- Deep Learning
- Machine Learning
- Explainable Machine Learning
- processing electronic health records
- clinical decision support systems
- International Classification of Diseases
- mining clinical databases
- Descriptive Statistics
- Electronic Health Records
- Ethics in EHR
- preprocessing of EHR and imputation
- Convolutional Neural Network
Informed Clinical Decision Making using Deep Learning専門講座
Apply Deep Learning in Electronic Health Records. Understand the road path from data mining of clinical databases to clinical decision support systems
提供：
Informed Clinical Decision Making using Deep Learning専門講座University of Glasgow 学習内容
Extract and preprocess data from complex clinical databases
Apply deep learning in Electronic Health Records
Imputation of Electronic Health Records and data encodings
Explainable, fair and privacy-preserved Clinical Decision Support Systems
習得するスキル
この専門講座について
This specialisation is for learners with experience in programming that are interested in expanding their skills in applying deep learning in Electronic Health Records and with a focus on how to translate their models into Clinical Decision Support Systems.
The main areas that would explore are:
Data mining of Clinical Databases: Ethics, MIMIC III database, International Classification of Disease System and definition of common clinical outcomes. Deep learning in Electronic Health Records: From descriptive analytics to predictive analytics Explainable deep learning models for healthcare applications: What it is and why it is needed Clinical Decision Support Systems: Generalisation, bias, ‘fairness’, clinical usefulness and privacy of artificial intelligence algorithms.
応用学習プロジェクト
Learners have the opportunity to choose and undertake an exercise based on MIMIC-III extracted datasets that combines knowledge from:
- Data mining of Clinical Databases to query the MIMIC database
- Deep learning in Electronic Health Records to pre-process EHR and build deep learning models
- Explainable deep learning models for healthcare to explain the models decision
Learners can choose from:
1. Permutation feature importance on the MIMIC critical care database
The technique is applied both on logistic regression and on an LSTM model. The explanations derived are global explanations of the model.
2. LIME on the MIMIC critical care database
The technique is applied on both logistic regression and an LSTM model. The explanations derived are local explanations of the model.
3. Grad-CAM on the MIMIC critical care database
GradCam is implemented and applied on an LSTM model that predicts mortality. The explanations derived are local explanations of the model.
共有できる証明書
修了時に証明書を取得
100%オンラインコース
自分のスケジュールですぐに学習を始めてください。
フレキシブルなスケジュール
柔軟性のある期限の設定および維持
中級レベル
Last year undergraduate or master students of computing science or engineering. Basic knowledge on SQL queries and python is required.
約5ヶ月で完了
5時間/週の推奨ペース
英語
字幕：英語
共有できる証明書
修了時に証明書を取得
100%オンラインコース
自分のスケジュールですぐに学習を始めてください。
フレキシブルなスケジュール
柔軟性のある期限の設定および維持
中級レベル
Last year undergraduate or master students of computing science or engineering. Basic knowledge on SQL queries and python is required.
約5ヶ月で完了
5時間/週の推奨ペース
英語
字幕：英語
専門講座の仕組み
コースを受講しましょう。
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実践型プロジェクト
すべての専門講座には、実践型プロジェクトが含まれています。専門講座を完了して修了証を獲得するには、成功裏にプロジェクトを終了させる必要があります。専門講座に実践型プロジェクトに関する別のコースが含まれている場合、専門講座を開始するには、それら他のコースをそれぞれ終了させる必要があります。
修了証を取得
すべてのコースを終了し、実践型プロジェクトを完了すると、修了証を獲得します。この修了証は、今後採用企業やあなたの職業ネットワークと共有できます。
この専門講座には5コースあります。
Data mining of Clinical Databases - CDSS 1
This course will introduce MIMIC-III, which is the largest publicly Electronic Health Record (EHR) database available to benchmark machine learning algorithms. In particular, you will learn about the design of this relational database, what tools are available to query, extract and visualise descriptive analytics.
Deep learning in Electronic Health Records - CDSS 2
Overview of the main principles of Deep Learning along with common architectures. Formulate the problem for time-series classification and apply it to vital signals such as ECG. Applying this methods in Electronic Health Records is challenging due to the missing values and the heterogeneity in EHR, which include both continuous, ordinal and categorical variables. Subsequently, explore imputation techniques and different encoding strategies to address these issues. Apply these approaches to formulate clinical prediction benchmarks derived from information available in MIMIC-III database.
Explainable deep learning models for healthcare - CDSS 3
This course will introduce the concepts of interpretability and explainability in machine learning applications. The learner will understand the difference between global, local, model-agnostic and model-specific explanations. State-of-the-art explainability methods such as Permutation Feature Importance (PFI), Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanation (SHAP) are explained and applied in time-series classification. Subsequently, model-specific explanations such as Class-Activation Mapping (CAM) and Gradient-Weighted CAM are explained and implemented. The learners will understand axiomatic attributions and why they are important. Finally, attention mechanisms are going to be incorporated after Recurrent Layers and the attention weights will be visualised to produce local explanations of the model.
Clinical Decision Support Systems - CDSS 4
Machine learning systems used in Clinical Decision Support Systems (CDSS) require further external validation, calibration analysis, assessment of bias and fairness. In this course, the main concepts of machine learning evaluation adopted in CDSS will be explained. Furthermore, decision curve analysis along with human-centred CDSS that need to be explainable will be discussed. Finally, privacy concerns of deep learning models and potential adversarial attacks will be presented along with the vision for a new generation of explainable and privacy-preserved CDSS.
提供：
University of Glasgow
The University of Glasgow has been changing the world since 1451. It is a world top 100 university (THE, QS) with one of the largest research bases in the UK.
よくある質問
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1つのコースだけに登録することは可能ですか？
学資援助はありますか？
無料でコースを受講できますか？
このコースは100%オンラインで提供されますか？実際に出席する必要のあるクラスはありますか？
専門講座を修了するのにどのくらいの期間かかりますか？
What background knowledge is necessary?
Do I need to take the courses in a specific order?
専門講座を修了することで大学の単位は付与されますか？
What will I be able to do upon completing the Specialization?
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