Automated ICD Coding Using Deep Learning

ICD (international classification of diseases) is a system developed by the WHO in which every unique diagnosis and procedure has a unique code. It provides a standardized way to represent medical information and makes it sharable and comparable across different hospitals and countries. Currently, the task of assigning ICD codes to patient discharge summaries is performed manually by medical coders. Manual coding is costly, time consuming, and inefficient for huge data. So, the healthcare industry requires automated solutions to make the medical coding more efficient, accurate, and consistent. In this study, the automated ICD-9 coding is approached as a multi-label text classification problem. A deep learning system is presented to assign ICD-9 codes automatically to the patient discharge summaries. Convolutional neural networks and word2vec model are combined to automatically extract features from the input text. The best model has achieved 83.28% accuracy. The results of this research prove the usability of deep learning for multi-label text classification and medical coding.

Machine Learning for Medical Coding in Health Care Surveys

This exploratory study evaluates the use of machine learning classifiers to perform automated medical coding for large statistical healthcare surveys.

Machine Learning for Medical Coding in Health Care Surveys

This exploratory study evaluates the use of machine learning classifiers to perform automated medical coding for large statistical healthcare surveys.

Prothrombin Complex Concentrate Utilization in Children's Hospitals

Prothrombin complex concentrates (PCCs) are used to manage bleeding in critically ill children. We performed a repeat cross-sectional study using the Pediatric Health Information System registry to describe PCC utilization in the U.S. children's hospitals over time and determine the relationship between PCC use and specific risk factors for bleeding. We included children < 18 years who received three-factor or four-factor PCC during hospital admission between January 2015 and December 2020 to describe the association between PCC therapy, anticoagulation therapies, and inherited or acquired bleeding diatheses. PCC use steadily increased over the 6-year study period (from 1.3 to 4.6 per 10,000 encounters). Patients exhibited a high degree of critical illness, with 85.0% requiring intensive care unit admission and a mortality rate of 25.8%. PCCs were used in a primarily emergent or urgent fashion (32.6 and 39.3%, respectively) and more frequently in surgical cases (79.0% surgical vs. 21.0% medical). Coding analysis suggested a low rate of chronic anticoagulant use which was supported by review of concomitant anticoagulant medications. PCC use is increasing in critically ill children and does not correlate with specific anticoagulant therapy use or other bleeding risk factors. These findings suggest PCC use is not limited to vitamin K antagonist reversal. Indications, efficacy, and safety of PCC therapy in children require further study.

Entropic measures of complexity in a new medical coding system

Background Transitioning from an old medical coding system to a new one can be challenging, especially when the two coding systems are significantly different. The US experienced such a transition in 2015. Objective This research aims to introduce entropic measures to help users prepare for the migration to a new medical coding system by identifying and focusing preparation initiatives on clinical concepts with more likelihood of adoption challenges. Methods Two entropic measures of coding complexity are introduced. The first measure is a function of the variation in the alphabets of new codes. The second measure is based on the possible number of valid representations of an old code. Results A demonstration of how to implement the proposed techniques is carried out using the 2015 mappings between ICD-9-CM and ICD-10-CM/PCS. The significance of the resulting entropic measures is discussed in the context of clinical concepts that were likely to pose challenges regarding documentation, coding errors, and longitudinal data comparisons. Conclusion The proposed entropic techniques are suitable to assess the complexity between any two medical coding systems where mappings or crosswalks exist. The more the entropy, the more likelihood of adoption challenges. Users can utilize the suggested techniques as a guide to prioritize training efforts to improve documentation and increase the chances of accurate coding, code validity, and longitudinal data comparisons.

A Tree Based Approach for Multi-class Classification of Surgical Procedures Using Structured and Unstructured Data

Background: In surgical department, CPT code assignment has been a complicated manual human effort, that entails significant related knowledge and experience. While there are several studies using CPTs to make predictions in surgical services, literature on predicting CPTs in surgical and other services using text features is very sparse. This study improves the prediction of CPTs by the means of informative features and novel re-prioritization algorithm. Methods: The input data used in this study is composed of both structured and unstructured data. The ground truth labels (CPTs) are obtained from medical coding databases using relative value units which indicates the major operational procedures in each surgery case. In the modeling process, we first utilize Random Forest multi-class classification model to predict the CPT codes. Second, we extract the key information such as label probabilities, feature importance measures, and medical term frequency. Then, the indicated factors are used in a novel algorithm to rearrange the alternative CPT codes in the list of potential candidates based on the calculated weights. Results: To evaluate the performance of both phases, prediction and complementary improvement, we report the accuracy scores of multi-class CPT prediction tasks for datasets of 5 key surgery case specialities. The Random Forest model performs the classification task with 74% to 76% when predicting the primary CPT versus the CPT set with respect to the two filtering conditions on CPT codes. The complementary algorithm improves the results from initial step by 8% on average. Furthermore, the incorporated text features enhanced the quality of the output by 20-35%. Conclusions: We have established a robust framework based on a decision tree predictive model. We predict the surgical codes more accurately and robust compared to the state-of-the-art deep neural structures which can help immensely in both surgery billing and scheduling purposes in such units.

Medical unfitness for work at sea: causes and incidence rate over a 12-year period in France

Background The purposes of the study were first to determine the incidence rate of medical unfitness for work at sea among French seafarers, second to identify the conditions (diseases or accidents) causing such incapacity so as to set up prevention measures where possible and third to ascertain whether there were any overrepresentations of diseases according to category of unfit seafarers (fishers, merchant seafarers, shellfish farmers and professional sailors). Methods An exhaustive, observational, descriptive, retrospective epidemiological and nosological study was carried out based on the medical coding of files stored in the Aesculapius® national database, which registers all medical data regarding seafarers presenting at the French seafarers’ health services. The increasing rate of permanent medical unfitness for work at sea was calculated in relation to the annual number of registered seafarers. A 12-year span was chosen in an attempt to ascertain the different sociodemographic categories associated with incapacity. Results In all, 2392 seafarers were declared unfit for work at sea. This represents a permanent medical unfitness for work at sea incidence rate of below 1% for all French seafarers examined for medical fitness between 2005 and 2016. The average age of the population of unfit seafarers was 48. The average time spent at sea before being declared unfit for work at sea was 15.5 years. Sixty-seven percent of the seafarers declared unfit had been working in the fishing sector. The main reasons for deciding permanent unfitness for work at sea were: rheumatological conditions associated specifically with the spine; injuries relating to accidents or other external causes, mostly affecting the upper limbs; mental and behavioural disorders, including mood disorders and particularly addictions; and diseases of the circulatory system, namely coronopathies. The incidence rate of medical unfitness for work at sea was seen to increase between 2005 and 2016, but a decrease due to the dilution effect was noted in 2015. Conclusions Permanent unfitness seldom occurs among French professional seafarers. Prevention measures must be focused on musculoskeletal disorders, psychiatric affections and coronary conditions as well as on combatting maritime accidents, especially in the professional fishing sector, where such affections and accidents are overrepresented.

Entropic Measures of Complexity in a New Medical Coding System

BackgroundTransitioning from an old medical coding system to a new one can be challenging, especially when thetwo coding systems are significantly different. The US experienced such a transition in 2015.ObjectiveThis research aims to introduce entropic measures to help users prepare for the migration to a newmedical coding system by identifying and focusing preparation initiatives on clinical concepts with morelikelihood of adoption challenges.MethodsTwo entropic measures of coding complexity are introduced. The first measure is a function of thevariation in the alphabets of new codes. The second measure is based on the possible number of validrepresentations of an old code.ResultsA demonstration of how to implement the proposed techniques is carried out using the 2015 mappingsbetween ICD-9-CM and ICD-10-CM/PCS. The significance of the resulting entropic measures is discussed inthe context of clinical concepts that were likely to pose challenges regarding documentation, coding errors,and longitudinal data comparisons.ConclusionThe proposed entropic techniques are suitable to assess the complexity between any two medical coding systems where mappings or crosswalks exist. The more the entropy, the more likelihood of adoptionchallenges. Users can utilize the suggested techniques as a guide to prioritize training efforts to improve documentation and increase the chances of accurate coding, code validity, and longitudinal data comparisons.

Multitask Recalibrated Aggregation Network for Medical Code Prediction

Medical coding translates professionally written medical reports into standardized codes, which is an essential part of medical information systems and health insurance reimbursement. Manual coding by trained human coders is time-consuming and error-prone. Thus, automated coding algorithms have been developed, building especially on the recent advances in machine learning and deep neural networks. To solve the challenges of encoding lengthy and noisy clinical documents and capturing code associations, we propose a multitask recalibrated aggregation network. In particular, multitask learning shares information across different coding schemes and captures the dependencies between different medical codes. Feature recalibration and aggregation in shared modules enhance representation learning for lengthy notes. Experiments with a real-world MIMIC-III dataset show significantly improved predictive performance.