{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T13:22:34Z","timestamp":1787404954321,"version":"build-2736575974"},"reference-count":0,"publisher":"Komitet Redakcyjno - Wydawniczy Polskiego Towarzystwa Psychiatrycznego","issue":"3","license":[{"start":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T00:00:00Z","timestamp":1751241600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T00:00:00Z","timestamp":1751241600000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T00:00:00Z","timestamp":1751241600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Psychiatr Pol"],"published-print":{"date-parts":[[2025,6,30]]},"abstract":"<jats:sec>\n                    <jats:title>Cel pracy<\/jats:title>\n                    <jats:p>W grupie pacjent\u00f3w ze zdiagnozowanymi zaburzeniami afektywnymi dwubiegunowymi (BD) i zaburzeniami depresyjnymi nawracaj\u0105cymi (MDD) przeprowadzono analiz\u0119 w celu oceny skuteczno\u015bci algorytm\u00f3w AI w wykrywaniu zmian stanu psychicznego na podstawie fizycznych parametr\u00f3w g\u0142osu.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Metoda<\/jats:title>\n                    <jats:p>Opracowano system MoodMon, obejmuj\u0105cy aplikacj\u0119 mobiln\u0105 na smartfony. W I etapie AI trenowano przy u\u017cyciu obiektywnych danych i ocen klinicznych przeprowadzonych przez psychiatr\u00f3w, kt\u00f3re obejmowa\u0142y 17-punktowe wersje skali HDRS (Hamilton Depression Rating Scale) i YMRS (Young Mania Rating Scale) oraz skal\u0119 CGI (Clinical Global Impression). Drugi etap to dalsze udoskonalenie AI przy u\u017cyciu danych indywidualnych i populacyjnych oraz generowanie alert\u00f3w po wykryciu subtelnych zmian stanu psychicznego. Oba etapy badania trwa\u0142y \u0142\u0105cznie 942 dni.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Wyniki<\/jats:title>\n                    <jats:p>Parametry fizycznego g\u0142osu mog\u0105 s\u0142u\u017cy\u0107 jako biomarkery w zaburzeniach afektywnych. Najskuteczniejsze w wykrywaniu zmian stanu psychicznego okaza\u0142o si\u0119 19 okre\u015blonych fizycznych parametr\u00f3w g\u0142osu. System wykaza\u0142 wysok\u0105 wydajno\u015b\u0107, z nast\u0119puj\u0105cymi warto\u015bciami czu\u0142o\u015bci (TPR, ang. true positive rate) i swoisto\u015bci (TNR, ang. true negative rate): dla obu diagnoz: TPR = 89,5%, TNR = 98,8%, F31: TPR = 89,6%, TNR = 98,9%, F33: TPR = 89,1%, TNR = 98,5%. MoodMon pom\u00f3g\u0142 dok\u0142adnie monitorowa\u0107 i przewidywa\u0107 zmiany stanu psychicznego pacjent\u00f3w z zaburzeniami afektywnymi.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Wnioski<\/jats:title>\n                    <jats:p>System MoodMon to obiektywne narz\u0119dzie AI skutecznie identyfikuj\u0105ce pocz\u0105tkowy okres zmian stanu psychicznego w zaburzeniach afektywnych na podstawie fizycznych parametr\u00f3w g\u0142osu.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.12740\/pp\/onlinefirst\/196927","type":"journal-article","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T12:02:32Z","timestamp":1758196952000},"page":"429-443","source":"Crossref","is-referenced-by-count":0,"title":["MoodMon application for patients with affective disorders created in Poland \u2013 results of the study of the system\u2019s effectiveness based on artificial intelligence algorithms"],"prefix":"10.12740","volume":"60","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6957-063X","authenticated-orcid":false,"given":"Marlena","family":"Sok\u00f3\u0142-Szaw\u0142owska","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ma\u0142gorzata","family":"Sochacka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"5036","published-online":{"date-parts":[[2025,6,30]]},"container-title":["Psychiatria Polska"],"original-title":["Stworzona w Polsce aplikacja MoodMon dla pacjent\u00f3w z zaburzeniami afektywnymi \u2013 wyniki badania\nskuteczno\u015bci systemu opartego na algorytmach sztucznej inteligencji"],"link":[{"URL":"https:\/\/www.psychiatriapolska.pl\/pdf-196927-132713","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.psychiatriapolska.pl\/pdf-196927-132713","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T12:31:18Z","timestamp":1787401878000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.psychiatriapolska.pl\/MoodMon-application-for-patients-with-affective-disorders-created-in-Poland-results,196927,0,2.html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,30]]},"references-count":0,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026]]}},"alternative-id":["196927"],"URL":"https:\/\/doi.org\/10.12740\/pp\/onlinefirst\/196927","relation":{},"ISSN":["0033-2674","2391-5854"],"issn-type":[{"value":"0033-2674","type":"print"},{"value":"2391-5854","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,30]]}}}