scholarly article | Q13442814 |
P356 | DOI | 10.1111/SLTB.12312 |
P698 | PubMed publication ID | 27813129 |
P50 | author | Louis-Philippe Morency | Q6686513 |
Kevin Bretonnel Cohen | Q57335098 | ||
P2093 | author name string | Brian Connolly | |
John P Pestian | |||
Michael Sorter | |||
Cheryl McCullumsmith | |||
Stefan Scherer | |||
Jeffry T Gee | |||
Lesley Rohlfs | |||
STM Research Group | |||
P2860 | cites work | Assessment of suicidal intention: The Scale for Suicide Ideation | Q28244530 |
Discovery and validation of blood biomarkers for suicidality | Q28296874 | ||
Hopelessness and eventual suicide: a 10-year prospective study of patients hospitalized with suicidal ideation | Q28307938 | ||
Classification of suicidal behaviors: I. Quantifying intent and medical lethality | Q33335272 | ||
Prediction of suicidal behavior in clinical research by lifetime suicidal ideation and behavior ascertained by the electronic Columbia-Suicide Severity Rating Scale | Q34376444 | ||
Ten-year review of rating scales. III: scales assessing suicidality, cognitive style, and self-esteem | Q34938861 | ||
A systematic review of suicide rating scales in schizophrenia | Q36399683 | ||
Predicting and preventing suicide: do we know enough to do either? | Q36600646 | ||
Neurobiology of suicide: do biomarkers exist? | Q38083578 | ||
Lying words: predicting deception from linguistic styles | Q38420908 | ||
Prediction error estimation: a comparison of resampling methods | Q40421222 | ||
Prediction of suicide in psychiatric patients. Report of a prospective study | Q43859960 | ||
Suicide risk scales: do they help to predict suicidal behaviour? | Q44899510 | ||
A Controlled Trial Using Natural Language Processing to Examine the Language of Suicidal Adolescents in the Emergency Department. | Q45953775 | ||
A Conversation with Edwin Shneidman | Q45962485 | ||
Tracking suicide risk factors through Twitter in the US. | Q46429869 | ||
Suicide risk assessment: myth and reality | Q48106255 | ||
Suicidal ideation and later suicide | Q48664567 | ||
An overview of statistical learning theory. | Q50660273 | ||
Standardized rater training for the Hamilton Depression Rating Scale (HAMD-17) in psychiatric novices | Q52955808 | ||
The prediction of suicide. Sensitivity, specificity, and predictive value of a multivariate model applied to suicide among 1906 patients with affective disorders. | Q53773025 | ||
Machine learning in automated text categorization | Q57771154 | ||
P433 | issue | 1 | |
P921 | main subject | suicidal ideation | Q944142 |
machine learning | Q2539 | ||
suicide | Q10737 | ||
suicide prevention | Q3298118 | ||
multicenter clinical trial | Q6934595 | ||
suicide risk | Q47319077 | ||
P304 | page(s) | 112-121 | |
P577 | publication date | 2016-11-03 | |
P1433 | published in | Suicide and Life-Threatening Behavior | Q15716384 |
P1476 | title | A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial | |
P478 | volume | 47 |
Q38636577 | A nonparametric Bayesian method of translating machine learning scores to probabilities in clinical decision support |
Q47631957 | Annual Research Review: Suicide among youth - epidemiology, (potential) etiology, and treatment |
Q98292695 | Assessment of supervised classifiers for the task of detecting messages with suicidal ideation |
Q94558942 | Automated voice biomarkers for depression symptoms using an online cross-sectional data collection initiative |
Q90024152 | Can acute suicidality be predicted by Instagram data? Results from qualitative and quantitative language analyses |
Q57299960 | Ecologically assessed affect and suicidal ideation following psychiatric inpatient hospitalization |
Q47114432 | Epilepsy Treatment: A Futurist View |
Q91834532 | Evidence-Based Assessment from Simple Clinical Judgments to Statistical Learning: Evaluating a Range of Options Using Pediatric Bipolar Disorder as a Diagnostic Challenge |
Q47590460 | Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing |
Q58701145 | Natural Language Processing of Social Media as Screening for Suicide Risk |
Q90390540 | Suicide prediction models: a critical review of recent research with recommendations for the way forward |
Q39431192 | Using New and Emerging Technologies to Identify and Respond to Suicidality Among Help-Seeking Young People: A Cross-Sectional Study |
Search more.