AI Enabled Drug Design and Side Effect Prediction Powered by Multi-Objective Evolutionary Algorithms & Transformer Models

Due to the large search space and conflicting objectives, drug design and discovery is a difficult problem for which new machine learning (ML) approaches are required. Here, the problem is to invent a method by which new, therapeutically useful, compounds can be discovered; and to simultaneously avoid compounds which will fail clinical trials or pass unwanted effects onto the end patient. By extending current technologies as well as adding new ones, more design criteria can be included, and more promising novel drugs can be discovered. This work advances the field of computational drug design by (1) developing MOEA-DT, a non-deep learning application for multi-objective molecular optimization, which generates new molecules with high performance in a variety of design criteria; and (2) developing SEMTL-BERT, a side effect prediction algorithm which leverages the latest ML techniques and datasets to accomplish its task. Experiments performed show that MOEA-DT either matches or outperforms other similar methods, and that SEMTL-BERT can enhance predictive ability

A CNN-LSTM for predicting mortality in the ICU

An accurate predicted mortality is crucial to healthcare as it provides an empirical risk estimate for prognostic decision making, patient stratification and hospital benchmarking. Current prediction methods in practice are severity of disease scoring systems that usually involve a fixed set of admission attributes and summarized physiological data. These systems are prone to bias and require substantial manual effort which necessitates an updated approach which can account for most shortcomings. Clinical observation notes allow for recording highly subjective data on the patient that can possibly facilitate higher discrimination. Moreover, deep learning models can automatically extract and select features without human input.This thesis investigates the potential of a combination of a deep learning model and notes for predicting mortality with a higher accuracy. A custom architecture, called CNN-LSTM, is conceptualized for mapping multiple notes compiled in a hospital stay to a mortality outcome. It employs both convolutional and recurrent layers with the former capturing semantic relationships in individual notes independently and the latter capturing temporal relationships between concurrent notes in a hospital stay. This approach is compared to three severity of disease scoring systems with a case study on the MIMIC-III dataset. Experiments are set up to assess the CNN-LSTM for predicting mortality using only the notes from the first 24, 12 and 48 hours of a patient stay. The model is trained using K-fold cross-validation with k=5 and the mortality probability calculated by the three severity scores on the held-out set is used as the baseline. It is found that the CNN-LSTM outperforms the baseline on all experiments which serves as a proof-of-concept of how notes and deep learning can better outcome prediction

A clinician’s guide to understanding and critically appraising machine learning studies: a checklist for Ruling Out Bias Using Standard Tools in Machine Learning (ROBUST-ML)

Developing functional machine learning (ML)-based models to address unmet clinical needs requires unique considerations for optimal clinical utility. Recent debates about the rigours, transparency, explainability, and reproducibility of ML models, terms which are defined in this article, have raised concerns about their clinical utility and suitability for integration in current evidence-based practice paradigms. This featured article focuses on increasing the literacy of ML among clinicians by providing them with the knowledge and tools needed to understand and critically appraise clinical studies focused on ML. A checklist is provided for evaluating the rigour and reproducibility of the four ML building blocks: data curation, feature engineering, model development, and clinical deployment. Checklists like this are important for quality assurance and to ensure that ML studies are rigourously and confidently reviewed by clinicians and are guided by domain knowledge of the setting in which the findings will be applied. Bridging the gap between clinicians, healthcare scientists, and ML engineers can address many shortcomings and pitfalls of ML-based solutions and their potential deployment at the bedside

The Convergence of Human and Artificial Intelligence on Clinical Care - Part I

This edited book contains twelve studies, large and pilots, in five main categories: (i) adaptive imputation to increase the density of clinical data for improving downstream modeling; (ii) machine-learning-empowered diagnosis models; (iii) machine learning models for outcome prediction; (iv) innovative use of AI to improve our understanding of the public view; and (v) understanding of the attitude of providers in trusting insights from AI for complex cases. This collection is an excellent example of how technology can add value in healthcare settings and hints at some of the pressing challenges in the field. Artificial intelligence is gradually becoming a go-to technology in clinical care; therefore, it is important to work collaboratively and to shift from performance-driven outcomes to risk-sensitive model optimization, improved transparency, and better patient representation, to ensure more equitable healthcare for all