Aplikasi Machine Learning dalam Deteksi Dini Gangguan Kejiwaan

Zakiyah Mujahidah; Afifah  Khaerani A

doi:10.56861/sp.39

Aplikasi Machine Learning dalam Deteksi Dini Gangguan Kejiwaan

Authors

Zakiyah Mujahidah

Universitas Salakanagara

Afifah Khaerani A

Universitas Salakanagara

DOI: https://doi.org/10.56861/sp.39

Keywords:

MACHINE LEARNING, GANGGUAN KEJIWAAN

Synopsis

Krisis kesehatan mental global saat ini menuntut inovasi teknologi sebagai solusi nyata. Buku Aplikasi Machine Learning dalam Deteksi Dini Gangguan Kejiwaan ini hadir membuka wawasan mengenai bagaimana kecerdasan buatan dapat membantu mengenali gejala depresi dan kecemasan. Pembaca akan diajak memahami mekanisme pengolahan data klinis lewat algoritma populer, analisis rekam medis elektronik, pola komunikasi, hingga pemanfaatan perangkat pintar (wearable) untuk mengenali ekspresi wajah dan nada suara pasien secara etis.

Buku ini juga membahas penerapan praktis sistem peringatan dini di lapangan serta tantangan hukum dan keamanan siber dari teknologi kecerdasan buatan. Ditulis dengan bahasa yang lugas, buku ini menjadi referensi yang sangat terbuka serta ramah dibaca oleh siapa saja, baik pengembang teknologi, praktisi lintas industri, maupun masyarakat umum yang tertarik mendalami pemanfaatan teknologi bagi kesejahteraan jiwa manusia.

References

Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317–1318. https://doi.org/10.1001/jama.2017.18391

Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.

Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. https://doi.org/10.1023/A:1010933404324

Chancellor, S., & De Choudhury, M. (2020). Methods in predictive techniques for mental health status on social media: A critical review. NPJ Digital Medicine, 3, Article 43. https://doi.org/10.1038/s41746-020-0233-7

Chen, J. H., & Asch, S. M. (2017). Machine learning and prediction in medicine: Beyond the peak of inflated expectations. The New England Journal of Medicine, 376(26), 2507–2509. https://doi.org/10.1056/NEJMp1702071

Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273–297. https://doi.org/10.1007/BF00994018

De Choudhury, M., Gamon, M., Counts, S., & Horvitz, E. (2013). Predicting depression via social media. Proceedings of the International AAAI Conference on Web and Social Media, 7(1), 128–137. https://doi.org/10.1609/icwsm.v7i1.14432

Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25, 24–29. https://doi.org/10.1038/s41591-018-0316-z

Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. https://doi.org/10.1016/j.patrec.2005.10.010

Ghassemi, M., Naumann, T., Schulam, P., Beam, A. L., Chen, I. Y., & Ranganath, R. (2020). A review of challenges and opportunities in machine learning for health. AMIA Summits on Translational Science Proceedings, 2020, 191–200.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.

Graham, S., Depp, C., Lee, E. E., Nebeker, C., Tu, X., Kim, H. C., & Jeste, D. V. (2019). Artificial intelligence for mental health and mental illnesses: An overview. Current Psychiatry Reports, 21, Article 116. https://doi.org/10.1007/s11920-019-1094-0

Harrington, P. (2012). Machine learning in action. Manning Publications.

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer. https://doi.org/10.1007/978-0-387-84858-7

Hinton, G. (2018). Deep learning: A technology with the potential to transform health care. JAMA, 320(11), 1101–1102. https://doi.org/10.1001/jama.2018.11100

Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Müller, H. (2019). Causability and explainability of artificial intelligence in medicine. WIREs Data Mining and Knowledge Discovery, 9(4), Article e1312. https://doi.org/10.1002/widm.1312

Hossain, M. M., Tasnim, S., Sultana, A., Faizah, F., Mazumder, H., Zou, L., McKyer, E. L. J., Ahmed, H. U., & Ma, P. (2020). Epidemiology of mental health problems in COVID-19: A review. F1000Research, 9, Article 636. https://doi.org/10.12688/f1000research.24457.1

Insel, T. R. (2017). Digital phenotyping: Technology for a new science of behavior. JAMA, 318(13), 1215–1216. https://doi.org/10.1001/jama.2017.11295

Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243. https://doi.org/10.1136/svn-2017-000101

Jurafsky, D., & Martin, J. H. (2023). Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition (3rd ed. draft). Stanford University.

Kementerian Kesehatan Republik Indonesia. (2022). Profil kesehatan Indonesia tahun 2021. Kementerian Kesehatan Republik Indonesia.

Kementerian Kesehatan Republik Indonesia. (2023). Profil kesehatan Indonesia tahun 2022. Kementerian Kesehatan Republik Indonesia.

Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606–613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x

Kumar, S., Nilsen, W. J., Abernethy, A., Atienza, A., Patrick, K., Pavel, M., Riley, W. T., Shar, A., Spring, B., Spruijt-Metz, D., Hedeker, D., Honavar, V., Kravitz, R., Craig Lefebvre, R., Mohr, D. C., Murphy, S. A., Quinn, C., Shusterman, V., & Swendeman, D. (2013). Mobile health technology evaluation: The mHealth evidence workshop. American Journal of Preventive Medicine, 45(2), 228–236. https://doi.org/10.1016/j.amepre.2013.03.017

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444. https://doi.org/10.1038/nature14539

Lovejoy, C. A. (2019). Technology and mental health: The role of artificial intelligence. European Psychiatry, 55, 1–3. https://doi.org/10.1016/j.eurpsy.2018.08.004

Luxton, D. D. (Ed.). (2016). Artificial intelligence in behavioral and mental health care. Academic Press.

Martinez-Martin, N., Kreitmair, K., & Cho, M. K. (2018). Ethical issues for direct-to-consumer digital psychotherapy apps: Addressing accountability, data protection, and consent. JMIR Mental Health, 5(2), Article e32. https://doi.org/10.2196/mental.9423

Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018). Deep learning for healthcare: Review, opportunities and challenges. Briefings in Bioinformatics, 19(6), 1236–1246. https://doi.org/10.1093/bib/bbx044

Murphy, K. P. (2012). Machine learning: A probabilistic perspective. MIT Press.

Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future: Big data, machine learning, and clinical medicine. The New England Journal of Medicine, 375(13), 1216–1219. https://doi.org/10.1056/NEJMp1606181

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342

Orsolini, L., Pompili, S., Salvi, V., & Volpe, U. (2021). A systematic review on teleMental health in youth mental health: Focus on anxiety, depression and obsessive-compulsive disorder. Medicina, 57(8), Article 793. https://doi.org/10.3390/medicina57080793

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.

Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. The New England Journal of Medicine, 380(14), 1347–1358. https://doi.org/10.1056/NEJMra1814259

Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., Ourselin, S., Sheller, M., Summers, R. M., Trask, A., Xu, D., Baust, M., & Cardoso, M. J. (2020). The future of digital health with federated learning. NPJ Digital Medicine, 3, Article 119. https://doi.org/10.1038/s41746-020-00323-1

Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.

Shatte, A. B. R., Hutchinson, D. M., & Teague, S. J. (2019). Machine learning in mental health: A scoping review of methods and applications. Psychological Medicine, 49(9), 1426–1448. https://doi.org/10.1017/S0033291719000151

Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018). Deep EHR: A survey of recent advances in deep learning techniques for electronic health record analysis. IEEE Journal of Biomedical and Health Informatics, 22(5), 1589–1604. https://doi.org/10.1109/JBHI.2017.2767063

Spitzer, R. L., Kroenke, K., Williams, J. B. W., & Löwe, B. (2006). A brief measure for assessing generalized anxiety disorder: The GAD-7. Archives of Internal Medicine, 166(10), 1092–1097. https://doi.org/10.1001/archinte.166.10.1092

Substance Abuse and Mental Health Services Administration. (2021). National guidelines for behavioral health crisis care: Best practice toolkit. U.S. Department of Health and Human Services.

Torous, J., Bucci, S., Bell, I. H., Kessing, L. V., Faurholt-Jepsen, M., Whelan, P., Carvalho, A. F., Keshavan, M., Linardon, J., & Firth, J. (2021). The growing field of digital psychiatry: Current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry, 20(3), 318–335. https://doi.org/10.1002/wps.20883

Torous, J., & Roberts, L. W. (2017). Needed innovation in digital health and smartphone applications for mental health: Transparency and trust. JAMA Psychiatry, 74(5), 437–438. https://doi.org/10.1001/jamapsychiatry.2017.0262

Topol, E. J. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.

Uddin, S., Khan, A., Hossain, M. E., & Moni, M. A. (2019). Comparing different supervised machine learning algorithms for disease prediction. BMC Medical Informatics and Decision Making, 19, Article 281. https://doi.org/10.1186/s12911-019-1004-8

Undang-Undang Republik Indonesia Nomor 17 Tahun 2023 tentang Kesehatan.

Undang-Undang Republik Indonesia Nomor 27 Tahun 2022 tentang Perlindungan Data Pribadi.

Vayena, E., Blasimme, A., & Cohen, I. G. (2018). Machine learning in medicine: Addressing ethical challenges. PLOS Medicine, 15(11), Article e1002689. https://doi.org/10.1371/journal.pmed.1002689

Wang, P., Krishnan, R., & Panch, T. (2021). The ethics of AI in health care: A mapping review. Social Science & Medicine, 260, Article 113172. https://doi.org/10.1016/j.socscimed.2020.113172

World Health Organization. (2019). WHO guideline: Recommendations on digital interventions for health system strengthening. World Health Organization.

World Health Organization. (2021). Guidance on community mental health services: Promoting person-centred and rights-based approaches. World Health Organization.

World Health Organization. (2021). Ethics and governance of artificial intelligence for health: WHO guidance. World Health Organization.

World Health Organization. (2022). World mental health report: Transforming mental health for all. World Health Organization.

World Health Organization. (2023). Global strategy on digital health 2020–2025. World Health Organization.

Zhang, Z., Ho, K. M., & Hong, Y. (2019). Machine learning for the prediction of volume responsiveness in patients with oliguric acute kidney injury in critical care. Critical Care, 23, Article 112. https://doi.org/10.1186/s13054-019-2411-z