An End-to-End Natural Language Processing Application for Prediction of Medical Case Coding Complexity: Algorithm Development and Validation

BackgroundMedical coding is the process that converts clinical documentation into standard medical codes. Codes are used for several key purposes in a hospital (eg, insurance reimbursement and performance analysis); therefore, their optimization is crucial. With the rapid growth of natural language processing technologies, several solutions based on artificial intelligence have been proposed to aid in medical coding by automatically suggesting relevant codes for clinical documents. However, their effectiveness is still limited to simple cases, and it is not yet clear how much value they can bring in improving coding efficiency and accuracy. ObjectiveThis study aimed to bring more efficiency to the coding process to improve the selection of codes by medical coders. To achieve this, we developed an innovative multimodal machine learning–based solution that, instead of predicting codes, detects the degree of coding complexity before coding is performed. The notion of coding complexity was used to better dispatch work among medical coders to eventually minimize errors and improve throughput. MethodsTo train and evaluate our approach, we collected 2060 cases rated by coders in terms of coding complexity from 1 (simplest) to 4 (most complex). We asked 2 expert coders to rate 3.01% (62/2060) of the cases as the gold standard. The agreements between experts were used as benchmarks for model evaluation. A case contains both clinical text and patient metadata from the hospital electronic health record. We extracted both text features and metadata features, then concatenated and fed them into several machine learning models. Finally, we selected 2 models. The first used cross-validated training on 1751 cases and testing on 309 cases aiming to assess the predictive power of the proposed approach and its generalizability. The second model was trained on 1998 cases and tested on the gold standard to validate the best model performance against human benchmarks. ResultsOur first model achieved a macro–F1-score of 0.51 and an accuracy of 0.59 on classifying the 4-scale complexity. The model distinguished well between the simple (combined complexity 1-2) and complex (combined complexity 3-4) cases with a macro–F1-score of 0.65 and an accuracy of 0.71. Our second model achieved 61% agreement with experts’ ratings and a macro–F1-score of 0.62 on the gold standard, whereas the 2 experts had a 66% (41/62) agreement ratio with a macro–F1-score of 0.67. ConclusionsWe propose a multimodal machine learning approach that leverages information from both clinical text and patient metadata to predict the complexity of coding a case in the precoding phase. By integrating this model into the hospital coding system, distribution of cases among coders can be done automatically with performance comparable with that of human expert coders, thus improving coding efficiency and accuracy at scale

Nomenclature report 2019 : major histocompatibility complex genes and alleles of Great and Small Ape and Old and New World monkey species

The major histocompatibility complex (MHC) is central to the innate and adaptive immune responses of jawed vertebrates. Characteristic of the MHC are high gene density, gene copy number variation, and allelic polymorphism. Because apes and monkeys are the closest living relatives of humans, the MHCs of these non-human primates (NHP) are studied in depth in the context of evolution, biomedicine, and conservation biology. The Immuno Polymorphism Database (IPD)-MHC NHP Database (IPD-MHC NHP), which curates MHC data of great and small apes, as well as Old and New World monkeys, has been upgraded. The curators of the database are responsible for providing official designations for newly discovered alleles. This nomenclature report updates the 2012 report, and summarizes important nomenclature issues and relevant novel features of the IPD-MHC NHP Database

Correction to : Nomenclature report 2019: major histocompatibility complex genes and alleles of great and small ape and old and new world monkey species (Immunogenetics, (2019), 10.1007/s00251-019-01132-x)

The original version of this article contained a spelling error in the Acknowledgments regarding the name of the funding organisation supporting GM and JAH. UKRI-BBSCR should have been UKRI-BBSRC, as is now indicated correctly below

Nomenclature report 2019: major histocompatibility complex genes and alleles of Great and Small Ape and Old and New World monkey species

The major histocompatibility complex (MHC) is central to the innate and adaptive immune responses of jawed vertebrates. Characteristic of the MHC are high gene density, gene copy number variation, and allelic polymorphism. Because apes and monkeys are the closest living relatives of humans, the MHCs of these non-human primates (NHP) are studied in depth in the context of evolution, biomedicine, and conservation biology. The Immuno Polymorphism Database (IPD)-MHC NHP Database (IPD-MHC NHP), which curates MHC data of great and small apes, as well as Old and New World monkeys, has been upgraded. The curators of the database are responsible for providing official designations for newly discovered alleles. This nomenclature report updates the 2012 report, and summarizes important nomenclature issues and relevant novel features of the IPD-MHC NHP Database

Correction to: Nomenclature report 2019: major histocompatibility complex genes and alleles of great and small ape and old and new world monkey species (Immunogenetics, (2019), 10.1007/s00251-019-01132-x)

The original version of this article contained a spelling error in the Acknowledgments regarding the name of the funding organisation supporting GM and JAH. UKRI-BBSCR should have been UKRI-BBSRC, as is now indicated correctly below

Nomenclature report 2019: Major histocompatibility complex genes and alleles of great and small ape and old and new world monkey species

The major histocompatibility complex (MHC) is central to the innate and adaptive immune responses of jawed vertebrates. Characteristic of the MHC are high gene density, gene copy number variation, and allelic polymorphism. Because apes and monkeys are the closest living relatives of humans, the MHCs of these non-human primates (NHP) are studied in depth in the context of evolution, biomedicine, and conservation biology. The Immuno Polymorphism Database (IPD)-MHC NHP Database (IPD-MHC NHP), which curates MHC data of great and small apes, as well as Old and New World monkeys, has been upgraded. The curators of the database are responsible for providing official designations for newly discovered alleles. This nomenclature report updates the 2012 report, and summarizes important nomenclature issues and relevant novel features of the IPD-MHC NHP Database

Effects of oral L-carnitine and DL-carnitine supplementation on alloxan-diabetic rats

The effect of oral L-carnitine (LC) or DL-carnitine (DLC) supplementation during one or four weeks (200 or 400 mg.kg-1.day-1) in diabetic rats was investigated. After the supplementation period, the blood was collected for the evaluation of total (TC) and free L-carnitine (FC), glucose, total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and triacylglycerol. Tissues were collected for the determination of TC and FC concentrations. The carnitine supplementation did not change levels of glucose, total cholesterol, HDL-C and LDL-C in the blood. Diabetic rats showed hypertriacylglycerolemia and decreased blood and tissue levels of FC and TC. Normalization of the blood triacylglycerol and increased blood and tissue levels of FC and TC were observed with the LC or DLC supplementation. However, the hyperglycemia remained unchanged. Thus, the reduction of blood triacylglycerol obtained with carnitine supplementation in the diabetic rats did not depend on an amelioration in the glycemia and was mediated partly at least by an increment of serum and tissue concentrations of FC and TC