder: preliminary considerations from a work in progress |
| Q90709129 | Translating basic research knowledge on the biological embedding of early-life stress into novel approaches for the developmental programming of lifelong health |
Search more.