Yu‐Ye Li
Biographic Data
| ID | 8417613 |
|---|---|
| NAME | Yu‐Ye Li |
| GIVEN NAMES | Yu‐Ye |
| FAMILY NAME | Li |
| SIGNATURE | LI Y Y |
| AFFILIATIONS | Kunming Medical University |
| ORCID | 0000-0003-0676-985X |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Machine learning methods to predict leprosy misdiagnosis in Yunnan Province, People's Republic of China
Background and objective: that is often misdiagnosed. This study aimed to identify factors associated with leprosy misdiagnosis and develop and compare machine learning (ML) models to predict the risk of misdiagnosis. Methods: A retrospective analysis was conducted on clinical and epidemiological data in 486 diagnosed leprosy patients. The outcome was a binary variable to indicate whether a patient had experienced a prior misdiagnosis. Features a…
Assessing the impact of Covid-19 interventions on the hand, foot and mouth disease in Guangdong Province, China
Background: The non-pharmaceutical interventions (NPIs) against COVID-19 may have affected the transmission of hand, foot and mouth disease (HFMD). We aimed to assess the impact of the NPIs on HFMD in the high epidemic area of HFMD, Guangdong Province. Methods: The data of HFMD cases, etiological information, and meteorological factors in Guangdong from January 1, 2012, to December 31, 2021, were collected. Using a Bayesian structural time series…
Deep Fungal Infections Among General Hospital Inpatients in Southwestern China
Background Deep fungal infection is a type of life-threatening opportunistic infection. Its incidence has been increasing in recent years. This infection can affect the prognosis of patients, prolong hospital stays and raise costs for patients and their families. Objective We aimed to understand the current situation of deep fungal infections in the First Affiliated Hospital of Kunming Medical University and to provide a basis for the clinical di…
Factors Influencing Leprosy Incidence
Leprosy remains endemic in some regions and is a global health concern. However, the possible causes and risk factors of the disease remain unclear. Data in Wenshan, China were collected from the Wenshan Institute of Dermatology (1986–2015); data in Nepal were obtained from the Leprosy Control Division, Department of Health Services, Nepal (2011 to 2015); and data from Indonesia, India, and Brazil were collected from WHO records. We assessed the …
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Factors Influencing Leprosy Incidence
Leprosy remains endemic in some regions and is a global health concern. However, the possible causes and risk factors of the disease remain unclear. Data in Wenshan, China were collected from the Wenshan Institute of Dermatology (1986–2015); data in Nepal were obtained from the Leprosy Control Division, Department of Health Services, Nepal (2011 to 2015); and data from Indonesia, India, and Brazil were collected from WHO records. We assessed the …
Deep Fungal Infections Among General Hospital Inpatients in Southwestern China
Background Deep fungal infection is a type of life-threatening opportunistic infection. Its incidence has been increasing in recent years. This infection can affect the prognosis of patients, prolong hospital stays and raise costs for patients and their families. Objective We aimed to understand the current situation of deep fungal infections in the First Affiliated Hospital of Kunming Medical University and to provide a basis for the clinical di…
Assessing the impact of Covid-19 interventions on the hand, foot and mouth disease in Guangdong Province, China
Background: The non-pharmaceutical interventions (NPIs) against COVID-19 may have affected the transmission of hand, foot and mouth disease (HFMD). We aimed to assess the impact of the NPIs on HFMD in the high epidemic area of HFMD, Guangdong Province. Methods: The data of HFMD cases, etiological information, and meteorological factors in Guangdong from January 1, 2012, to December 31, 2021, were collected. Using a Bayesian structural time series…
Machine learning methods to predict leprosy misdiagnosis in Yunnan Province, People's Republic of China
Background and objective: that is often misdiagnosed. This study aimed to identify factors associated with leprosy misdiagnosis and develop and compare machine learning (ML) models to predict the risk of misdiagnosis. Methods: A retrospective analysis was conducted on clinical and epidemiological data in 486 diagnosed leprosy patients. The outcome was a binary variable to indicate whether a patient had experienced a prior misdiagnosis. Features a…
Epidemiology (3 works) · Geography (3 works) · Medicine (3 works) · China (2 works) · Demography (2 works) · Disease (2 works) · Environmental health (2 works) · Leprosy (2 works) · Leprosy Research and Treatment (2 works) · Pathology (2 works)