AI for Medical Prognosis (Coursera)

AI for Medical Prognosis (Coursera)

Añade tu reseña
Añadir a Mis FavoritosAñadido a tus favoritosEliminado de tus favoritos 0
Añadir para comparar

Descripción de “AI for Medical Prognosis (Coursera)”

AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine.

Machine learning is a powerful tool for prognosis, a branch of medicine that specializes in predicting the future health of patients. In this second course, you’ll walk through multiple examples of prognostic tasks. You’ll then use decision trees to model non-linear relationships, which are commonly observed in medical data, and apply them to predicting mortality rates more accurately. Finally, you’ll learn how to handle missing data, a key real-world challenge.

These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. This course focuses on tree-based machine learning, so a foundation in deep learning is not required for this course. However, a foundation in deep learning is highly recommended for course 1 and 3 of this specialization. You can gain a foundation in deep learning by taking the Deep Learning Specialization offered by deeplearning.ai and taught by Andrew Ng.

Course 2 of 3 in the AI for Medicine Specialization.

WHAT YOU WILL LEARN

– Walk through examples of prognostic tasks

– Apply tree-based models to estimate patient survival rates

– Navigate practical challenges in medicine like missing data

Syllabus

WEEK 1

Linear prognostic models

Build a linear prognostic model using logistic regression, then evaluate the model by calculating the concordance index. Finally, improve the model by adding feature interactions.

WEEK 2

Prognosis with Tree-based models

Tune decision tree and random forest models to predict the risk of a disease. Evaluate the model performance using the c-index. Identify missing data and how it may alter the data distribution, then use imputation to fill in missing data, in order to improve model performance.

WEEK 3

Survival Models and Time

This week, you will work with data where the time that a disease occurs is a variable. Instead of predicting just the 10-year risk of a disease, you will build more flexible models that can predict the 5 year, 7 year, or 10 year risk.

WEEK 4

Build a risk model using linear and tree-based models

This week, you will fit a linear model, and a tree-based risk model on survival data, to customize a risk score for each patient, based on their health profile. The risk score represents the patient’s relative risk of getting a particular disease. You will then evaluate each model’s performance by implementing and using a concordance index that incorporates time to event and censored data.

Especificaciones: AI for Medical Prognosis (Coursera)

Curso ofrecido por
Disponibilidad

✔ Disponible

Plataforma

Universidad

Impartido por

Bora Uyumazturk Eddy Shyu Pranav Rajpurkar

País

USA

Nivel, duración y fechas
Nivel

Intermedio

Fecha

04/05/2020

Duración

4 semanas

Tiempo necesario

7-8 horas/semana

Idioma del curso
Idioma vehicular

Inglés

Subtítulos

Inglés

Exámenes y Certificados
Certificados

Certificado de Pago

Exámenes/Proyectos

Con Examen/Proyecto Final de pago

User Reviews

0.0 fuera de 5
0
0
0
0
0
Write a review

Aún no hay reseñas.

Se el primero en opinar sobre “AI for Medical Prognosis (Coursera)”

Tu dirección de correo electrónico no será publicada. Los campos obligatorios están marcados con *

Antes de enviar tu opinión, has de aceptar nuestra política de cookies y privacidad

AI for Medical Prognosis (Coursera)
AI for Medical Prognosis (Coursera)

Este sitio web utiliza cookies para un correcto funcionamiento. Si continúas navegando estás dando tu consentimiento para estas cookies y aceptas nuestra política de cookies, clic para más información.

ACEPTAR
Aviso de cookies
Comparar artículos
  • Total (0)
Comparar
0