Course Overview Without adequate knowledge and expertise, medical coding can be a very complex task. That is why Jante has created the Medical Coding: ICD-10-CM Essentials course, where you can educate yourself on the crucial aspects of clinical coding before stepping into this sector. In this Medical Coding: ICD-10-CM Essentials course, you will learn all the key information of general coding. The course will help you grasp the codes used while treating Cancer. The instructor will teach you the specific coding for infectious and parasitic diseases. In addition, you will receive elaborate lessons on endocrine, nutritional, and metabolic diseases and their coding's. The course curriculum also includes modules on pain coding guidelines along with glaucoma and blindness coding guidelines. This Medical Coding: ICD-10-CM Essentials course is the perfect training ground for aspiring professionals in this sector. If you are one of them, enroll today to fast-track your career. Learning Outcomes Enrich your understanding of general coding guidelines Learn the code for specific infectious and parasitic diseases Broaden your understanding of Cancer-related coding Deepen your knowledge of Endocrine, Nutritional, and Metabolic Diseases Attain lessons on nervous system diseases and mental disorders Who is this course for? The Medical Coding: ICD-10-CM Essentials course is for those who are interested to learn medical coding Entry Requirement This course is available to all learners, of all academic backgrounds. Learners should be aged 16 or over to undertake the qualification. Good understanding of English language, numeracy and ICT are required to attend this course. Certification After you have successfully completed the course, you will be able to obtain an Accredited Certificate of Achievement. You can however also obtain a Course Completion Certificate following the course completion without sitting for the test. Certificates can be obtained either in hardcopy at the cost of £39 or in PDF format at the cost of £24. PDF certificate's turnaround time is 24 hours, and for the hardcopy certificate, it is 3-9 working days. Why choose us? Affordable, engaging & high-quality e-learning study materials; Tutorial videos/materials from the industry-leading experts; Study in a user-friendly, advanced online learning platform; Efficient exam systems for the assessment and instant result; The UK & internationally recognized accredited qualification; Access to course content on mobile, tablet or desktop from anywhere anytime; The benefit of career advancement opportunities; 24/7 student support via email. Career Path The Medical Coding: ICD-10-CM Essentials course is a useful qualification to possess and would be beneficial for any related profession or industry such as: Medical Coder Nurse Medical Records Coordinator Medical Coding Specialist Medical Billing Specialist Medical coding Introduction Medical Coding Introduction 00:15:00 Placeholder 00:01:00 How to Use Medical Coding Books? 00:36:00 Conventions for the ICD-10-CM Conventions Introduction 00:03:00 Conventions Part-1 00:19:00 Conventions Part-2 00:17:00 Conventions Part-3 00:17:00 Conventions Part-4 00:37:00 General Coding Guidelines Locating a code in the ICD-10-CM 00:02:00 Level of Detail in Coding 00:01:00 Code or codes from A00.0 through T88.9, Z00-Z99.8 00:01:00 Signs and symptoms 00:02:00 Conditions that are an integral part of a disease process 00:01:00 Conditions that are not an integral part of a disease process 00:02:00 Multiple coding for a single condition Part-1 00:02:00 Multiple coding for a single condition Part 2 00:01:00 Acute and Chronic Conditions 00:02:00 Combination Code 00:02:00 Sequela (Late Effects) 00:02:00 Impending or Threatened Condition 00:01:00 Reporting Same Diagnosis Code More than Once 00:01:00 Laterality 00:02:00 Documentation by Clinicians Other than the Patient's Provider 00:02:00 Documentation of Complications of Care Part 1 00:02:00 Documentation of Complications of Care Part 2 00:01:00 Borderline Diagnosis 00:01:00 Use of Sign -Symptom- Unspecified Codes 00:02:00 Coding for Healthcare Encounters in Hurricane Aftermath 00:06:00 Chapter Specific Coding Guidelines Introduction Digital Medical Coding Mastery (DMCM) 00:05:00 Chapter 1: Certain Infectious and Parasitic Diseases (A00-B99) HIV Coding Guidelines 00:13:00 Infections resistant to antibiotics 00:02:00 Coding of Sepsis, Severe Sepsis, and Septic Shock 00:13:00 Zika virus infections 00:02:00 Chapter 2: Neoplasms (C00-D49) Introduction of Neoplasm or Cancer 00:02:00 Treatment directed at Malignancy or Cancer 00:02:00 Treatment of Secondary Cancer site 00:02:00 Coding & Sequencing of complications 00:04:00 Primary malignancy previously excised 00:01:00 Encounters involving chemotherapy, immunotherapy and radiation therapy 00:02:00 Encounter to determine extent of malignancy 00:01:00 Signs and Symptoms with Neoplasms 00:02:00 Admission for pain control management 00:01:00 Malignancy in two or more non-contiguous sites 00:02:00 Disseminated Malignant Neoplasm 00:01:00 Malignant Neoplasm without specification of site 00:01:00 Sequencing of Neoplasm codes 00:06:00 Current Malignancy versus personal history of Malignancy 00:04:00 Leukemia, Multiple Myeloma 00:02:00 Aftercare, Follow-up, Prophylactic organ removal 00:05:00 Chapter 3: Endocrine, Nutritional, and Metabolic Diseases (E00-E89) Introduction Endocrine, Nutritional, and Metabolic Diseases 00:01:00 Diabetes mellitus (DM) 00:01:00 Diabetes mellitus and use of insulin 00:01:00 DM in Pregnancy 00:01:00 Complications due to Insulin Pump Malfunction 00:02:00 Secondary Diabetes Mellitus (DM) 00:06:00 Chapter 4: Mental, Behavioral and Neurodevelopmental disorders (F01 - F99) Pain Disorder related to Psychological Factor 00:02:00 MSBP (Munchausen's Syndrome By Proxy) 00:05:00 Chapter 5: Diseases of the Nervous System (G00-G99) Introduction of Nervous System 00:04:00 Pain Coding Guidelines 00:03:00 Pain due to Device and Post OP Pain 00:03:00 Post OP Pain associated 00:01:00 Chronic Pain, Neoplasm related and Chronic pain Syndrome 00:05:00 Tip for Today 00:03:00 Chapter 6: Diseases of the Eye and Adnexa (H00-H59) Glaucoma Coding Guidelines 00:07:00 Blindness Coding Guidelines 00:07:00 Chapter 7: Diseases of the Circulatory System (I00-I99) Hypertension (HTN) Coding Guidelines 00:06:00 Different types of Hypertension combination codes 00:10:00 Coronary Artery Disease (CAD) Coding Guidelines 00:04:00 Intra and Postoperative CVA 00:03:00 Acute and Sequela Cerebrovascular Accident (CVA) 00:06:00 Myocardial Infarction (MI) 00:09:00 Tip for Today 00:03:00 Chapter 8: Diseases of the Respiratory System (J00-J99) Guidelines for COPD 00:07:00 Guidelines for Respiratory Failure and Influenza 00:07:00 Ventilator Associated Pneumonia (VAP) 00:05:00 Chapter 9: Diseases of the Skin and Subcutaneous Tissue (L00-L99) Pressure Ulcer Coding Guidelines 00:07:00 Non Pressure Ulcer Coding Guidelines 00:06:00 Chapter 10: Diseases of the Musculoskeletal System & Connective Tissue (M00-M99) Musculoskeletal System 00:14:00 Chapter 11: Diseases of Genitourinary System (N00-N99) CKD & Transplant status 00:09:00 Tip for you 00:02:00 Chapter 12: Pregnancy, Childbirth, and the Puerperium (O00-O9A) General Rules to code Pregnancy 00:15:00 Selection of Codes in obstetrics 00:10:00 Pre-existing conditions in Pregnancy 00:07:00 Pregnancy Combination conditions 00:17:00 Postpartum and Peri partum 00:09:00 Termination and Spontaneous Abortion 00:09:00 Chapter 13: Certain Conditions Originating in the Perinatal Period (P00-P96) Newborn General Guidelines 00:10:00 Additional Diagnosis and Newborn codes 00:07:00 Chapter 14: Congenital malformations, deformations, chromosomal abnormal(Q00-Q99) Congenital Malformation Codes 00:07:00 Chapter 15: Symptoms, signs, & abnormal clinical & laboratory findings (R00-R99) Symptoms Guidelines R Series 00:16:00 Chapter 16: Injury, poisoning, certain consequences of external causes (S00-T88) Coding Injury & Fracture 00:26:00 Coding of Burns and Corrosions 00:15:00 Adult, Child Abuse, Neglect 00:08:00 Adverse effect, Poisoning 00:17:00 Complication of Care 00:09:00 Conclusion Conclusion 00:01:00 Certificate and Transcript Order Your Certificates and Transcripts 00:00:00
For anyone with a sense of adventure Army, Air or Sea Cadets, GAP Year, CCF, School trips and exotic family adventure holidays The Active First Aid course is designed to give you the theoretical knowledge to learn how to keep yourself and others safe and to know how to help should an accident happen. The course is ideal for anyone with a sense of adventure as a vital life skill to equip you with the skills to help an adult or child in a medical emergency if help may not be immediately available. The course covers CPR, choking, the recovery position, drowning, burns, bites and stings and much more. The course consists of illustrated step by step directions, flow charts, diagrams, videos and test yourself sections fully compatible with all computers and mobile devices. You will be able to stop and start as often as you like and print your Certificate on completion. You will have continuous access to the course for 12 months. It is impossible to cover all eventualities within this course, or to equip you with the knowledge and skills to appropriately diagnose and treat in unpredictable real life situations. If you suspect serious illness or injury, you should always seek immediate professional medical advice. The Author has made every effort to ensure the accuracy of the information contained within the course, however this course is merely a guide and the Author does not accept any liability or responsibility for any inaccuracies or for any mistreatment or misdiagnosis of any person, however caused. The course material has been written by Emma Hammett, Qualified Nurse, First Aid Trainer and founder of First Aid for Life in conjunction with other medical and first aid professionals. If you have any queries concerning this course, please contact emma@firstaidforlife.org.uk Course contents: Action in an emergency Role of the First Aider The Primary Survey – How to help in an Emergency Unresponsive and Breathing Recovery Position Secondary Survey Unconscious and not breathing Resuscitation Heart Attack and Angina Breathing Problems Choking Drowning Asthma Panic Attacks and hyperventilation Anaphylactic Shock Wounds and bleeding Shock Fainting Bites and Stings Burns Breaks, Sprains and Dislocations Head Injuries Spinal Injuries Fitting / Seizures / Convulsions Extremes of body temperature Diabetes
It is a requirement of the Health & Safety law that employers provide a safe and healthy environment to work in. If your assessment of first aid needs highlights the need for qualified first aiders, then this level 3 qualification (level 6 in Scotland) will provide you with suitable, highly trained personnel. This comprehensive three-day course covers a wide range of first aid emergencies, enabling all participants to deal with emergency situations with confidence in a prompt, safe and effective way. In addition, this course will include any protocol changes that may have arisen since their last training course.
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The 'Complete Python Machine Learning & Data Science Fundamentals' course covers the foundational concepts of machine learning, data science, and Python programming. It includes hands-on exercises, data visualization, algorithm evaluation techniques, feature selection, and performance improvement using ensembles and parameter tuning. Learning Outcomes: Understand the fundamental concepts and types of machine learning, data science, and Python programming. Learn to prepare the system and environment for data analysis and machine learning tasks. Master the basics of Python, NumPy, Matplotlib, and Pandas for data manipulation and visualization. Gain insights into dataset summary statistics, data visualization techniques, and data preprocessing. Explore feature selection methods and evaluation metrics for classification and regression algorithms. Compare and select the best machine learning model using pipelines and ensembles. Learn to export, save, load machine learning models, and finalize the chosen models for real-time predictions. Why buy this Complete Python Machine Learning & Data Science Fundamentals? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Complete Python Machine Learning & Data Science Fundamentals there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This Complete Python Machine Learning & Data Science Fundamentals course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill. Prerequisites This Complete Python Machine Learning & Data Science Fundamentals does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Complete Python Machine Learning & Data Science Fundamentals was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Complete Python Machine Learning & Data Science Fundamentals is a great way for you to gain multiple skills from the comfort of your home. Course Curriculum Course Overview & Table of Contents Course Overview & Table of Contents 00:09:00 Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types 00:05:00 Introduction to Machine Learning - Part 2 - Classifications and Applications Introduction to Machine Learning - Part 2 - Classifications and Applications 00:06:00 System and Environment preparation - Part 1 System and Environment preparation - Part 1 00:08:00 System and Environment preparation - Part 2 System and Environment preparation - Part 2 00:06:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 1 00:10:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 2 00:09:00 Learn Basics of python - Functions Learn Basics of python - Functions 00:04:00 Learn Basics of python - Data Structures Learn Basics of python - Data Structures 00:12:00 Learn Basics of NumPy - NumPy Array Learn Basics of NumPy - NumPy Array 00:06:00 Learn Basics of NumPy - NumPy Data Learn Basics of NumPy - NumPy Data 00:08:00 Learn Basics of NumPy - NumPy Arithmetic Learn Basics of NumPy - NumPy Arithmetic 00:04:00 Learn Basics of Matplotlib Learn Basics of Matplotlib 00:07:00 Learn Basics of Pandas - Part 1 Learn Basics of Pandas - Part 1 00:06:00 Learn Basics of Pandas - Part 2 Learn Basics of Pandas - Part 2 00:07:00 Understanding the CSV data file Understanding the CSV data file 00:09:00 Load and Read CSV data file using Python Standard Library Understanding the CSV data file 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using Pandas Load and Read CSV data file using Pandas 00:05:00 Dataset Summary - Peek, Dimensions and Data Types Dataset Summary - Peek, Dimensions and Data Types 00:09:00 Dataset Summary - Class Distribution and Data Summary Dataset Summary - Class Distribution and Data Summary 00:09:00 Dataset Summary - Explaining Correlation Dataset Summary - Explaining Correlation 00:11:00 Dataset Summary - Explaining Skewness - Gaussian and Normal Curve Dataset Summary - Explaining Skewness - Gaussian and Normal Curve 00:07:00 Dataset Visualization - Using Histograms Dataset Visualization - Using Histograms 00:07:00 Dataset Visualization - Using Density Plots Dataset Visualization - Using Density Plots 00:06:00 Dataset Visualization - Box and Whisker Plots Dataset Visualization - Box and Whisker Plots 00:05:00 Multivariate Dataset Visualization - Correlation Plots Multivariate Dataset Visualization - Correlation Plots 00:08:00 Multivariate Dataset Visualization - Scatter Plots Multivariate Dataset Visualization - Scatter Plots 00:05:00 Data Preparation (Pre-Processing) - Introduction Data Preparation (Pre-Processing) - Introduction 00:09:00 Data Preparation - Re-scaling Data - Part 1 Data Preparation - Re-scaling Data - Part 1 00:09:00 Data Preparation - Re-scaling Data - Part 2 Data Preparation - Re-scaling Data - Part 2 00:09:00 Data Preparation - Standardizing Data - Part 1 Data Preparation - Standardizing Data - Part 1 00:07:00 Data Preparation - Standardizing Data - Part 2 Data Preparation - Standardizing Data - Part 2 00:04:00 Data Preparation - Normalizing Data Data Preparation - Normalizing Data 00:08:00 Data Preparation - Binarizing Data Data Preparation - Binarizing Data 00:06:00 Feature Selection - Introduction Feature Selection - Introduction 00:07:00 Feature Selection - Uni-variate Part 1 - Chi-Squared Test Feature Selection - Uni-variate Part 1 - Chi-Squared Test 00:09:00 Feature Selection - Uni-variate Part 2 - Chi-Squared Test Feature Selection - Uni-variate Part 2 - Chi-Squared Test 00:10:00 Feature Selection - Recursive Feature Elimination Feature Selection - Recursive Feature Elimination 00:11:00 Feature Selection - Principal Component Analysis (PCA) Feature Selection - Principal Component Analysis (PCA) 00:09:00 Feature Selection - Feature Importance Feature Selection - Feature Importance 00:07:00 Refresher Session - The Mechanism of Re-sampling, Training and Testing Refresher Session - The Mechanism of Re-sampling, Training and Testing 00:12:00 Algorithm Evaluation Techniques - Introduction Algorithm Evaluation Techniques - Introduction 00:07:00 Algorithm Evaluation Techniques - Train and Test Set Algorithm Evaluation Techniques - Train and Test Set 00:11:00 Algorithm Evaluation Techniques - K-Fold Cross Validation Algorithm Evaluation Techniques - K-Fold Cross Validation 00:09:00 Algorithm Evaluation Techniques - Leave One Out Cross Validation Algorithm Evaluation Techniques - Leave One Out Cross Validation 00:05:00 Algorithm Evaluation Techniques - Repeated Random Test-Train Splits Algorithm Evaluation Techniques - Repeated Random Test-Train Splits 00:07:00 Algorithm Evaluation Metrics - Introduction Algorithm Evaluation Metrics - Introduction 00:09:00 Algorithm Evaluation Metrics - Classification Accuracy Algorithm Evaluation Metrics - Classification Accuracy 00:08:00 Algorithm Evaluation Metrics - Log Loss Algorithm Evaluation Metrics - Log Loss 00:03:00 Algorithm Evaluation Metrics - Area Under ROC Curve Algorithm Evaluation Metrics - Area Under ROC Curve 00:06:00 Algorithm Evaluation Metrics - Confusion Matrix Algorithm Evaluation Metrics - Confusion Matrix 00:10:00 Algorithm Evaluation Metrics - Classification Report Algorithm Evaluation Metrics - Classification Report 00:04:00 Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction 00:06:00 Algorithm Evaluation Metrics - Mean Absolute Error Algorithm Evaluation Metrics - Mean Absolute Error 00:07:00 Algorithm Evaluation Metrics - Mean Square Error Algorithm Evaluation Metrics - Mean Square Error 00:03:00 Algorithm Evaluation Metrics - R Squared Algorithm Evaluation Metrics - R Squared 00:04:00 Classification Algorithm Spot Check - Logistic Regression Classification Algorithm Spot Check - Logistic Regression 00:12:00 Classification Algorithm Spot Check - Linear Discriminant Analysis Classification Algorithm Spot Check - Linear Discriminant Analysis 00:04:00 Classification Algorithm Spot Check - K-Nearest Neighbors Classification Algorithm Spot Check - K-Nearest Neighbors 00:05:00 Classification Algorithm Spot Check - Naive Bayes Classification Algorithm Spot Check - Naive Bayes 00:04:00 Classification Algorithm Spot Check - CART Classification Algorithm Spot Check - CART 00:04:00 Classification Algorithm Spot Check - Support Vector Machines Classification Algorithm Spot Check - Support Vector Machines 00:05:00 Regression Algorithm Spot Check - Linear Regression Regression Algorithm Spot Check - Linear Regression 00:08:00 Regression Algorithm Spot Check - Ridge Regression Regression Algorithm Spot Check - Ridge Regression 00:03:00 Regression Algorithm Spot Check - Lasso Linear Regression Regression Algorithm Spot Check - Lasso Linear Regression 00:03:00 Regression Algorithm Spot Check - Elastic Net Regression Regression Algorithm Spot Check - Elastic Net Regression 00:02:00 Regression Algorithm Spot Check - K-Nearest Neighbors Regression Algorithm Spot Check - K-Nearest Neighbors 00:06:00 Regression Algorithm Spot Check - CART Regression Algorithm Spot Check - CART 00:04:00 Regression Algorithm Spot Check - Support Vector Machines (SVM) Regression Algorithm Spot Check - Support Vector Machines (SVM) 00:04:00 Compare Algorithms - Part 1 : Choosing the best Machine Learning Model Compare Algorithms - Part 1 : Choosing the best Machine Learning Model 00:09:00 Compare Algorithms - Part 2 : Choosing the best Machine Learning Model Compare Algorithms - Part 2 : Choosing the best Machine Learning Model 00:05:00 Pipelines : Data Preparation and Data Modelling Pipelines : Data Preparation and Data Modelling 00:11:00 Pipelines : Feature Selection and Data Modelling Pipelines : Feature Selection and Data Modelling 00:10:00 Performance Improvement: Ensembles - Voting Performance Improvement: Ensembles - Voting 00:07:00 Performance Improvement: Ensembles - Bagging Performance Improvement: Ensembles - Bagging 00:08:00 Performance Improvement: Ensembles - Boosting Performance Improvement: Ensembles - Boosting 00:05:00 Performance Improvement: Parameter Tuning using Grid Search Performance Improvement: Parameter Tuning using Grid Search 00:08:00 Performance Improvement: Parameter Tuning using Random Search Performance Improvement: Parameter Tuning using Random Search 00:06:00 Export, Save and Load Machine Learning Models : Pickle Export, Save and Load Machine Learning Models : Pickle 00:10:00 Export, Save and Load Machine Learning Models : Joblib Export, Save and Load Machine Learning Models : Joblib 00:06:00 Finalizing a Model - Introduction and Steps Finalizing a Model - Introduction and Steps 00:07:00 Finalizing a Classification Model - The Pima Indian Diabetes Dataset Finalizing a Classification Model - The Pima Indian Diabetes Dataset 00:07:00 Quick Session: Imbalanced Data Set - Issue Overview and Steps Quick Session: Imbalanced Data Set - Issue Overview and Steps 00:09:00 Iris Dataset : Finalizing Multi-Class Dataset Iris Dataset : Finalizing Multi-Class Dataset 00:09:00 Finalizing a Regression Model - The Boston Housing Price Dataset Finalizing a Regression Model - The Boston Housing Price Dataset 00:08:00 Real-time Predictions: Using the Pima Indian Diabetes Classification Model Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:07:00 Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:00 Real-time Predictions: Using the Boston Housing Regression Model Real-time Predictions: Using the Boston Housing Regression Model 00:08:00 Resources Resources - Python Machine Learning & Data Science Fundamentals 00:00:00
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