A citation-based method for searching scientific literature

Jihoon Oh, Kyongsik Yun, Ji-Hyun Hwang, Jeong-Ho Chae. Front Psychiatry 2017
Times Cited: 32







List of co-cited articles
125 articles co-cited >1



Times Cited
  Times     Co-cited
Similarity


Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research.
Joseph C Franklin, Jessica D Ribeiro, Kathryn R Fox, Kate H Bentley, Evan M Kleiman, Xieyining Huang, Katherine M Musacchio, Adam C Jaroszewski, Bernard P Chang, Matthew K Nock. Psychol Bull 2017
37

Identifying a clinical signature of suicidality among patients with mood disorders: A pilot study using a machine learning approach.
Ives Cavalcante Passos, Benson Mwangi, Bo Cao, Jane E Hamilton, Mon-Ju Wu, Xiang Yang Zhang, Giovana B Zunta-Soares, Joao Quevedo, Marcia Kauer-Sant'Anna, Flávio Kapczinski,[...]. J Affect Disord 2016
79
34

Predicting Suicidal Behavior From Longitudinal Electronic Health Records.
Yuval Barak-Corren, Victor M Castro, Solomon Javitt, Alison G Hoffnagle, Yael Dai, Roy H Perlis, Matthew K Nock, Jordan W Smoller, Ben Y Reis. Am J Psychiatry 2017
146
25

Predicting suicides after psychiatric hospitalization in US Army soldiers: the Army Study To Assess Risk and rEsilience in Servicemembers (Army STARRS).
Ronald C Kessler, Christopher H Warner, Christopher Ivany, Maria V Petukhova, Sherri Rose, Evelyn J Bromet, Millard Brown, Tianxi Cai, Lisa J Colpe, Kenneth L Cox,[...]. JAMA Psychiatry 2015
241
21

Use of a Machine Learning Algorithm to Predict Individuals with Suicide Ideation in the General Population.
Seunghyong Ryu, Hyeongrae Lee, Dong-Kyun Lee, Kyeongwoo Park. Psychiatry Investig 2018
19
36

Classification of suicide attempters in schizophrenia using sociocultural and clinical features: A machine learning approach.
Nuwan C Hettige, Thai Binh Nguyen, Chen Yuan, Thanara Rajakulendran, Jermeen Baddour, Nikhil Bhagwat, Ali Bani-Fatemi, Aristotle N Voineskos, M Mallar Chakravarty, Vincenzo De Luca. Gen Hosp Psychiatry 2017
20
30

Predicting Suicide Attempts and Suicide Deaths Following Outpatient Visits Using Electronic Health Records.
Gregory E Simon, Eric Johnson, Jean M Lawrence, Rebecca C Rossom, Brian Ahmedani, Frances L Lynch, Arne Beck, Beth Waitzfelder, Rebecca Ziebell, Robert B Penfold,[...]. Am J Psychiatry 2018
156
18

Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning.
Colin G Walsh, Jessica D Ribeiro, Joseph C Franklin. J Child Psychol Psychiatry 2018
84
18


Gender Differences in Machine Learning Models of Trauma and Suicidal Ideation in Veterans of the Iraq and Afghanistan Wars.
Jaimie L Gradus, Matthew W King, Isaac Galatzer-Levy, Amy E Street. J Trauma Stress 2017
27
18

Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth.
Marcel Adam Just, Lisa Pan, Vladimir L Cherkassky, Dana L McMakin, Christine Cha, Matthew K Nock, David Brent. Nat Hum Behav 2017
107
15

Acute Mental Discomfort Associated with Suicide Behavior in a Clinical Sample of Patients with Affective Disorders: Ascertaining Critical Variables Using Artificial Intelligence Tools.
Susana Morales, Jorge Barros, Orietta Echávarri, Fabián García, Alex Osses, Claudia Moya, María Paz Maino, Ronit Fischman, Catalina Núñez, Tita Szmulewicz,[...]. Front Psychiatry 2017
17
29

Suicide detection in Chile: proposing a predictive model for suicide risk in a clinical sample of patients with mood disorders.
Jorge Barros, Susana Morales, Orietta Echávarri, Arnol García, Jaime Ortega, Takeshi Asahi, Claudia Moya, Ronit Fischman, María P Maino, Catalina Núñez. Braz J Psychiatry 2017
13
38


The PHQ-9: validity of a brief depression severity measure.
K Kroenke, R L Spitzer, J B Williams. J Gen Intern Med 2001
15

Machine Learning Approaches for Clinical Psychology and Psychiatry.
Dominic B Dwyer, Peter Falkai, Nikolaos Koutsouleris. Annu Rev Clin Psychol 2018
233
15

A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial.
John P Pestian, Michael Sorter, Brian Connolly, Kevin Bretonnel Cohen, Cheryl McCullumsmith, Jeffry T Gee, Louis-Philippe Morency, Stefan Scherer, Lesley Rohlfs. Suicide Life Threat Behav 2017
47
12

Use of emergency department electronic medical records for automated epidemiological surveillance of suicide attempts: a French pilot study.
Marie-Hélène Metzger, Nastassia Tvardik, Quentin Gicquel, Côme Bouvry, Emmanuel Poulet, Véronique Potinet-Pagliaroli. Int J Methods Psychiatr Res 2017
28
14

A Controlled Trial Using Natural Language Processing to Examine the Language of Suicidal Adolescents in the Emergency Department.
John P Pestian, Jacqueline Grupp-Phelan, Kevin Bretonnel Cohen, Gabriel Meyers, Linda A Richey, Pawel Matykiewicz, Michael T Sorter. Suicide Life Threat Behav 2016
31
12

Predicting the risk of suicide by analyzing the text of clinical notes.
Chris Poulin, Brian Shiner, Paul Thompson, Linas Vepstas, Yinong Young-Xu, Benjamin Goertzel, Bradley Watts, Laura Flashman, Thomas McAllister. PLoS One 2014
69
12

Self-injurious thoughts and behaviors as risk factors for future suicide ideation, attempts, and death: a meta-analysis of longitudinal studies.
J D Ribeiro, J C Franklin, K R Fox, K H Bentley, E M Kleiman, B P Chang, M K Nock. Psychol Med 2016
490
12

Prediction Models for Suicide Attempts and Deaths: A Systematic Review and Simulation.
Bradley E Belsher, Derek J Smolenski, Larry D Pruitt, Nigel E Bush, Erin H Beech, Don E Workman, Rebecca L Morgan, Daniel P Evatt, Jennifer Tucker, Nancy A Skopp. JAMA Psychiatry 2019
185
12

Machine learning in suicide science: Applications and ethics.
Kathryn P Linthicum, Katherine Musacchio Schafer, Jessica D Ribeiro. Behav Sci Law 2019
35
12

Contact with mental health and primary care providers before suicide: a review of the evidence.
Jason B Luoma, Catherine E Martin, Jane L Pearson. Am J Psychiatry 2002
792
9

The Columbia-Suicide Severity Rating Scale: initial validity and internal consistency findings from three multisite studies with adolescents and adults.
Kelly Posner, Gregory K Brown, Barbara Stanley, David A Brent, Kseniya V Yershova, Maria A Oquendo, Glenn W Currier, Glenn A Melvin, Laurence Greenhill, Sa Shen,[...]. Am J Psychiatry 2011
9

The Suicidal Behaviors Questionnaire-Revised (SBQ-R): validation with clinical and nonclinical samples.
A Osman, C L Bagge, P M Gutierrez, L C Konick, B A Kopper, F X Barrios. Assessment 2001
890
9

Data resource profile: the Korea National Health and Nutrition Examination Survey (KNHANES).
Sanghui Kweon, Yuna Kim, Myoung-jin Jang, Yoonjung Kim, Kirang Kim, Sunhye Choi, Chaemin Chun, Young-Ho Khang, Kyungwon Oh. Int J Epidemiol 2014
9

Characteristics of suicidal ideation that predict the transition to future suicide attempts in adolescents.
Regina Miranda, Ana Ortin, Michelle Scott, David Shaffer. J Child Psychol Psychiatry 2014
72
9


Predicting suicidal ideation in primary care: An approach to identify easily assessable key variables.
Pascal Jordan, Meike C Shedden-Mora, Bernd Löwe. Gen Hosp Psychiatry 2018
14
21

Predicting suicides after outpatient mental health visits in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS).
R C Kessler, M B Stein, M V Petukhova, P Bliese, R M Bossarte, E J Bromet, C S Fullerton, S E Gilman, C Ivany, L Lewandowski-Romps,[...]. Mol Psychiatry 2017
91
9

Assessing Suicide Risk and Emotional Distress in Chinese Social Media: A Text Mining and Machine Learning Study.
Qijin Cheng, Tim Mh Li, Chi-Leung Kwok, Tingshao Zhu, Paul Sf Yip. J Med Internet Res 2017
55
9

Novel Use of Natural Language Processing (NLP) to Predict Suicidal Ideation and Psychiatric Symptoms in a Text-Based Mental Health Intervention in Madrid.
Benjamin L Cook, Ana M Progovac, Pei Chen, Brian Mullin, Sherry Hou, Enrique Baca-Garcia. Comput Math Methods Med 2016
41
9

Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports.
R C Kessler, H M van Loo, K J Wardenaar, R M Bossarte, L A Brenner, T Cai, D D Ebert, I Hwang, J Li, P de Jonge,[...]. Mol Psychiatry 2016
96
9

Use of natural language processing in electronic medical records to identify pregnant women with suicidal behavior: towards a solution to the complex classification problem.
Qiu-Yue Zhong, Leena P Mittal, Margo D Nathan, Kara M Brown, Deborah Knudson González, Tianrun Cai, Sean Finan, Bizu Gelaye, Paul Avillach, Jordan W Smoller,[...]. Eur J Epidemiol 2019
18
16

Using Neural Networks with Routine Health Records to Identify Suicide Risk: Feasibility Study.
Marcos DelPozo-Banos, Ann John, Nicolai Petkov, Damon Mark Berridge, Kate Southern, Keith LLoyd, Caroline Jones, Sarah Spencer, Carlos Manuel Travieso. JMIR Ment Health 2018
19
15

Identifying Suicide Ideation and Suicidal Attempts in a Psychiatric Clinical Research Database using Natural Language Processing.
Andrea C Fernandes, Rina Dutta, Sumithra Velupillai, Jyoti Sanyal, Robert Stewart, David Chandran. Sci Rep 2018
52
9

Anxiety disorders and risk for suicidal ideation and suicide attempts: a population-based longitudinal study of adults.
Jitender Sareen, Brian J Cox, Tracie O Afifi, Ron de Graaf, Gordon J G Asmundson, Margreet ten Have, Murray B Stein. Arch Gen Psychiatry 2005
482
9

Smartphones, Sensors, and Machine Learning to Advance Real-Time Prediction and Interventions for Suicide Prevention: a Review of Current Progress and Next Steps.
John Torous, Mark E Larsen, Colin Depp, Theodore D Cosco, Ian Barnett, Matthew K Nock, Joe Firth. Curr Psychiatry Rep 2018
71
9

Identification of suicidal behavior among psychiatrically hospitalized adolescents using natural language processing and machine learning of electronic health records.
Nicholas J Carson, Brian Mullin, Maria Jose Sanchez, Frederick Lu, Kelly Yang, Michelle Menezes, Benjamin Lê Cook. PLoS One 2019
25
12

Detecting risk of suicide attempts among Chinese medical college students using a machine learning algorithm.
Yanmei Shen, Wenyu Zhang, Bella Siu Man Chan, Yaru Zhang, Fanchao Meng, Elizabeth A Kennon, Hanjing Emily Wu, Xuerong Luo, Xiangyang Zhang. J Affect Disord 2020
7
42

Prediction models for high risk of suicide in Korean adolescents using machine learning techniques.
Jun Su Jung, Sung Jin Park, Eun Young Kim, Kyoung-Sae Na, Young Jae Kim, Kwang Gi Kim. PLoS One 2019
17
17

Predicting suicide attempt or suicide death following a visit to psychiatric specialty care: A machine learning study using Swedish national registry data.
Qi Chen, Yanli Zhang-James, Eric J Barnett, Paul Lichtenstein, Jussi Jokinen, Brian M D'Onofrio, Stephen V Faraone, Henrik Larsson, Seena Fazel. PLoS Med 2020
17
17

Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records.
Le Zheng, Oliver Wang, Shiying Hao, Chengyin Ye, Modi Liu, Minjie Xia, Alex N Sabo, Liliana Markovic, Frank Stearns, Laura Kanov,[...]. Transl Psychiatry 2020
19
15


Introducing Machine Learning to Detect Personality Faking-Good in a Male Sample: A New Model Based on Minnesota Multiphasic Personality Inventory-2 Restructured Form Scales and Reaction Times.
Cristina Mazza, Merylin Monaro, Graziella Orrù, Franco Burla, Marco Colasanti, Stefano Ferracuti, Paolo Roma. Front Psychiatry 2019
19
15

Meta-Analysis of Longitudinal Cohort Studies of Suicide Risk Assessment among Psychiatric Patients: Heterogeneity in Results and Lack of Improvement over Time.
Matthew Large, Muthusamy Kaneson, Nicholas Myles, Hannah Myles, Pramudie Gunaratne, Christopher Ryan. PLoS One 2016
101
6

Suicide Trends Among and Within Urbanization Levels by Sex, Race/Ethnicity, Age Group, and Mechanism of Death - United States, 2001-2015.
Asha Z Ivey-Stephenson, Alex E Crosby, Shane P D Jack, Tadesse Haileyesus, Marcie-Jo Kresnow-Sedacca. MMWR Surveill Summ 2017
118
6


Unrecognized suicidal ideation in ED patients: are we missing an opportunity?
Robin S Kemball, Renee Gasgarth, Brian Johnson, Mrunalee Patil, Debra Houry. Am J Emerg Med 2008
42
6


Co-cited is the co-citation frequency, indicating how many articles cite the article together with the query article. Similarity is the co-citation as percentage of the times cited of the query article or the article in the search results, whichever is the lowest. These numbers are calculated for the last 100 citations when articles are cited more than 100 times.