Medical Coding


Medical coding is used to accurately and consistently describe medical conditions, treatments, and procedures in a standardized way. This allows for easier communication and understanding between healthcare providers, and it also enables the creation of accurate and complete records of patient care. Medical coding is important for billing and reimbursement purposes, as well as for conducting research and analyzing healthcare data. There are several standardized medical coding systems, such as the International Classification of Diseases (ICD) and the Current Procedural Terminology (CPT), that are used globally. Medical coders must be trained in the use of these systems and must be able to accurately apply the codes to patient records.

Clinical coders use medical classification systems such as the International Classification of Diseases (ICD) or the Current Procedural Terminology (CPT) to assign codes to patient diagnoses, procedures, and other medical services. These codes are used for a variety of purposes, including insurance billing, statistical analysis, and electronic health records. Clinical coders play a critical role in the healthcare industry by ensuring that patient records are accurately and consistently coded, which helps to improve the efficiency and effectiveness of the healthcare system.

AI can be used in medical coding in a few ways:

  1. To help with the coding process: AI algorithms can be used to automatically assign codes to certain diagnoses and procedures, reducing the workload for clinical coders.
  2. To check for errors in coding: AI can be used to identify errors in coding, such as incorrect codes being assigned or codes that are not supported by the patient’s medical record.
  3. To improve the accuracy of coding: AI algorithms can analyze large amounts of data to identify patterns and trends that may not be immediately apparent to a human coder. This can help improve the accuracy of coding and ensure that patients are being properly billed for the medical services they receive.
  4. To analyze coding data: AI can be used to analyze large amounts of coding data to identify trends and patterns, which can help healthcare organizations improve their operations and identify areas for cost savings.



See also:

100 Best IBM Watson Medical Videos100 Best Virtual Nurse Videos100 Best Virtual Patient VideosMedical Expert SystemsVirtual Nurses 2013Virtual Nurses 2014Virtual Nurses 2015Virtual Patients 2013Virtual Patients 2014Virtual Patients 2015

Overview of the ImageCLEF 2015 Medical Classification Task.
AGS de Herrera, H Müller, S Bromuri – CLEF (Working Notes), 2015 –
Abstract. This articles describes the ImageCLEF 2015 Medical Classification task. The task contains several subtasks that all use a data set of figures from the biomedical open access literature (PubMed Central). Particularly compound figures are targeted that are frequent in

Temporal trends in the systemic inflammatory response syndrome, sepsis, and medical coding of sepsis
BS Thomas, SR Jafarzadeh… – BMC …, 2015 –
Background Recent reports using administrative claims data suggest the incidence of community-and hospital-onset sepsis is increasing. Whether this reflects changing epidemiology, more effective diagnostic methods, or changes in physician documentation

Step-By-Step Medical Coding, 2017 Edition-E-Book
CJ Buck – 2016 –
Take your first step toward a successful career in medical coding with guidance from the most trusted name in coding education! From Carol J. Buck, the bestselling Step-by-Step Medical Coding is a practical, easy-to-use resource that shows you exactly how to code

FHDO Biomedical Computer Science Group at Medical Classification Task of ImageCLEF 2015.
O Pelka, CM Friedrich – CLEF (Working Notes), 2015 –
Abstract. This paper presents the modelling approaches performed by the FHDO Biomedical Computer Science Group for the compound figure detection and subfigure classification tasks at ImageCLEF 2015 medical classification. This is the first participation of the group at

Understanding medical coding: A comprehensive guide
SL Johnson, R Linker – 2015 –
Learn everything you need to know about medical coding with the practical and easy to understand UNDERSTANDING MEDICAL CODING: A COMPREHENSIVE GUIDE, 4E. Using clear, step-by-step instructions, readers learn how to code a claim correctly and link

A class based approach for medical classification of chest pain
K RuthRamya, K Anusha, K Chanti, VS Vidya… – Int J Eng Trends …, 2012 –
Abstract—This paper focuses on class based data mining algorithm and their use in medical applications. Data mining techniques have been used in medical research for many years and have been known to be effective. In order to solve such problems as long-waiting time,

A supportive attribute-assisted discretization model for medical classification
DF Wong, LS Chao, XD Zeng – Bio-medical materials and …, 2014 –
Abstract Discretization of a continuous-valued symptom (attribute) in medical data set is a crucial preprocessing step for the medical classification task. This paper proposes a supportive attribute–assisted discretization (SAAD) model for medical diagnostic problems.

Risk selection, risk adjustment, and manipulable medical coding: Evidence from Medicare
M Geruso, T Layton – 2014 –
Abstract Risk adjustment is commonly used in health insurance markets to deal with problems of adverse selection and cream skimming by compensating health plans for insuring consumers whose diagnoses imply high expected costs. However, in all real world

Connecting high-dimensional mRNA and miRNA expression data for binary medical classification problems
M Fuchs, T Beißbarth, E Wingender, K Jung – Computer methods and …, 2013 – Elsevier
Abstract In modern molecular biology, high-throughput experiments allow the simultaneous study of expression levels of thousands of biopolymers such as mRNAs, miRNAs or proteins. A typical goal of such experiments is to find molecular signatures that can

Medical Coding
PT Aalseth – 2014 –
In clear and straightforward language, Medical Coding: What It Is and How It Works, Second Edition provides an overview of the evolution of medical coding and all the various coding systems, how they relate, and how they function. Reasoning and consequences of the

Traditional Feature Engineering and Deep Learning Approaches at Medical Classification Task of ImageCLEF 2016.
S Koitka, CM Friedrich – CLEF (Working Notes), 2016 –
Abstract. This paper describes the modeling approaches used for the Subfigure Classification subtask at ImageCLEF 2016 by the FHDO Biomedical Computer Science Group (BCSG). Besides traditional feature engineering, modern Deep Convolutional Neural

Medical Image Classification via 2D color feature based Covariance Descriptors.
P Cirujeda, X Binefa – CLEF (Working Notes), 2015 –
… Despite of that, the resemblance between image cues is high and poses a challenging problem from a classification perspective [6]. The ImageCLEF Medical Classification challenge [8] provides a benchmark to test the impact of different image classification and feature …

Medical Data Classi?cation with Naive Bayes Approach
KM Al-Aidaroos, AA Bakar, Z Othman – Information Technology …, 2012 –
… However, among the different approaches and techniques used for medical applications, in this paper we are concerned with the use of Naive Bayes (NB) for medical classification. In the following we discuss its basic features and how it suits for this domain. …

Variation in academic medical centers’ coding practices for postoperative respiratory complications: Implications for the AHRQ postoperative respiratory failure patient …
GH Utter, J Cuny, A Strater, MR Silver, S Hossli… – Medical care, 2012 –
Background: The Agency for Healthcare Research and Quality Patient Safety Indicator (PSI) 11 uses In.

A combined AdaBoost and NEWFM technique for medical data classification
KA Abuhasel, AM Iliyasu, C Fatichah – Information Science and …, 2015 – Springer
… To validate the proposal, we applied in a medical classification task for the diagnosis of epileptic seizures, Parkinson, cardio- vascular (heart), and hepatitis ailments. Section 2 presents a brief description of the AdaBoost ensemble method and the NEWFM method. …

Three-Term Backpropagation Network based on elitist multiobjective genetic algorithm for medical diseases diagnosis classification
AO Ibrahim, SM Shamsuddin… – Life Science …, 2013 –
… Furthermore, Multiobjective evolutionary Algorithms MOEAs research is one of the hottest areas in the field of evolutionary computation [7]. So, there are various methods that have used MOOP to solve medical classification problems and other problems. …

A framework for medical images classification using soft set
SA Lashari, R Ibrahim – Procedia Technology, 2013 – Elsevier
… for the classification of medical images. Thus, current medical classification approaches have been reviewed with an emphasis placed on the different classification methods for medical imaging applications. As a result, a new …

Precision-Recall-Optimization in Learning Vector Quantization Classifiers for Improved Medical Classification Systems
T Villmann, M Kaden, M Lange… – … and Data Mining ( …, 2014 –
Abstract: Classification and decision systems in data analysis are mostly based on accuracy optimization. This criterion is only a conditional informative value if the data are imbalanced or false positive/negative decisions cause different costs. Therefore more sophisticated

Hybrid metaheuristics for medical data classification
S Al-Muhaideb, M El Bachir Menai – Hybrid Metaheuristics, 2013 – Springer
… Several medical classification tasks exist, among which medical diagnosis and prognosis are most common. … Identifying the most suitable strategy for a particular medical classification problem along with its optimal parameters is no less difficult. …

Measuring diversity in medical reports based on categorized attributes and international classification systems
P P?e?ková, J Zvárová, K Zvára – BMC medical …, 2012 – bmcmedinformdecismak. …
… than 100 classifications systems. ICD is one of the oldest medical classification systems. The foundation was laid in 1855. The World Health Organization took it over in 1948. At that time it was its 6th revision. Since 1994 the …

Identification and classification of diseases: Fundamental problems in medical ontology and epistemology
L Nordenfelt – Studia Philosophica Estonica, 2013 –
… In the history of medical ideas the term ‘physiologism’ stands for a par- ticular attitude towards the task of medical classification. … What does “the same” mean in such a case? We see here in a nutshell an almost insurmountable problem for medical classification. …

A comprehensive analysis on associative classification in medical datasets
D Sasirekha, A Punitha – Indian Journal of Science and Technology, 2015 –
… algorithm. This method performs high level rules that are accurate and comprehensible, and contains high interesting value. K. Ruth Ramya et al.21 proposed class based approach for medical classification of chest pain. The …

A hybrid FAM–CART model and its application to medical data classification
M Seera, CP Lim, SC Tan, CK Loo – Neural Computing and Applications, 2015 – Springer
… 4 Benchmark medical classification. In this section, an empirical evaluation using a total of six publicly available medical data sets from the UCI machine learning repository [38] is presented. A summary of the numbers of samples and attributes are shown in Table 2. Table 2 …

Feature selection for classification incorporating less meaningful attributes in medical diagnostics
A Wosiak, D Zakrzewska – Computer Science and Information …, 2014 –
… consequence, new therapies. We consider the methodology, which aims at indicating these features from among less meaningful for medical classification, that can be used in automated diagnosis of the disease. The proposed …

Designing Ontology-Based Knowledge Integration for Preprocessing of Medical Data in Enhancing a Machine Learning System for Coding Assignment of a …
P Waraporn – Proceedings of World Academy of Science, …, 2013 –
… The preliminary results obtained show a satisfactory finding that the use of medical ontologies improves the overall system performance. Keywords—Medical Ontology, Knowledge Integration, Machine Learning, Medical Coding, Text Assignment. …

Mortality from hypertension: reliability of medical coding of death certificates in primary healthcare
ZV Lashkul – Wiadomosci lekarskie (Warsaw, Poland: 1960), 2014 –
OBJECTIVE: To examine the reliability of accounting causes of death of the population of hypertension in the centers of primary health care at the regional level and to detect defects that affect the quality of coding of causes of death. MATERIALS AND METHODS: Statistics,

An ensemble of fine-tuned convolutional neural networks for medical image classification
A Kumar, J Kim, D Lyndon, M Fulham… – IEEE journal of …, 2017 –
Page 1. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 21, NO. 1, JANUARY 2017 31 An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification Ashnil Kumar, Member …

Combined methodology of the classification rules for medical data-sets.
VS Latha, PYL Swetha, M Bhavya, G Geetha… – International Journal of …, 2012 –
… [1] “A Hybrid Data Mining Method for the Medical Classification of Chest Pain”authored by Sung Ho Ha and Seong Hyeon Joo [2]“Performance Evaluation of Decision Tree Classifiers on Medical Datasets” authored by D.Lavanya Dr. K.Usha Rani Research Scholar Dept. …

The self-organizing maps of Kohonen in the medical classification
M Zribi, Y Boujelbene, I Abdelkafi… – Sciences of Electronics, …, 2012 –
Abstract: In Tunisia, breast cancer is the most common cancer among women; it presents the leading cause of female mortality in the age group 35 to 55 years. This paper uses a neural approach based on Kohonen self-organizing maps to perform a classification of tumors

Rough–Granular Computing knowledge discovery models for medical classification
MM Eissa, M Elmogy, M Hashem – Egyptian Informatics Journal, 2016 – Elsevier
Abstract Medical domain has become one of the most important areas of research in order to richness huge amounts of medical information about the symptoms of diseases and how to distinguish between them to diagnose it correctly. Knowledge discovery models play vital

Investigation of Alternative Evolutionary Prototype Generation in Medical Classification
C Stoean, R Stoean, A Sandita – Symbolic and Numeric …, 2014 –
Abstract: The response of a computational system to support medical diagnosis should simultaneously be accurate, comprehensible, flexible and prompt in order to be qualified as a reliable second opinion. Based on the above characteristics, the current paper examines

ICD-10 Medical Coding: The Role of Perioperative Services in Addressing Implementation Challenges
TL Wing – AORN journal, 2016 – Elsevier
Abstract The International Classification of Diseases, 10th Revision (ICD-10) was adopted in the United States on October 1, 2015. Replacing the outdated ICD, Ninth Revision, Clinical Modification (ICD-9-CM) coding system was long overdue, and the updated classifications

A Survey of Multi-Agent based Intelligent Decision Support System for Medical Classification Problems
H Salem, G Attiya, N El-Fishawy – International Journal of …, 2015 –
Abstract There has been growing on big data since last decade for discovering useful trends or patterns that are used in diagnosis and decision making. Intelligent decision support system an automated judgment that supports decision making is composed of human and

Mastering Medical Coding-E-Book
M Diamond – 2013 –
This practical approach to coding provides a solid foundation in basic coding principles with an emphasis on learning through realistic physician documentation. It prepares students to tackle any coding scenario, from routine to complex. Chapters begin with an emphasis on

Interpretable and accurate medical data classification–a multi-objective genetic-fuzzy optimization approach
MB Gorza?czany, F Rudzi?ski – Expert Systems with Applications, 2017 – Elsevier
… It is worth stressing that the overwhelming majority of the existing medical classification methods concentrate almost exclusively on the accuracy issues. Keywords. Accuracy and interpretability of medical classification systems …

“Mad, Bad and Dangerous to Know”: The pervasive socio-medical and spatial coding of mental health day centres
LA Smith, I Tucker – Emotion, space and society, 2015 – Elsevier
… November 2014. Highlights. • ‘Othering’ within ‘Otherness’. • The pervasive nature of socio-medical coding within mental health day centres. • Unconventional body behaviour and exclusion within mental health day centres. • The …

Gender as an ‘interplay of rules’: Detecting epistemic interplay of medical and legal discourse with sex and gender classification in four editions of the Dewey …
MJ Fox – 2015 –
Gender as an ‘interplay of rules’: Detecting epistemic interplay of medical and legal discourse with sex and gender classification in four editions of the Dewey Decimal Classification. Abstract. When groups of people are …

Medical image modality classification using discrete Bayesian networks
J Arias, J Martínez-Gómez, JA Gámez… – Computer Vision and …, 2016 – Elsevier
In this paper we propose a complete pipeline for medical image modality classification focused on the application of discrete Bayesian network classifiers. Moda.

International Medical Classifications from the Terminological Point of View (Cases: International Classification of Diseases and Terminologia Anatomica)
I Kudashev – Kielet liikkeessä. VAKKI-symposiumi XXXII. Vaasa, 2012 –
… Pohdin myös teoreettisia kysymyksiä, joita luokittelujen käsittely on herättänyt, mm. luokitte- luissa käytettyjen luokkien nimien ja tunnisteiden luonnetta ja asemaa. Keywords: terminology work, terminology management, classification, medical classification 1 Introduction …

Optimized Convolutional Neural Network Ensembles for Medical Subfigure Classification
S Koitka, CM Friedrich – International Conference of the Cross-Language …, 2017 – Springer
… Koitka, S., Friedrich, CM: Traditional feature engineering and deep learning approaches at medical classification task of ImageCLEF 2016. … Pelka, O., Friedrich, CM: FHDO biomedical computer science group at medical classification task of imageclef 2015. …

An Analytical Study on Classification Algorithms for Medical Datasets
A Sharma, S Sharma – International Journal of Electrical Electronics & …, 2015 –
… II. EXISTING WORK Lot of work is already defined by various researchers on medical classification algorithm. Some of the work defined by different researchers on different medical domains and approaches is presented in this section. …

Notice of Violation of IEEE Publication Principles A framework for medical image classification using soft set
NK Anitha, G Keerthika, M Maheswari… – Current Trends in …, 2014 –
… for the classification of medical images. Thus, current medical classification approaches have been reviewed with an emphasis placed on the different classification methods for medical imaging application. As a result, a new …

Optimizing voting classification using cluster analysis on medical diagnosis data
A Tamvakis, CN Anagnostopoulos… – Proceedings of the 16th …, 2015 –
… To this aim, the objectives of the study are (a) to assess the efficiency of base classifiers in different medical classification tasks, (b) to determine and analyze the classifier combinations with high voting performance (c) to identify the combination consisting of classifiers with the …

The Role of Medical Classification and Coding in Clinical Quality Assurance
Q JIANG, Z ZHANG, Y ZHAO – Chinese Health Quality …, 2012 –
Medical classification is the core of standardized medical information, and it’s also important for hospital quality management. It includes 3 main reference classifications, derived classification and other types of classifications, casemix and DRGs classification system is

in the Classification of Medical Concepts
R Klar – … , Data Analysis, and Knowledge Organization: Models …, 2012 –
… This center should be responsible for the coordination of semantical clas- sifications, updating classifications and thesauri, computer-assisted encoding and especially for converting systems from one medical classification to another. …

Modelling Approaches and Results of the FHDO Biomedical Computer Science Group at ImageCLEF 2015 Medical Classification Task
O Pelka, CM Friedrich – 2015 –
… Modelling Approaches and Results of the FHDO Biomedical Computer Science Group at ImageCLEF 2015 Medical Classification Task … I First participation in ImageCLEF 2015 Medical Classification Task I Biomedical Computer Science Group (BCSG) 1 Task1 – Compound Figure

Medical Coding, an Universal Language in the 21s Century
SP Georgescu, AB Tanase – Romanian Journal of Clinical and …, 2016 –
Since Hippocrates, physicians have tried to classify diseases and related information (signs, symptoms, abnormal findings, social circumstances, external causes, etc). After several inconsistent approaches, the first modern system of codes named the International

Challenges in Medical Coding-Concomitant Medication Therapy Data in Clinical Trials
M Dziedziurko – 2017 –
Clinical trials are intended to prove or disprove a hypothesis set in the study protocol. For this reason, the quality of captured data plays an important role in the study outcome. From a Data Management (DM) perspective, the aim of the clinical study is to achieve clean,

Medical Classification and Terminology Systems in a Secondary Use Context: Challenges and Perils.
H Hund, S Gerth, HA Katus, C Fegeler – MIE, 2016 –
Abstract. Since the introduction of diagnosis-related groups in the German healthcare system, classifying patient diagnosis and procedures with controlled vocabularies have become mandatory and thus creating a large dataset for secondary use in biomedical

A multi objective approach to evolving artificial neural networks for medical classification tasks
A Shenfield, S Rostami –
Abstract—The optimisation of the accuracy of classifiers in pattern recognition is a complex problem that is often poorly understood. Whilst numerous techniques exist for the optimisation of weights in artificial neural networks (eg the Widrow-Hoff least mean squares

G205 (P) Improving quality of medical coding to attract appropriate payment tariffs: A quality improvement project
N Williams, A Howells, J Ganapathi – 2016 –
Background Accurate and legible documentation is essential; not only for safe patient care, but also for aiding the coding department to accurately code the clinical episode. After an episode, hospital clinical coders go through documentation on medical notes to assign ICD-

Increasing Compliance With Documentation Using Modern Electronic Health Record Systems Toward Quality Improvement in Radiation Practice Medical Coding
SE Braunstein, W Silveira, J Alexander… – International Journal of …, 2013 –
Results The overall departmental baseline ICD-9 coding accuracy in 2010 was 81.5% with lowest accuracy within CNS (67.3%) and head and neck (85.5%) subsites. Highest accuracy was within breast (98.3%) and gastrointestinal (94.7%) malignancies. In 2012, the overall

Case-Based Interpretation of Best Medical Coding Practices—Application to Data Collection for Cancer Registries
M Schnell, S Couffignal, J Lieber, S Saleh… – Case-Based Reasoning …, 2017 – Springer
Abstract. Cancer registries are important tools in the fight against cancer. At the heart of these registries is the data collection and coding process. Ruled by complex international standards and numerous best practices, operators are easily overwhelmed. In this paper, a

Indeterminacy in medical classification: On continuity, uncertainty, and vagueness
R Hauswald, L Keuck – Vagueness in Psychiatry, 2016 –
This chapter aims to clarify the vocabulary of and relations among ontological, epistemological, and semantic aspects of indeterminacy in medical classification systems. Although classifications of diseases and of mental disorders are often characterized as

Help! Where’s the code for this? Here’s what you can do when clinical care and medical coding don’t match
J Rumpakis – Review of Optometry, 2012 –
Items in My Folder and/or Highlights & Notes may not have been saved to Google Drive™ or Microsoft OneDrive™. Are you sure you want to logout? … ListenLarger documents may require additional load time. … Has this ever happened to you? You go to a clinical lecture and hear

Accuracy of Medical Coding Algorithms to Identify Complex Conditions in United States Hospitals: The Case of Sepsis
S Shahraz – 2014 –
Abstract Administrative claims and hospital discharge data are used for provider payment and to estimate burden of disease. Despite the extensive use of such data, little is known about the accuracy of the data for conditions like sepsis for which medical coders need to

Medical Coding in the United States: Introduction and Historical Overview
KR Borman – Principles of Coding and Reimbursement for Surgeons, 2017 – Springer
Abstract The Merriam-Webster definition of “code” includes the concepts of a system of principles or rules, a system of letter and number symbols used to represent assigned meanings, and a set of instructions for a computer [1]. Medical diagnostic and procedural

A Classification Method to Extract Knowledge from Text Documents: A novel Cluster-Classification Method for accurate classification of medical text reports
F Saad, B de la Iglesia, D Bell – 2012 –
… Importantly, the addition of clustering features further improves the accuracy of the final classifier. Results are cross-checked using different medical classification tasks. top of page AUTHORS. Fathi Saad No contact information provided yet. Bibliometrics: publication history …

The opportunity to evaluate the impact of our changing health care system through archetypes and reinforced use of medical coding
M Bradway, R Pedersen… – … of Integrated Care, 2016 –
Abstract Intro: We address two major problems related to electronic health record (EHR) systems in Norway: 1) a disconnect between health care personnel when using separate systems, and 2) a difficulty to accurately and easily track the financial and clinical impact of

The forensic medical classification of damages to the liver in the case of a blunt abdominal injury
II Pigolkin, IA Dubrovina, IA Dubrovin… – Sudebno- …, 2012 –
Abstract The present work had the objective to evaluate the existing classifications of damages to the liver associated with a blunt abdominal injury. An original forensic medical classification of hepatic lesions has been developed taking into consideration both

A Test of a Blended Method for Teaching Medical Coding
OD Meredith Whiteside, S Ge… – Optometric …, 2017 –
Background: Evaluation and Management (E/M) codes are used by all healthcare providers to bill third-party payers for their services. The purpose of this study is to determine whether providing education about E/M coding to third-year optometry students in a blended

Adapting Pre-trained Word Embeddings For Use In Medical Coding
K Patel, D Patel, M Golakiya, P Bhattacharyya, N Birari – BioNLP 2017, 2017 –
Abstract Word embeddings are a crucial component in modern NLP. Pre-trained embeddings released by different groups have been a major reason for their popularity. However, they are trained on generic corpora, which limits their direct use for domain

A Framework for Medical Service Collaboration Using Medical Coding System
JJW Yoo, K Gnanasekaran – IIE Annual Conference. …, 2012 –
Abstract Collaboration is becoming increasingly critical in many business fields to maximize revenue and minimize cost by taking advantage of expertise of others. For example, Intel, Samsung, LG and Sharp are major suppliers of Apple for iPhone, and they collaborate even

Jedi-jedi: Towards A Formal Medical Classification Of A Sugar Problem In Africans
P Brimah, R Adigun – RGUILD, 2014 –
Objective:“Jedi-jedi” is a very common presenting complaint in medical centers and social circles in West Africa. The symptom, constellation of symptoms or syndrome is not formally classified as a disease entity or syndrome in orthodox medical practice. Low back pain and

Workbook for Step-by-Step Medical Coding, 2015 Edition-E-Book
CJ Buck – 2014 –
Strengthen your ability to code accurately and obtain optimal reimbursement for medical services! Corresponding to the chapters in Carol J. Buck’s Step-by-Step Medical Coding, 2015 Edition, this workbook offers review and practice with more than 1,500 questions and

The Next Step: Advanced Medical Coding and Auditing
CJ Buck – 2015 –
Mastering advanced medical coding skills is easier with Carol J. Buck’s proven, step-by-step method! The Next Step: Advanced Medical Coding and Auditing, 2016 Edition uses real-world patient cases to explain coding for services such as medical visits, diagnostic testing

Learner retention of medical vocabulary based on instructional format and success in medical coding
MR Gruich – 2013 –
Abstract This quantitative research study explores the correlation between instructional formats and the measurement of knowledge retention, as well as subsequent course mastery and whether other factors such as age, ethnicity, gender, number of study hours,

ChargeMed: Development of a Mobile Application for Medical Coding and Billing within the Ontario Healthcare Environment
A Agarwal, T Xenodemetropoulos… – … (HICSS), 2013 46th …, 2013 –
Abstract: Traditional processes involved in coding and billing for medical services performed by physicians present many inherent challenges in complete charge capture, accurate coding and entry as well as timely submission for reimbursement. Process improvement

The Next Step: Advanced Medical Coding and Auditing, 2017/2018 Edition-E-Book
CJ Buck – 2016 –
Mastering advanced medical coding skills is easier with Carol J. Buck’s proven, step-by-step method! The Next Step: Advanced Medical Coding and Auditing, 2017/2018 Edition shows how to code for services such as medical visits, diagnostic testing and interpretation,

Outpatient Coding for Medical Services
PJ Enking – Essential Clinical Procedures E-Book, 2013 –
… aapc. com/icd-10/icd-10-white-paper. aspx. 2. American Health Information Management Association. Medical coding: http://www. ahima. org/coding. 3. Centers for Medicare & Medicaid Services. The ICD-10 transition: an introduction: http://www. cms. …

Hybrid real-coded genetic algorithm and MIMO CMAC NN classifier for solving medical data classification problems
JY Wu – International Conference on Brain Inspired Cognitive …, 2013 – Springer
… The external RGA method is used to optimize the optimal parameter settings of the internal MIMO CMAC NN classifier, and the internal MIMO CMAC NN classifier is applied to solve benchmark medical classification problems. …

On the Influence of Class Noise in Medical Data Classification: Treatment Using Noise Filtering Methods
JA Sáez, B Krawczyk, M Wo?niak – Applied Artificial Intelligence, 2016 – Taylor & Francis
… The rest of this article is organized as follows. “Medical Data Classification in Presence of Class Label Noise” introduces medical classification with noisy data. “Noise Filtering Methods” details the use of noise filters, paying atten- tion to those considered in this work. …

Shallow Training is cheap but is it good enough? Experiments with Medical Fact Coding
R Nallapati, R Florian – ACL-IJCNLP 2015, 2015 –
… pipeline. In this work, we investigate the feasibility of a shal- low medical coding model that trains only on fact annotations, while disregarding fact-attributes and relations, potentially saving considerable annota- tion time and costs. …

On Classification with Unreliable Labels for Environmental and Medical Applications
MNH Hajjchehade – 2012 –
… In this dissertation, we consider environmental and medical classification problems where the validation process is challenging due to the difficulty of collecting class labels and ground truth. We divide our work into three parts. …

Impact of preprocessing on medical data classification
S Almuhaideb, MEB Menai – Frontiers of Computer Science, 2016 – Springer
… MDC is dif- ferent from medical classification, or medical coding, which is the process of assigning internationally endorsed classifi- cation codes to each medical diagnosis and procedure (the WHO Family of International Classifications1)). …

Variation between Hospitals with Regard to Diagnostic Practice, Coding Accuracy, and Case-Mix. A Retrospective Validation Study of Administrative Data …
J Helgeland, DT Kristoffersen, KD Skyrud, AS Lindman – PloS one, 2016 –
Background The purpose of this study was to assess the validity of patient administrative data (PAS) for calculating 30-day mortality after hip fracture as a quality indicator, by a retrospective study of medical records. Methods We used PAS data from all Norwegian hospitals (2005 …

JG Kiongo, JW Gichuhi, JG Kiongo –
… mortality and morbidity data. Disease classification, or medical coding, is the process of transforming descriptions of medical diagnoses and procedures into universal medical code numbers. The diagnoses and procedures …

Task-Driven Dictionary Learning Based on Mutual Information for Medical Image Classification
I Diamant, E Klang, M Amitai, E Konen… – IEEE Transactions …, 2017 –
… In the current work, we focus on three medical classification tasks: chest x-ray pathology identification, benign/malignant clusters of microcalcifications (MCs) classification in breast mammograms, and liver lesion classification in portal phase computed tomography (CT) images. …

Feature selection and representation for probabilistic neural network in medical data classification tasks
M Kusy – International Journal of Electronics and …, 2015 –
… In Section V, a short experimental study concerned with the results of feature selection and extraction in medical classification problems performed by various machine learning algorithms is outlined. The paper is concluded in SectionVI. Page 3. …

Introducing a new medical waste tracking and classification system for Jordan
B Y. Ammary – World Journal of Science, Technology and …, 2014 –
… Jordan. The introduction of a manifest system and the adoption of a medical classification system, in addition to the calculation of the generation rates are very important for sustainable development in the country. Keywords …

Classification of Medical Images and Illustrations in the Biomedical Literature Using Synergic Deep Learning
J Zhang, Y Xia, Q Wu, Y Xie – arXiv preprint arXiv:1706.09092, 2017 –
… probability. We have evaluated it on the ImageCLEF2016 medical classification challenge dataset and the experimental results show that our proposed SDL model is the state-of-the-art on the medical classification problem. II. …

Classifier Ensemble Selection Based on mRMR Algorithm and Diversity Measures: An Application of Medical Data Classification
S Cheriguene, N Azizi, N Dey, AS Ashour… – International Workshop …, 2016 – Springer
… that affect the patients’ health. In medical classification modalities, accuracy is very important [3], which led to the development of several medical data applications and intelligent classifiers [4, 5, 6]. The MCSs are very efficient …

Natural Language Processing Based Instrument for Classification of Free Text Medical Records
M Khachidze, M Tsintsadze… – BioMed research …, 2016 –
… we updated the Georgian words database [28] with more nouns and terms used in Georgian medical terminology according to ICD-10- (the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD), a medical classification list by …

Dimensionality Reduction for Probabilistic Neural Network in Medical Data Classification Problems
M Kusy – International Journal of Electronics and …, 2015 –
… In Section V, a short experimental study concerned with the results of feature selection and extraction in medical classification problems performed by various machine learning algorithms is outlined. The paper is concluded in SectionVI. …

Modality-bridge Transfer Learning for Medical Image Classification
HG Kim, Y Choi, YM Ro – arXiv preprint arXiv:1708.03111, 2017 –
… B. Overall Framework of the Modality-bridge Transfer Learning for Medical Image Classification Fig. 2 shows the overall framework of the proposed modality-bridge transfer learning for medical classification using domain adaptation. …

A New RBFNDDA-KNN Network and Its Application to Medical Pattern Classification
SC Tan, CP Lim, RF Harrison, RL Kennedy – Soft Computing in Industrial …, 2014 – Springer
… 3.2 A Real Medical Problem. In this experiment, a data set comprising 500 patient records collected from the Northern General Hospital, Sheffield, UK, was used to evaluate the applicability of the RBFNDDA-KNN network to real-world medical classification problems. …

Medical Billing & Coding for Dummies
K Smiley – 2015 –
… 17 Looking at Medical Coding….. 17 Verifying documentation….. … 21 Transforming visits into revenue….. 22 Determining whether medical coding suits you….. …

Outpatient Coding for Medical Services
J PATRICK – … Clinical Procedures: Expert Consult-Online and …, 2013 –
… aapc. com/icd-10/iccl-lO-white-paper. aspx. 2. American Health Information Management Association. Medical coding: http://Www. ahima. org/coding. 3. Centers for Medicare & Medicaid Services. The ICD-lO transition: an introduction: http://WWw. cms. …

J Zhang, C Chen, Y Xiang, W Zhou, Y Xiang – 2016 –
… ARTICLE INFO ABSTRACT Corresponding Author: Dr.B.Murugeshwari Professor, Department of Information Technology,Velammal Institute of Technology Keywords: Naive Bayesian classification, medical classification, Data mining …

Intelligent methods for automatic classification of medical images
MR Zare – 2013 –

Deep Transfer Learning for Modality Classification of Medical Images
Y Yu, H Lin, J Meng, X Wei, H Guo, Z Zhao – Information, 2017 –
Medical images are valuable for clinical diagnosis and decision making. Image modality is an important primary step, as it is capable of aiding clinicians to access required medical image in retrieval systems. Traditional methods of modality classification are dependent on the choice …