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Paper published in Clinical Pharmacology and Therapeutics

Ioana BicaAhmed AlaaMihaela van der SchaarNick Maxfield
May 26, 2020
2 min read

A paper co-authored by Ioana Bica, Ahmed Alaa, Craig Lambert and Mihaela van der Schaar has been published in Clinical Pharmacology & Therapeutics (editor-in-chief: Piet H. van der Graaf, PhD, PharmD).

Title: “From real‐world patient data to individualized treatment effects using machine learning: Current and future methods to address underlying challenges”

Date accepted: May 24, 2020

Clinical decision making needs to be supported by evidence that treatments are beneficial to individual patients. While randomized control trials are the gold standard for testing and introducing new drugs, due to the focus on specific questions with respect to establishing efficacy and safety versus standard treatment, they do not provide a full characterization of the heterogeneity in the final intended treatment population. Conversely, real‐world observational data, such as electronic health records contain large amounts of clinical information about heterogeneous patients and their response to treatments. In this paper, we introduce the main opportunities and challenges in using observational data for training machine learning methods to estimate individualized treatment effects and make treatment recommendations. We describe the modelling choices of the state‐of‐the‐art machine learning methods for causal inference, developed for estimating treatment effects both in the cross‐section and longitudinal settings. Additionally, we highlight future research directions that could lead to achieving the full potential of leveraging electronic health records and machine learning for making individualized treatment recommendations. We also discuss how experimental data from randomized control trials and Pharmacometric and Quantitative Systems Pharmacology approaches can be used to not only improve machine learning methods, but also provide ways for validating them. These future research directions will require us to collaborate across the scientific disciplines to incorporate models based on randomized control trials and known disease processes, physiology and pharmacology into these machine learning models based on electronic health records to fully optimize the opportunity these data present.

For a full list of the van der Schaar Lab’s publications, click here.

Ioana Bica

Ioana Bica is a second year PhD student at the University of Oxford and at the Alan Turing Institute. She has previously completed a BA and MPhil in Computer Science at the University of Cambridge where she has specialised in machine learning and its applications to biomedicine.

Ioana’s PhD research focuses on building machine learning methods for causal inference and individualised treatment effect estimation from observational data. In particular, she has developed methods capable of estimating the heterogeneous effects of time-dependent treatments, thus enabling us to determine when to give treatments to patients and how to select among multiple treatments over time.

Recently, Ioana has started working on methods for understanding and modelling clinical decision making through causality, inverse reinforcement learning and imitation learning.

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Ahmed Alaa

Ahmed M. Alaa is a Postdoctoral Scholar at the ECE Department, University of California, Los Angeles (UCLA), and an affiliated Postdoctoral Researcher at the Department of Applied Mathematics and Theoretical Physics, University of Cambridge.

His primary research focus has been on causal inference, automated machine learning, uncertainty quantification and time-series analysis.

He has published papers in several leading machine learning conferences including NeurIPS, ICML, ICLR and AISTATS.

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Mihaela van der Schaar

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Fellow at The Alan Turing Institute in London.

Mihaela has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.

In 2019, she was identified by National Endowment for Science, Technology and the Arts as the most-cited female AI researcher in the UK. She was also elected as a 2019 “Star in Computer Networking and Communications” by N²Women. Her research expertise span signal and image processing, communication networks, network science, multimedia, game theory, distributed systems, machine learning and AI.

Mihaela’s research focus is on machine learning, AI and operations research for healthcare and medicine.

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Nick Maxfield

From 2020 to 2022, Nick oversaw the van der Schaar Lab’s communications, including media relations, content creation, and maintenance of the lab’s online presence.

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