| Identification of risk factors and prediction of sepsis in pregnancy using machine learning methods G Kopanitsa, O Metsker, D Paskoshev, S Greschischeva Procedia Computer Science 193, 393-401, 2021 | 10 | 2021 |
| Àäìèíèñòðàòèâíàÿ ïðåþäèöèÿ ïî äåëàì î ìåëêîì õèùåíèè (ñò. 7.27 ÊîÀÏ ÐÔ è ñò. 158.1 ÓÊ ÐÔ): êàê áîëüøèå äàííûå ñóäåáíûõ àêòîâ îòðàæàþò ãóìàíèçàöèþ è êà÷åñòâî ïðàâîñóäèÿ Å Òðîôèìîâ, Îà Ìåöêåð, ÄÄ Ïàñêîøåâ Þðèäè÷åñêèå èññëåäîâàíèÿ, 81-124, 2021 | 7 | 2021 |
| Regulation Modelling and Analysis Using Machine Learning During the Covid-19 Pandemic in Russia E Trofimov, O Metsker, G Kopanitsa, D Paskoshev pHealth 2021, 259-264, 2021 | 6 | 2021 |
| Gynecological surgery and machine learning: complications and length of stay prediction O Metsker, G Kopanitsa, A Malushko, E Komlichenko, K Bolgova, ... Public Health and Informatics, 575-579, 2021 | 6 | 2021 |
| Simulation of a Judicial Process Using Machine Learning to Analyze Administrative Prejudice and Indicate the Quality of Justice D Paskoshev, G Kopanitsa, E Trofimov, O Metsker 2021 30th Conference of Open Innovations Association FRUCT, 165-170, 2021 | 4 | 2021 |
| Âû÷èñëèòåëüíàÿ ïëàòôîðìà äëÿ àíàëèçà ðàçíîðîäíûõ þðèäè÷åñêèõ äàííûõ â öåëÿõ èññëåäîâàíèÿ îïòèìèçàöèè ïðàâîâîãî ðåãóëèðîâàíèÿ ÄÄ Ïàñêîøåâ, Îà Ìåöêåð, ÃÄ Êîïàíèöà, Å Òðîôèìîâ ÑÎÂÐÅÌÅÍÍÛÅ ÒÅÍÄÅÍÖÈÈ ÐÀÇÂÈÒÈß ×ÀÑÒÍÎÃÎ ÏÐÀÂÀ, ÈÑÏÎËÍÈÒÅËÜÍÎÃÎ ÏÐÎÈÇÂÎÄÑÒÂÀ …, 2020 | 4 | 2020 |
| Àäìèíèñòðàòèâíàÿ ïðåþäèöèÿ, ãóìàíèçàöèÿ è êà÷åñòâî ïðàâîñóäèÿ: àíàëèç áîëüøèõ äàííûõ ñóäåáíûõ àêòîâ ïî äåëàì î ìåëêîì õèùåíèè (ñò. 7.27 ÊîÀÏ ÐÔ è ñò. 1581 ÓÊ ÐÔ) 1 Å Òðîôèìîâ, Îà Ìåöêåð, ÄÄ Ïàñêîøåâ ÀÊÒÓÀËÜÍÛÅ ÂÎÏÐÎÑÛ ÐÀÇÂÈÒÈß ÃÎÑÓÄÀÐÑÒÂÅÍÍÎÑÒÈ È ÏÓÁËÈ×ÍÎÃÎ ÏÐÀÂÀ, 66-81, 2021 | 2 | 2021 |
| ÐÅØÅÍÈÅ ÑÈÑÒÅÌÛ ÄÈÔÔÅÐÅÍÖÈÀËÜÍÛÕ ÓÐÀÂÍÅÍÈÉ ÌÅÒÎÄÎÌ ÐÓÍÃÅ-ÊÓÒÒÛ 4-ÃÎ ÏÎÐßÄÊÀ ÄÄ Ïàñêîøåâ, ÍË Ëåîíîâà ÁÁÊ 74.58 Ì 748 Ìîé âêëàä â íàóêó-2018: ñáîðíèê ìàòåðèàëîâ IV ñòóäåí÷åñêîé …, 2019 | | 2019 |
| ÑÅÐÂÈÑ ÂÈÇÓÀËÈÇÀÖÈÈ ÄÎÅÇÄÀ ÁÐÈÃÀÄ ÑÊÎÐÎÉ ÌÅÄÈÖÈÍÑÊÎÉ ÏÎÌÎÙÈ È ÈÍÔÎÐÌÈÐÎÂÀÍÈß ÃÐÀÆÄÀÍ ÅÂ Áîëãîâà, ÀÌ ÇÛÊÓÍÎÂ, ÎÃ ÌÅÖÊÅÐ, ÄÄ ÏÀÑÊÎØÅÂ | | |
