Background Natural language processing (NLP) is an important traditional field in computer science, but its application in medical research has faced many challenges. With the extensive digitalization of medical information globally and increasing importance of understanding and mining big data in the medical field, NLP is becoming more crucial. Objective The goal of the research was to perform a systematic review on the use of NLP in medical research with the aim of understanding the global progress on NLP research outcomes, content, methods, and study groups involved. Methods A systematic review was conducted using the PubMed database as a search platform. All published studies on the application of NLP in medicine (except biomedicine) during the 20 years between 1999 and 2018 were retrieved. The data obtained from these published studies were cleaned and structured. Excel (Microsoft Corp) and VOSviewer (Nees Jan van Eck and Ludo Waltman) were used to perform bibliometric analysis of publication trends, author orders, countries, institutions, collaboration relationships, research hot spots, diseases studied, and research methods. Results A total of 3498 articles were obtained during initial screening, and 2336 articles were found to meet the study criteria after manual screening. The number of publications increased every year, with a significant growth after 2012 (number of publications ranged from 148 to a maximum of 302 annually). The United States has occupied the leading position since the inception of the field, with the largest number of articles published. The United States contributed to 63.01% (1472/2336) of all publications, followed by France (5.44%, 127/2336) and the United Kingdom (3.51%, 82/2336). The author with the largest number of articles published was Hongfang Liu (70), while Stéphane Meystre (17) and Hua Xu (33) published the largest number of articles as the first and corresponding authors. Among the first author’s affiliation institution, Columbia University published the largest number of articles, accounting for 4.54% (106/2336) of the total. Specifically, approximately one-fifth (17.68%, 413/2336) of the articles involved research on specific diseases, and the subject areas primarily focused on mental illness (16.46%, 68/413), breast cancer (5.81%, 24/413), and pneumonia (4.12%, 17/413). Conclusions NLP is in a period of robust development in the medical field, with an average of approximately 100 publications annually. Electronic medical records were the most used research materials, but social media such as Twitter have become important research materials since 2015. Cancer (24.94%, 103/413) was the most common subject area in NLP-assisted medical research on diseases, with breast cancers (23.30%, 24/103) and lung cancers (14.56%, 15/103) accounting for the highest proportions of studies. Columbia University and the talents trained therein were the most active and prolific research forces on NLP in the medical field.

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Central Yunnan, constantly in a state of water shortage, is a region of fragile ecological environment whose regional agricultural production and economic development are seriously restricted by drought, despite its relatively developed social economy and dense population. In order to study the drought situation in this area, Landsat TM\OLI remote sensing image data of the region in 2008, 2011, 2014, and 2017 was selected for the construction of the NDVI-LST characteristic space temperature vegetation drought index (TVDI) using the normalized difference vegetation index (NDVI) and the land surface temperature (LST). The validity of TVDI was verified by Standardized Precipitation Index (SPI), Palmer Drought Severity Index (PDSI) and Potential Evapotranspiration (PET), and the TVDI calculation results were divided into 5 drought levels: wet, normal, moderately dry, dry and severe dry. It is shown that: (1) The TVDI values of different land use types have obvious changes and good applicability;(2) TVDI can effectively reflect the characteristics of drought and spatial-temporal changes in central Yunnan, and the severe drought is mostly distributed in the west and southwest; (3) In the past 10 years, the dynamics of drought in this area have changed significantly, mainly from drought to slight drought, and the overall drought has decreased with time. It is concluded that TVDI model accurately reflected the drought situation in central Yunnan and thus is an instructive tool for regional drought monitoring.