BACKGROUND: Real-world evidence research plays an increasingly important role in diabetes care. However, a large fraction of real-world data are "locked" in narrative format. Natural language processing (NLP) technology offers a solution for analysis of narrative electronic data. METHODS: We conducted a systematic review of studies of NLP technology focused on diabetes. Articles published prior to June 2020 were included. RESULTS: We included 38 studies in the analysis. The majority (24; 63.2%) described only development of NLP tools; the remainder used NLP tools to conduct clinical research. A large fraction (17; 44.7%) of studies focused on identification of patients with diabetes; the rest covered a broad range of subjects that included hypoglycemia, lifestyle counseling, diabetic kidney disease, insulin therapy and others. The mean F1 score for all studies where it was available was 0.882. It tended to be lower (0.817) in studies of more linguistically complex concepts. Seven studies reported findings with potential implications for improving delivery of diabetes care. CONCLUSION: Research in NLP technology to study diabetes is growing quickly, although challenges (e.g. in analysis of more linguistically complex concepts) remain. Its potential to deliver evidence on treatment and improving quality of diabetes care is demonstrated by a number of studies. Further growth in this area would be aided by deeper collaboration between developers and end-users of natural language processing tools as well as by broader sharing of the tools themselves and related resources.
BACKGROUND: Real-world evidence research plays an increasingly important role in diabetes care. However, a large fraction of real-world data are "locked" in narrative format. Natural language processing (NLP) technology offers a solution for analysis of narrative electronic data. METHODS: We conducted a systematic review of studies of NLP technology focused on diabetes. Articles published prior to June 2020 were included. RESULTS: We included 38 studies in the analysis. The majority (24; 63.2%) described only development of NLP tools; the remainder used NLP tools to conduct clinical research. A large fraction (17; 44.7%) of studies focused on identification of patients with diabetes; the rest covered a broad range of subjects that included hypoglycemia, lifestyle counseling, diabetic kidney disease, insulin therapy and others. The mean F1 score for all studies where it was available was 0.882. It tended to be lower (0.817) in studies of more linguistically complex concepts. Seven studies reported findings with potential implications for improving delivery of diabetes care. CONCLUSION: Research in NLP technology to study diabetes is growing quickly, although challenges (e.g. in analysis of more linguistically complex concepts) remain. Its potential to deliver evidence on treatment and improving quality of diabetes care is demonstrated by a number of studies. Further growth in this area would be aided by deeper collaboration between developers and end-users of natural language processing tools as well as by broader sharing of the tools themselves and related resources.
Entities:
Keywords:
diabetes; electronic health records; natural language processing
Authors: J M Czerniecki; M L Thompson; A J Littman; E J Boyko; G J Landry; W G Henderson; A P Turner; C Maynard; K P Moore; D C Norvell Journal: Br J Surg Date: 2019-05-28 Impact factor: 6.939
Authors: Brian L Hazlehurst; Jean M Lawrence; William T Donahoo; Nancy E Sherwood; Stephen E Kurtz; Stan Xu; John F Steiner Journal: Am J Prev Med Date: 2014-05 Impact factor: 5.043
Authors: A Rumshisky; M Ghassemi; T Naumann; P Szolovits; V M Castro; T H McCoy; R H Perlis Journal: Transl Psychiatry Date: 2016-10-18 Impact factor: 6.222
Authors: Sudhi G Upadhyaya; Dennis H Murphree; Che G Ngufor; Alison M Knight; Daniel J Cronk; Robert R Cima; Timothy B Curry; Jyotishman Pathak; Rickey E Carter; Daryl J Kor Journal: Mayo Clin Proc Innov Qual Outcomes Date: 2017-04-28
Authors: Adam N Berman; David W Biery; Curtis Ginder; Olivia L Hulme; Daniel Marcusa; Orly Leiva; Wanda Y Wu; Nicholas Cardin; Jon Hainer; Deepak L Bhatt; Marcelo F Di Carli; Alexander Turchin; Ron Blankstein Journal: Clin Cardiol Date: 2021-08-04 Impact factor: 3.287