Janie Baxter
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View article: Enabling scalable clinical interpretation of machine learning (ML)-based phenotypes using real world data
Enabling scalable clinical interpretation of machine learning (ML)-based phenotypes using real world data Open
Background: Large and deep electronic health record (EHR) datasets have the potential to increase understanding of real-world patient journeys, and to identify subgroups of patients currently grouped with a common disease label but differi…
View article: Enabling scalable clinical interpretation of ML-based phenotypes using real world data
Enabling scalable clinical interpretation of ML-based phenotypes using real world data Open
The availability of large and deep electronic healthcare records (EHR) datasets has the potential to enable a better understanding of real-world patient journeys, and to identify novel subgroups of patients. ML-based aggregation of EHR dat…
View article: Compensating trajectory bias for unsupervised patient stratification using adversarial recurrent neural networks
Compensating trajectory bias for unsupervised patient stratification using adversarial recurrent neural networks Open
Electronic healthcare records are an important source of information which can be used in patient stratification to discover novel disease phenotypes. However, they can be challenging to work with as data is often sparse and irregularly sa…
View article: Deep Semi-Supervised Embedded Clustering (DSEC) for Stratification of Heart Failure Patients
Deep Semi-Supervised Embedded Clustering (DSEC) for Stratification of Heart Failure Patients Open
Determining phenotypes of diseases can have considerable benefits for in-hospital patient care and to drug development. The structure of high dimensional data sets such as electronic health records are often represented through an embeddin…
View article: Deep Semi-Supervised Embedded Clustering (DSEC) for Stratification of\n Heart Failure Patients
Deep Semi-Supervised Embedded Clustering (DSEC) for Stratification of\n Heart Failure Patients Open
Determining phenotypes of diseases can have considerable benefits for\nin-hospital patient care and to drug development. The structure of high\ndimensional data sets such as electronic health records are often represented\nthrough an embed…
View article: A Comparison of Artificial Intelligence and Human Doctors for the Purpose of Triage and Diagnosis
A Comparison of Artificial Intelligence and Human Doctors for the Purpose of Triage and Diagnosis Open
AI virtual assistants have significant potential to alleviate the pressure on overly burdened healthcare systems by enabling patients to self-assess their symptoms and to seek further care when appropriate. For these systems to make a mean…
View article: Associations of comorbidities and medications with COVID-19 outcome: A retrospective analysis of real-world evidence data
Associations of comorbidities and medications with COVID-19 outcome: A retrospective analysis of real-world evidence data Open
Background Hundreds of thousands of deaths have already been recorded for patients with the severe acute respiratory syndrome coronavirus (SARS-CoV-2; aka COVID-19). Understanding whether there is a relationship between comorbidities and C…
View article: A comparative study of artificial intelligence and human doctors for the purpose of triage and diagnosis
A comparative study of artificial intelligence and human doctors for the purpose of triage and diagnosis Open
Online symptom checkers have significant potential to improve patient care, however their reliability and accuracy remain variable. We hypothesised that an artificial intelligence (AI) powered triage and diagnostic system would compare fav…
View article: A comparative study of artificial intelligence and human doctors for the\n purpose of triage and diagnosis
A comparative study of artificial intelligence and human doctors for the\n purpose of triage and diagnosis Open
Online symptom checkers have significant potential to improve patient care,\nhowever their reliability and accuracy remain variable. We hypothesised that an\nartificial intelligence (AI) powered triage and diagnostic system would compare\n…