Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Piyalitt Ittichaiwong"'
Autor:
Borriwat Santipas, Kanyakorn Veerakanjana, Piyalitt Ittichaiwong, Piya Chavalparit, Sirichai Wilartratsami, Panya Luksanapruksa
Publikováno v:
Asian Spine Journal, Vol 18, Iss 3, Pp 325-335 (2024)
Study Design A retrospective study. Purpose This study aimed to develop machine-learning algorithms for predicting survival in patients who underwent surgery for spinal metastasis. Overview of Literature This study develops machine-learning models to
Externí odkaz:
https://doaj.org/article/7e2cd9ff29bb4552bbf806001b2d8dde
Autor:
Borriwat Santipas, Apisun Chanajit, Sirichai Wilartratsami, Piyalitt Ittichaiwong, Kanyakorn Veerakanjana, Panya Luksanapruksa
Publikováno v:
Siriraj Medical Journal, Vol 76, Iss 6 (2024)
Objective: This study aims to develop and compare machine learning models (MLMs) for predicting venous thromboembolism (VTE) in patients undergoing surgery for spinal metastasis. The study evaluates the predictive capabilities of MLMs for preoperativ
Externí odkaz:
https://doaj.org/article/e6345605881747798eb83579fb03c51c
Autor:
Narongrid Seesawad, Piyalitt Ittichaiwong, Thapanun Sudhawiyangkul, Phattarapong Sawangjai, Peti Thuwajit, Paisarn Boonsakan, Supasan Sripodok, Kanyakorn Veerakanjana, Komgrid Charngkaew, Ananya Pongpaibul, Napat Angkathunyakul, Narit Hnoohom, Sumeth Yuenyong, Chanitra Thuwajit, Theerawit Wilaiprasitporn
Publikováno v:
IEEE Open Journal of Engineering in Medicine and Biology, Vol 5, Pp 514-523 (2024)
Background: Deep learning models for patch classification in whole-slide images (WSIs) have shown promise in assisting follicular lymphoma grading. However, these models often require pathologists to identify centroblasts and manually provide refined
Externí odkaz:
https://doaj.org/article/0c94c612c58b45a59a13709fb48791dd
Autor:
Parpada Piamjinda, Chiraphat Boonnag, Piyalitt Ittichaiwong, Seandee Rattanasonrerk, Kanyakorn Veerakanjana, Khanita Duangchaemkarn, Warissara Limpornchitwilai, Kamonwan Thanontip, Napasara Asawalertsak, Thitikorn Kaewlee, Theerawit Wilaiprasitporn
Publikováno v:
IEEE Journal of Translational Engineering in Health and Medicine, Vol 12, Pp 390-400 (2024)
Background: CHIVID is a telemedicine solution developed under tight time constraints that assists Thai healthcare practitioners in monitoring non-severe COVID-19 patients in isolation programs during crises. It assesses patient health and notifies he
Externí odkaz:
https://doaj.org/article/e2159bfb8e404dd4b8d964911898edd6
Autor:
Piya Chavalparit, Sirichai Wilartratsami, Borriwat Santipas, Piyalitt Ittichaiwong, Kanyakorn Veerakanjana, Panya Luksanapruksa
Publikováno v:
Asian Spine Journal, Vol 17, Iss 6, Pp 1013-1023 (2023)
Study Design Retrospective cohort study. Purpose This study aimed to develop machine-learning algorithms to predict ambulation outcomes following surgery for spinal metastasis. Overview of Literature Postoperative ambulation status following spinal m
Externí odkaz:
https://doaj.org/article/4f89633056954618820bc3ae598e9489
Autor:
Chiraphat Boonnag, Piyalitt Ittichaiwong, Wanumaidah Saengmolee, Narongrid Seesawad, Amrest Chinkamol, Saendee Rattanasomrerk, Kanyakorn Veerakanjana, Kamonwan Thanontip, Warissara Limpornchitwilai, Theerawit Wilaiprasitporn
In light of the COVID-19 pandemic, patients were required to manually input their daily oxygen saturation (SpO2) and pulse rate (PR) values into a health monitoring system-unfortunately, such a process trend to be an error in typing. Several studies
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a3a8a52a5d718fa44f6f60f68242c6ae
http://arxiv.org/abs/2212.04964
http://arxiv.org/abs/2212.04964
Autor:
Piya Chavalparit, Sirichai Wilartratsami, Borriwat Santipas, Piyalitt Ittichaiwong, Panya Luksanapruksa
Background: Postoperative ambulation status after spinal metastasis surgery is currently difficult to predict. Improved ability to predict this important postoperative outcome would improve management decision-making and help in determining realistic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b6acc56f68c749a86626fa09887c5341
https://doi.org/10.21203/rs.3.rs-1436840/v1
https://doi.org/10.21203/rs.3.rs-1436840/v1
Autor:
Pratintip Lee, Pannathat Soontrapa, Vachirapon Vonpen, Kanyakorn Veerakanjana, Manop Pithukpakorn, Pongtawat Lertwilaiwittaya, Piyalitt Ittichaiwong
Publikováno v:
Molecular Genetics and Metabolism. 132:S160-S161