Datadriven sjukvård

4 hp

Målet med kursen är att studenten ska lära sig koncept och metoder för att adressera sjukvårdsproblem med hjälp av datadrivna metoder, och öva i att analysera realistiska data med prediktiva analytiska metoder. Kursen ingår i programmet MAISTR (hh.se/maistr) där du som deltagare kan läsa hela programmet eller enstaka kurser. Kursen är för yrkesverksamma och ges på distans på engelska. Anmälan är öppen så länge det finns möjlighet att bli antagen.

In English: The course is part of the programme MAISTR (hh.se/maistr) where participants can take the entire programme or individual courses. The course is for professionals and is held online in English. Application is open as long as there is a possibility of admission. The courses qualify for credits and are free of charge for participants who are citizens of any EU or EEA country, or Switzerland, or are permanent residents in Sweden. More information can be found at antagning.se.

About the course Datadriven Healthcare, 4 credits
This course aims to provide a broad introduction to health care analytics: Applying data analytics tools and techniques to organize and analyze healthcare data.

The course is broken down into four parts:

1. Healthcare data understanding and ethics. This part discusses general issues related to the collection, sharing, and management of healthcare data, as well as issues related to patients’ privacy, ethics, bias, social and economic constraints when using healthcare data.

2. Data preparation and visualization. This part will discuss challenges related to healthcare data such as the data size and the class imbalance problem. Then, it introduces techniques for preprocessing healthcare data, extracting and selecting the most relevant features, and visualizing the data.

3. Classification techniques in healthcare data. This part will discuss predictive modeling techniques such as classification using decision trees, neural networks, and others. These techniques will be applied to various practical health care problems, such as: readmission risk assessment, personalization of treatment regimen, predicting patient survival rates, etc.

4. Evaluation metrics in predictive analytics. This part will present commonly used metrics to evaluate the predicted outcomes, but also introduce evaluation strategies relevant in the healthcare domain such as: AB Testing, Propensity Scores, and Randomized Control Trials.

VT 2022 (Distans (Internet), Ortsoberoende, 33%)

Nivå:

Avancerad nivå

Anmälningskod:

23805

Behörighetskrav:

Kandidatexamen i datateknik eller Högskoleingenjörsexamen i datateknik samt Programming 7,5 hp och Maskininlärning 7,5 hp eller motsvarande. Engelska 6.

Urvalsregler:

Högskolepoäng: 100%

Mer information om urvalsregler.

Startvecka:

Vecka: 03

Obligatoriska sammankomster:

0

Undervisningstid:

Blandade tider

Studieavgift:

För sökande med medborgarskap utanför EU/EES och Schweiz: Mer information om studieavgift

Undervisningsspråk:

Undervisningen bedrivs på engelska.

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