Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer RB Parikh et al, JAMA Network Open, October 25, 2019

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Last Posted: Oct 26, 2019

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• Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer
  RB Parikh et al, JAMA Network Open, October 25, 2019
• Trends and Focus of Machine Learning Applications for Health Research
  BB Jones et al, JAMA Network Open, October 25, 2019
• Machine Learning for Suicide Research–Can It Improve Risk Factor Identification?
  S Fazel et al, JAMA Psychiatry, October 23, 2019
• KETOS: Clinical decision support and machine learning as a service - A training and deployment platform based on Docker, OMOP-CDM, and FHIR Web Services.
  Gruendner Julian et al. PloS one 2019 (10) e0223010
• A comparative study of machine learning classifiers for risk prediction of asthma disease.
  Ullah Rahat et al. Photodiagnosis and photodynamic therapy 2019 Oct
• A Resource-Optimized VLSI Implementation of a Patient-Specific Seizure Detection Algorithm on a Custom-Made [Formula: see text] Wireless Device for Ambulatory Epilepsy Diagnostics.
  Zhan Tianyu et al. IEEE transactions on biomedical circuits and systems 2019 Oct
• Deep Learning: A Review for the Radiation Oncologist.
  Boldrini Luca et al. Frontiers in oncology 2019 9977
• Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone.
  Kim Heejung et al. JMIR mHealth and uHealth 2019 Oct 7(10) e14149
• Detecting the impact of subject characteristics on machine learning-based diagnostic applications.
  Chaibub Neto Elias et al. NPJ digital medicine 2019 299
• Developing a FHIR-based EHR phenotyping framework: A case study for identification of patients with obesity and multiple comorbidities from discharge summaries.
  Hong Na et al. Journal of biomedical informatics 2019 Oct 99103310

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