Machine Learning (ML) & Artificial Intelligence (AI): From Black Box to White Box Models in 4 Steps - Resources for Explainable AI & ML Model Interpretability.

✔️STEP 1 - ARTICLES

- (short) KDnuggets article: https://lnkd.in/eRyTXcQ

- (long) O'Reilly article: https://lnkd.in/ehMHYsr

✔️STEP 2 - BOOKS

- Interpretable Machine Learning: A Guide for Making Black Box Models Explainable (free e-book): https://lnkd.in/eUWfa5y

- An Introduction to Machine Learning Interpretability: An Applied Perspective on Fairness, Accountability, Transparency, and Explainable AI (free e-book): https://lnkd.in/dJm595N

✔️STEP 3 - COLLABORATE

- Join Explainable AI (XAI) Group: https://lnkd.in/dQjmhZQ

✔️STEP 4 - PRACTICE

- Hands-On Practice: Open-Source Tools & Tutorials for ML Interpretability (Python/R): https://lnkd.in/d5bXgV7

- Python Jupyter Notebooks: https://lnkd.in/dETegUH

#machinelearning #datascience #analytics #bigdata #statistics #artificialintelligence #ai #datamining #deeplearning #neuralnetworks #interpretability #science #research #technology #business #healthcare

✴️ @AI_Python_EN

Fundamentals of Clinical Data Science (Open-Access Book) - for healthcare & IT professionals: https://lnkd.in/eacNnjz
#
For more interesting & helpful content on healthcare & data science, follow me and Brainformatika on LinkedIn.

Table of Contents

Part I. Data Collection

- Data Sources
- Data at Scale
- Standards in Healthcare Data
- Research Data Stewardship for Healthcare Professionals
- The EU’s General Data Protection Regulation (GDPR) in a Research Context

Part II. From Data to Model

- Preparing Data for Predictive Modelling
- Extracting Features from Time Series
- Prediction Modeling Methodology
- Diving Deeper into Models
- Reporting Standards & Critical Appraisal of Prediction Models

Part III. From Model to Application

- Clinical Decision Support Systems
- Mobile Apps
- Optimizing Care Processes with Operational Excellence & Process Mining
- Value-Based Health Care Supported by Data Science

#healthcare #datascience #digitalhealth #analytics #machinelearning #bigdata #populationhealth #ai #medicine #informatics #artificialintelligence #research #precisionmedicine #publichealth #science #health #innovation #technology #informationtechnology

✴️ @AI_Python_EN

A Pattern-Based Method for Medical Entity Recognition From Chinese Diagnostic Imaging Text

The identification of medical entities and relations from electronic medical records is a fundamental research issue for medical informatics. However, the task of extracting valuable knowledge from these records is challenging due to its high complexity.

The method proves to be stable and robust with different amounts of testing data. It achieves a comparatively high performance in the CHIP 2018 open challenge, demonstrating its effectiveness in extracting tumor-related entities from Chinese diagnostic imaging text.

Paper: https://lnkd.in/g3uezmR
#LSTM #RNN #deeplearning #healthcare #algorithm

✴️ @AI_Python_EN

End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography

Researchers from #GoogleAi and #Stanford published work today in #Nature that shows great potential to use machine learning to help catch more lung cancer cases earlier and increase survival likelihood.

Link: https://lnkd.in/fUMtA-3

#LungCancer #Cancer #biolearning #healthcare #DL

✴️ @AI_Python_EN

Another lovely development in #Healthcare #DeepLearning

Building a Benchmark Dataset and Classifiers for Sentence-Level Findings in AP Chest X-rays.

#datasets
Arxiv: https://lnkd.in/dxx5iCY

✴️ @AI_Python_EN

Really awesome #deeplearning #rnn paper 👏🏻 explaining an increase in predicted risk for clinical alerts.

Explanations in a temporal setting where a stateful dynamical model produces a sequence of risk estimates given an input at each time step.

The goal here is to alert a clinician when a patient’s risk of deterioration rises. The clinician then has to decide whether to intervene and adjust the treatment.

Given a potentially long sequence of new events since she last saw the patient, a concise explanation helps her to quickly triage the alert.

Authors developed methods to lift static attribution techniques to the dynamical setting, where they identified and addressed challenges specific to dynamics.

They then experimentally assess the utility of different explanations of clinical alerts through expert evaluation.

Here’s full paper: https://lnkd.in/duMQYyW
#healthcare #diagnostics #clinical
#prediction
✴️ @AI_Python_EN

Predicting survival from colorectal cancer histology slides using #deeplearning

1. They conducted the study because:
• Colorectal cancer (CRC) is a common disease with a variable clinical course, and there is a high clinical need to more accurately predict the outcome of individual patients.
• For almost every CRC patient, histological slides of tumor tissue are routinely available.
• Deep learning can be used to extract information from very complex images, and we hypothesized that deep learning can predict clinical outcome directly from histological images of CRC.

2. What did the researchers do and find?
• We trained a deep neural network to identify different tissue types & demonstrated that it can decompose complex tissue into its constituent parts and thereby showed that this score improves survival prediction compared to the SOTA avaiable.

3. Conclusion
• Deep learning is an inexpensive tool to predict the clinical course of CRC patients based on ubiquitously available histological images.
• Prospective validation studies are needed to firmly establish this biomarker for routine clinical use.

☞ Link to research

#healthcare #AI #machinelearning

❇️ @AI_Python_EN