cirpublications.com-Wc40-1790880683-w392021408 1790880683 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Artificial Intelligence for Healthcare Systems J. Artif. Intell. Healthc. Syst. 3149-8981 2025 4 1 Ricardo Alves Bruno Costa Helena Martins Nuno Faria 2025 10.68159/w392021408 https://cirpublications.com/pub/journal/1/article/w392021408 Adkins DE. Machine learning and electronic health records: a paradigm shift. Am J Psychiatry. 2017;174(2):93-4. 10.1176/appi.ajp.2016.16101169 Barak-Corren Y, Castro VM, Javitt S, Hoffnagle AG, Dai Y, Perlis RH, et al. Predicting suicidal behavior from longitudinal electronic health records. Am J Psychiatry. 2017;174(2):154-62. 10.1176/appi.ajp.2016.16010077 Walsh CG, Ribeiro JD, Franklin JC. Predicting risk of suicide attempts over time through machine learning. Clin Psychol Sci. 2017;5(3):457-69. 10.1177/2167702617691560 Walsh CG, Ribeiro JD, Franklin JC. Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning. J Child Psychol Psychiatry. 2018;59(12):1261-70. 10.1111/jcpp.12916 Simon GE, Johnson E, Lawrence JM, Rossom RC, Ahmedani BK, Lynch FL, et al. Predicting suicide attempts and suicide deaths following outpatient visits using electronic health records. Am J Psychiatry. 2018;175(10):951-60. 10.1176/appi.ajp.2018.17101167 Barak-Corren Y, Castro VM, Nock MK, Mandl KD, Madsen EM, Seiger A, et al. Validation of an electronic health record-based suicide risk prediction modeling approach across multiple health care systems. JAMA Netw Open. 2020;3(3):e201262. 10.1001/jamanetworkopen.2020.1262 Roy A, Nikolitch K, McGinn R, Jinah S, Klement W, Kaminsky ZA. A machine learning approach predicts future risk to suicidal ideation from social media data. NPJ Digit Med. 2020;3(1):78. 10.1038/s41746-020-0287-6 Kaminsky Z, McQuaid RJ, Hellemans KG, Patterson ZR, Saad M, Gabrys RL, et al. Machine learning-based suicide risk prediction model for suicidal trajectory on social media following suicidal mentions: independent algorithm validation. J Med Internet Res. 2024;26:e49927. 10.2196/49927 Kim H, Son Y, Lee H, Kang J, Hammoodi A, Choi Y, et al. Machine learning-based prediction of suicidal thinking in adolescents by derivation and validation in 3 independent worldwide cohorts: algorithm development and validation study. J Med Internet Res. 2024;26:e55913. 10.2196/55913 Abdulsalam A, Alhothali A. Suicidal ideation detection on social media: a review of machine learning methods. Soc Netw Anal Min. 2024;14(1):188. 10.1007/s13278-024-01384-6 Liu D, Feng XL, Ahmed F, Shahid M, Guo J. Detecting and measuring depression on social media using a machine learning approach: systematic review. JMIR Ment Health. 2022;9(3):e27244. 10.2196/27244 Tumaliuan FB, Grepo L, Jalao ER. Development of depression data sets and a language model for depression detection: mixed methods study. JMIR Data. 2024;5:e53365. 10.2196/53365 Tazawa Y, Liang KC, Yoshimura M, Kitazawa M, Kaise Y, Takamiya A, et al. Evaluating depression with multimodal wristband-type wearable device: screening and assessing patient severity utilizing machine-learning. Heliyon. 2020;6(2):e03240. 10.1016/j.heliyon.2020.e03240 Pedrelli P, Fedor S, Ghandeharioun A, Howe E, Ionescu DF, Bhathena D, et al. Monitoring changes in depression severity using wearable and mobile sensors. Front Psychiatry. 2020;11:584711. 10.3389/fpsyt.2020.584711 De Angel V, Lewis S, White K, Oetzmann C, Leightley D, Oprea E, et al. Digital health tools for the passive monitoring of depression: a systematic review of methods. NPJ Digit Med. 2022;5(1):3. 10.1038/s41746-021-00548-8 Rykov Y, Thach TQ, Bojic I, Christopoulos G, Car J. Digital biomarkers for depression screening with wearable devices: cross-sectional study with machine learning modeling. JMIR Mhealth Uhealth. 2021;9(10):e24872. 10.2196/24872 Shah RV, Grennan G, Zafar-Khan M, Alim F, Dey S, Ramanathan D, et al. Personalized machine learning of depressed mood using wearables. Transl Psychiatry. 2021;11(1):338. 10.1038/s41398-021-01445-3 Lee S, Kim H, Park MJ, Jeon HJ. Current advances in wearable devices and their sensors in patients with depression. Front Psychiatry. 2021;12:672347. 10.3389/fpsyt.2021.672347 Long N, Lei Y, Peng L, Xu P, Mao P. A scoping review on monitoring mental health using smart wearable devices. Math Biosci Eng. 2022;19(8):7899-919. 10.3934/mbe.2022369 Ahmed A, Aziz S, Alzubaidi M, Schneider J, Irshaidat S, Serhan HA, et al. Wearable devices for anxiety & depression: a scoping review. Comput Methods Programs Biomed Update. 2023;3:100095. 10.1016/j.cmpbup.2022.100095 Ahmed A, Ramesh J, Ganguly S, Aburukba R, Sagahyroon A, Aloul F. Investigating the feasibility of assessing depression severity and valence-arousal with wearable sensors using discrete wavelet transforms and machine learning. Information. 2022;13(9):406. 10.3390/info13090406 Ikäheimonen A, Luong N, Baryshnikov I, Darst R, Heikkilä R, Holmen J, et al. Predicting and monitoring symptoms in patients diagnosed with depression using smartphone data: observational study. J Med Internet Res. 2024;26:e56874. 10.2196/56874 Rozek DC, Andres WC, Smith NB, Leifker FR, Arne K, Jennings G, et al. Using machine learning to predict suicide attempts in military personnel. Psychiatry Res. 2020;294:113515. 10.1016/j.psychres.2020.113515 Tsui FR, Shi L, Ruiz V, Ryan ND, Biernesser C, Iyengar S, et al. Natural language processing and machine learning of electronic health records for prediction of first-time suicide attempts. JAMIA Open. 2021;4(1):ooab011. Levis M, Levy J, Dufort V, Gobbel GT, Watts BV, Shiner B. Leveraging unstructured electronic medical record notes to derive population-specific suicide risk models. Psychiatry Res. 2022;315:114703. 10.1016/j.psychres.2022.114703 Levis M, Levy J, Dent KR, Dufort V, Gobbel GT, Watts BV, et al. Leveraging natural language processing to improve electronic health record suicide risk prediction for Veterans Health Administration users. J Clin Psychiatry. 2023;84(4):22m14568. 10.4088/JCP.22m14568 Holm S. Ethical trade-offs in AI for mental health. Front Psychiatry. 2024;15:1407562. 10.3389/fpsyt.2024.1407562 Kirtley OJ, van Mens K, Hoogendoorn M, Kapur N, De Beurs D. Translating promise into practice: a review of machine learning in suicide research and prevention. Lancet Psychiatry. 2022;9(3):243-52. 10.1016/S2215-0366(21)00390-X Arowosegbe A, Oyelade T. Application of natural language processing (NLP) in detecting and preventing suicide ideation: a systematic review. Int J Environ Res Public Health. 2023;20(2):1514. 10.3390/ijerph20021514 Ermers NJ, Hagoort K, Scheepers FE. The predictive validity of machine learning models in the classification and treatment of major depressive disorder: state of the art and future directions. Front Psychiatry. 2020;11:472. 10.3389/fpsyt.2020.00472 Sequeira L, Perrotta S, LaGrassa J, Merikangas K, Kreindler D, Kundur D, et al. Mobile and wearable technology for monitoring depressive symptoms in children and adolescents: a scoping review. J Affect Disord. 2020;265:314-24. 10.1016/j.jad.2020.01.143