list_of_works

Published work

Journal Articles

2022
Lam, K. H., Twose, J., Lissenberg-Witte, B., Licitra, G., Meijer, K., Uitdehaag, B., De Groot, V., Killestein, J. (2022). The use of smartphone keystroke dynamics to passively monitor upper limb and cognitive function in multiple sclerosis: a longitudinal analysis (In Press)
- https://www.jmir.org/author/submission/37614
Lam, K. H., Twose, J., McConchie, H., Licitra, G., Meijer, K., de Ruiter, L., … & Killestein, J. (2022). Smartphone‐derived keystroke dynamics are sensitive to relevant changes in multiple sclerosis. European journal of neurology, 29(2), 522-534.
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/ene.15162
2021
Lam, K. H., Meijer, K. A., Loonstra, F. C., Coerver, E. M. E., Twose, J., Redeman, E., … & Killestein, J. (2021). Real-world keystroke dynamics are a potentially valid biomarker for clinical disability in multiple sclerosis. Multiple Sclerosis Journal, 27(9), 1421-1431.
- https://journals.sagepub.com/doi/pdf/10.1177/1352458520968797
2020
Twose, J., Licitra, G., McConchie, H., Lam, K. H., & Killestein, J. (2020). Early-warning signals for disease activity in patients diagnosed with multiple sclerosis based on keystroke dynamics. Chaos (Woodbury, NY), 30(11), 113133.
- https://aip.scitation.org/doi/abs/10.1063/5.0022031
- https://jameshtwose.github.io/EWS-supplemental-material/

Posters/ Abstracts

2021
Lam, K., Twose, J., Licitra, G., Meijer, K., Uitdehaag, B., De Groot, V., Killestein, J. (2021). Passively acquired smartphone keystroke dynamics are associated with clinical outcomes in multiple sclerosis: a longitudinal analysis (P302). ECTRIMS 2021 - ePoster
- https://journals.sagepub.com/doi/full/10.1177/13524585211044667
2020
Lam, K., Twose, J., McConchie, H., Licitra, G., Moraal, B., Barkhof, F., Uitdehaag, B., De Groot, V., Killestein, J. (2020). Smartphone keystroke dynamics are sensitive to changes in disease activity and clinical disability measures in multiple sclerosis. ECTRIMS 2020
- https://cslide.ctimeetingtech.com/msdc2020/attendee/person/1681

Projects/ Analyses at Neurocast

Innovation Options

Using Linear Mixed Models to predict outcomes → look for changes using sum of dynamic complexity and cumulative complexity peaks (Twose et al, 2020)
Recurrence Network Transitivity
- Using multiple Recurrence Networks over a sliding window → from each network calculate the transitivity coefficient (Marwan & Kurths, 2015) → compare the peaks in this coefficient with changes in outcome measure
Network approaches
- ML-VAR and uSEM networks (Boorsboom et al., 2021, Yang et al., 2018)
Optimal control theory
- Coupled nonlinear difference equations to model outcomes (e.g. Schöller et al., 2018)
Subtype and Stage Inference (SuStaIn)
- Using SuStaIn to identify MS subtypes in keystroke data (Young et al., 2018, Aksman et al., 2021)

Applications/ packages

Created a sample size calculation web application called NeuroShiny using R Shiny and deployed it using github actions, docker and AWS ECR, ECS, fargate
Created a python package to help aid all internal analyses called neuropy. This included unit tests (pytest), Continuous Integration and Continuous Delivery (CICD) via github actions and was fully documented (sphinx) with complimentary scripts to show the intended use of all of the functions.
Created a private pypi server to host internal python packages

Projects

Amsterdam University medical center
- longitudinal analysis using Linear Mixed Models to predict MS related outcome with keystroke composite scores (Lam et al., 2022)
- responsiveness analysis using receiver operating characteristic (ROC) analysis (Lam et al., 2022)
- cross sectional analysis using correlations, t-tests, intra class correlations and bland-altman plots (Lam et al., 2021)
- nonlinear time series analysis using dynamic complexity (Twose et al., 2020)
- hierarchical machine learning analyses to predict MS outcomes (e.g. Hajjem, Bellavance and Larocque, 2014)
Fatigue questionnaire validation using confirmatory factor analysis
Writing a github wiki to explain data science methodology at Neurocast

Personal Projects

2022
Landing page of analyses for the Citizen Shield project (Python, Jupyter, HTML, CSS)
- https://github.com/Citizen-Shield/multi-method-protective-behaviour
- https://citizen-shield.github.io/multi-method-protective-behaviour/
A fully functioning frontend for a food tracking application (React, Javascript, HTML, CSS)
- https://github.com/jameshtwose/jmstracker-frontend
- https://trckr.jms.rocks
A fully functioning API backend for a food tracking application (Python - FastAPI, deta, Postgresql)
- https://github.com/jameshtwose/jmstracker-backend
- https://4qcow4.deta.dev/docs
A website showing latest data science work and portfolio (HTML, CSS, Javascript)
- https://github.com/jameshtwose/services.jms.rocks
- https://services.jms.rocks
A general Data Science repository (Python, R, Jupyter, HTML)
- https://github.com/jameshtwose/Data-Science
- https://jameshtwose.github.io/Data-Science/
A fully functioning chatbot (Pytorch, Flask, Microsoft Azure)
- https://github.com/jameshtwose/dumb_friend
Stand alone health app (Python - FastAPI, deta, nosql)
- https://github.com/jameshtwose/health_deta
- https://jameshtwose.github.io/health_deta/
- https://2q6e55.deta.dev/docs
Learning jQuery (Javascript)
- https://github.com/jameshtwose/learning_jquery
- https://jameshtwose.github.io/learning_jquery/
Vitarenal - suggested analyses (Python)
- https://github.com/jameshtwose/vitarenal-DS
- https://jameshtwose.github.io/vitarenal-DS/
Simple Surf Conditions Dashboard (HTML, CSS)
- https://github.com/jameshtwose/surf-dashboard
- https://jameshtwose.github.io/surf-dashboard/
Example Deliverables Documentation (Python, Github Actions)
- https://github.com/jameshtwose/example_deliverables
- https://jameshtwose.github.io/example_deliverables/
Todos App (Python - FastAPI, PostgreSQL)
- https://github.com/jameshtwose/todos-app
- https://tp03k7.deta.dev
Sample Size Calculation Dashboard - jmspwr (R - Shiny, Docker, Github Actions)
- https://github.com/jameshtwose/jmspwr
- https://itsamejms.shinyapps.io/jmspwr/
Personal Python Package - jmspack (Python)
With unit tests, CICD, documentation showing examples and docstrings
- https://github.com/jameshtwose/jmspack
- Documentation: https://docs.jms.rocks
Price Forecasting of Raw Materials (Python)
- https://github.com/jameshtwose/mike_jms_collabs
- https://jameshtwose.github.io/mike_jms_collabs/
Mobility Dashboard using Apple data (Python - dash, plotly)
- https://github.com/jameshtwose/apple-mobility-dash
Generative music, and audio analysis (Python - librosa, music21)
- https://github.com/jameshtwose/musiplay
Sudoku Solver App (Javascript)
- https://github.com/jameshtwose/sudoku_solver
Vanilla Documentation (Python, HTML, Github Actions)
- https://github.com/jameshtwose/vanilla_documentation
- https://jameshtwose.github.io/vanilla_documentation/
Learning Sphinx Documentation (Python, Github Actions)
- https://github.com/jameshtwose/learning_sphinx_documentation
- https://jameshtwose.github.io/learning_sphinx_documentation/
Learning R Shiny with Docker and Github Actions Deploy (R, Docker)
- https://github.com/jameshtwose/r-shiny-github-actions-deploy
- https://itsamejms.shinyapps.io/mtcars-example/
Early Warning Signals Journal Article Supplemental Material (HTML, CSS)
- https://github.com/jameshtwose/EWS-supplemental-material
2021
Workout Chooser App (Javascript)
- https://github.com/jameshtwose/workout_app
- https://workout-chooser-app.herokuapp.com
Simple word picker (Javascript)
- https://github.com/jameshtwose/word_picker_js
Learning Continuous Integration/ Continuous Development (CICD) (Python)
- https://github.com/jameshtwose/learning_CICD
- https://jameshtwose.github.io/learning_CICD/
COVID dashboard (Python - dash, plotly)
- https://github.com/jameshtwose/covid-dash-jms
- https://covid-dash-jms.herokuapp.com
Personal fitbit dashboard (Python - dash, plotly)
- https://github.com/jameshtwose/fitbit-dash-jms
Personal fitbit analyses (Python)
- https://github.com/jameshtwose/jms_fitbit_analyses/tree/eda
2020
JmsTracker (Attempt 1) (Python)
- https://github.com/jameshtwose/jms_tracker