rwilliams

Ross Williams, PhD

Assistant Professor 

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Background

Ross obtained his MSc (2017) in Data Science from King's College London having previously obtained his BSc in Physics and Philosophy from the same institution. His thesis focused on predicting the change in diastolic blood pressure level for patients being treated for hypertension based upon demographics and pharmacological intervention, a particular focus of this was on external validation of the developed models.

His current focus is on methodological research into external validation of prediction models. Including but not limited too assessment metrics, transportability and recalibration. Accordingly he has contributed code to the PatientLevelPrediction R package along these lines. Further lines of interest include the creation of a prediction model library, implementation of Association Rule mining, temporal data analysis and methods for dealing with class imbalance. 

Publications

Using Iterative Pairwise External Validation to Contextualize Prediction Model Performance: A Use Case Predicting 1-Year Heart Failure Risk in Patients with Diabetes Across Five Data Sources

Drug Safety
2022-05 | Journal article
DOI: 10.1007/s40264-022-01161-8
CONTRIBUTORS: Ross D. Williams; Jenna M. Reps; Jan A. Kors; Patrick B. Ryan; Ewout Steyerberg; Katia M. Verhamme; Peter R. Rijnbeek
 

Use of unstructured text in prognostic clinical prediction models: a systematic review

Journal of the American Medical Informatics Association
2022-04-27 | Journal article
DOI: 10.1093/jamia/ocac058
CONTRIBUTORS: Tom M Seinen; Egill A Fridgeirsson; Solomon Ioannou; Daniel Jeannetot; Luis H John; Jan A Kors; Aniek F Markus; Victor Pera; Alexandros Rekkas; Ross D Williams et al.

Trends in the conduct and reporting of clinical prediction model development and validation: a systematic reviewJournal of the American Medical Informatics Association

2022-04-13 | Journal article
DOI: 10.1093/jamia/ocac002
CONTRIBUTORS: Cynthia Yang; Jan A Kors; Solomon Ioannou; Luis H John; Aniek F Markus; Alexandros Rekkas; Maria A J de Ridder; Tom M Seinen; Ross D Williams; Peter R Rijnbeek

Sarcopenia, systemic immune-inflammation index and all-cause mortality in middle-aged and older people with COPD and asthma: a population-based study 

ERJ Open Research
2022-01 | Journal article
DOI: 10.1183/23120541.00628-2021
CONTRIBUTORS: Elizabeth Benz; Sara R.A. Wijnant; Katerina Trajanoska; Johnmary T. Arinze; Emmely W. de Roos; Maria de Ridder; Ross Williams; Frank van Rooij; Katia M.C. Verhamme; M. Arfan Ikram et al.

Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network

BMC medical research methodology
2022-01 | Journal article
DOI: 10.1186/s12874-022-01505-z
PMID: 35094685
PMC: PMC8801189
CONTRIBUTORS: Williams RD; Markus AF; Yang C; Duarte-Salles T; DuVall SL; Falconer T; Jonnagaddala J; Kim C; Rho Y; Williams AE et al.
 
 
90-Day all-cause mortality can be predicted following a total knee replacement: an international, network study to develop and validate a prediction model
 
Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
2021-12 | Journal article
DOI: 10.1007/s00167-021-06799-y
PMID: 34870731
CONTRIBUTORS: Williams RD; Reps JM; OHDSI/EHDEN Knee Arthroplasty Group; Rijnbeek PR; Ryan PB; Prieto-Alhambra D

Trends in the conduct and reporting of clinical prediction model development and validation: a systematic review

2021-10-25 | Other
DOI: 10.1101/2021.10.22.21265374
CONTRIBUTORS: Cynthia Yang; Jan A. Kors; Solomon Ioannou; Luis H. John; Aniek F. Markus; Alexandros Rekkas; Maria A.J. de Ridder; Tom M. Seinen; Ross D. Williams; Peter R. Rijnbeek
 
Prediction of sustained biologic and targeted synthetic DMARD-free remission in rheumatoid arthritis patients
Rheumatology Advances in Practice
2021-08-31 | Journal article
DOI: 10.1093/rap/rkab087
CONTRIBUTORS: Theresa Burkard; Ross D Williams; Enriqueta Vallejo-Yagüe; Thomas Hügle; Axel Finckh; Diego Kyburz; Andrea M Burden

Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study

JMIR Medical Informatics

2021-04-05 | journal-article

DOI: 10.2196/21547

 

Sarcopenia in older people with chronic airway diseases: the Rotterdam study

ERJ Open Research

2021-01-15 | journal-article

DOI: 10.1183/23120541.00522-2020

 

Renin-angiotensin system blockers and susceptibility to COVID-19: an international, open science, cohort analysis.

The Lancet. Digital health

2020-12 | journal-article

PMID: 33342753PMC: PMC7834915DOI: 10.1016/s2589-7500(20)30289-2

  

Renin-angiotensin system blockers and susceptibility to COVID-19: a multinational open science cohort study2020-06 | preprint

OTHER-ID: PPR174750DOI: 10.1101/2020.06.11.20125849

 

Feasibility and evaluation of a large-scale external validation approach for patient-level prediction in an international data network: validation of models predicting stroke in female patients newly diagnosed with atrial fibrillation.

BMC medical research methodology

2020-05 | journal-article

PMID: 32375693PMC: PMC7201646DOI: 10.1186/s12874-020-00991-3

 

Development and validation of a prognostic model predicting symptomatic hemorrhagic transformation in acute ischemic stroke at scale in the OHDSI network.

PloS one

2020-01 | journal-article

PMID: 31910437PMC: PMC6946584DOI: 10.1371/journal.pone.0226718

 

Sarcopenia in COPD: a systematic review and meta-analysis.

European respiratory review : an official journal of the European Respiratory Society

2019-11-13 | journal-article

PMID: 31722892DOI: 10.1183/16000617.0049-2019

 

Opioid use, postoperative complications, and implant survival after unicompartmental versus total knee replacement: a population-based network study

The Lancet Rheumatology

2019 | journal-article

 

Teaching

  • Assists Dr. Rijnbeek in the teaching of data science to students of the “Klinische Technology” Master of Science program. This course aims to provide students the fundamentals of machine learning in a medical context. The course includes practical exercises that focus on the development of clinical prediction models. Ideally, this course is further extended to a full curriculum on health data science to teach all medical students at the Erasmus MC the basics of this exciting multidisciplinary field.
  • Teaches on the Patient-Level Prediction tutorial day. A day organised around the OHDSI Symposiums and at other times during the year which teaches students the why, what and how of running a clinical prediction model using the OHDSI tools.

 

Invited Lectures

  • Prediction Modelling Autumn School, King's College London, November 2017.

  • Population Health Management course Advanced Risk Stratification, Leiden UMC, May 2018.

  • Young Researcher in the Spotlight, HealthySciencesDay, ErasmusMC, April 2019.

 

Conference Presentations

  • "The Prediction Model Library, OHDSI Symposium 2021
  • "Predicting adverse events following total knee replacement", ISPE 2019, Philadelphia, August 2019
  • "Predicting Heart Failure in PAtients newly initialising treatment for type 2 diabetes", OHDSI Symposium 2018, October 2018.

 

Awards

2019 Titan Award for Clinical Application, OHDSI Symposium, Bethesda, September 2019

2021 Titan Award for Community Support, OHDSI Symposium, September 2021