1. Select known barriers to telemedicine. (select all that apply) A. It studies how genes can...

1. Select known barriers to telemedicine. (select all that apply)

	A.	It studies how genes can be manipulated for better drug effect
	B.	It studies genetic information looking for new targets for drugs
	C.	It studies how genetic information determines drug allergies
	D.	It studies how drugs affect genetic material

2.What tests/algorithms are shared between statistics and machine learning?

	A.	Bayes, decision trees, neural networks
	B.	Linear regression, logistic regression, neural networks
	C.	Bayes, linear regression, logistic regression
	D.	Linear regression, logistic regression, decision trees

Expert Solution

1. Telehealth is the distribution of health-related services and information via electronic information and telecommunication technologies. It allows long-distance patient and clinician contact, care, advice, reminders, education, intervention, monitoring, and remote admissions.
The known barriers to telemedicine are :
Limited reimbursement

Lack of standards

Interstate licensure laws

2. C) Bayes , linear regression, logistic regression

Bayes theorem: in probability and statistics it describes the probability of an event , based on prior knowledge of conditions that might be related to the event.

Bayes Theorem is a useful tool in applied machine learning. It provides a way of thinking about the relationship between data and a model. A machine learning algorithm or model is a specific way of thinking about the structured relationships in the data.

Linear regression: In statistics it is a linear approach to modelling the relationship between a scalar response and one or more explanatory variables.

It is a supervised machine learning algorithm where the predicted output is continuous and has a constant slope . It's used to predict value within a continuous range rather than trying to classify them into categories.

Logistic regression: is a statistical model that in its basic form uses logistic function to model a binary dependent variable.

In machine learning it's a supervised learning classification algorithm used to predict the probability of a target variable.

Nightingale answered 1 month ago

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