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10.2196/mental.4822 http://scihub22266oqcxt.onion/10.2196/mental.4822 C4886102!4886102!27185366     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=27185366&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       JMIR+Ment+Health 2016 ; 3 (2): ä Nephropedia Template TP {{tp|p=27185366|t=2016. Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality |pdf=|usr=}}{{27185366}} gab.com Text Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality JO: JMIR Ment Health http://www.kidney.de/pget.php?&tw=1&pmid=27185366 #FOAvip Background: One of the leading causes of death in the United States (US) is suicide and new methods of assessment are needed to track its risk in real time. Objective: Our objective is to validate the use of machine learning algorithms for Twitter data against empirically validated measures of suicidality in the US population. Methods: Using a machine learning algorithm, the Twitter feeds of 135 Mechanical Turk (MTurk) participants were compared with validated, self-report measures of suicide risk. Results: Our findings show that people who are at high suicidal risk can be easily differentiated from those who are not by machine learning algorithms, which accurately identify the clinically significant suicidal rate in 92% of cases (sensitivity: 53%, specificity: 97%, positive predictive value: 75%, negative predictive value: 93%). Conclusions: Machine learning algorithms are efficient in differentiating people who are at a suicidal risk from those who are not. Evidence for suicidality can be measured in nonclinical populations using social media data. Twit Text FOAVip Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality ????JMIR Ment Health http://www.kidney.de/pget.php?&tw=1&pmid=27185366 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27185366 #FOAvip English Wikipedia <ref>{{Cite pmid|27185366}}</ref> Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality #MMPMID27185366 Coppersmith G; Spitzberg B; LItman L; Tsou MH; Konstantinidis S; Braithwaite SR; Giraud-Carrier C; West J; Barnes MD; Hanson CL JMIR Ment Health 2016[Apr]; 3 (2): ä PMID27185366show ga Background: One of the leading causes of death in the United States (US) is suicide and new methods of assessment are needed to track its risk in real time. Objective: Our objective is to validate the use of machine learning algorithms for Twitter data against empirically validated measures of suicidality in the US population. Methods: Using a machine learning algorithm, the Twitter feeds of 135 Mechanical Turk (MTurk) participants were compared with validated, self-report measures of suicide risk. Results: Our findings show that people who are at high suicidal risk can be easily differentiated from those who are not by machine learning algorithms, which accurately identify the clinically significant suicidal rate in 92% of cases (sensitivity: 53%, specificity: 97%, positive predictive value: 75%, negative predictive value: 93%). Conclusions: Machine learning algorithms are efficient in differentiating people who are at a suicidal risk from those who are not. Evidence for suicidality can be measured in nonclinical populations using social media data. äValidating Machine Learning Algorithms for Twitter Data Against Establ(epmc).pdf DeepDyve Pubget Overpricing