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Aleksandar Savkov

5 accepted papers

2022

Consultation Checklists: Standardising the Human Evaluation of Medical Note Generation

Aleksandar Savkov, Francesco Moramarco, Alex Papadopoulos Korfiatis, Mark Perera, Anya Belz, Ehud Reiter
EMNLP 2022industry

Evaluating automatically generated text is generally hard due to the inherently subjective nature of many aspects of the output quality. This difficulty is compounded in automatic consultation note generation by differing opinions between medical experts both about which patient statements should be…

Cited by 10Source ↗PDF ↗Scholar ↗
2022

Human Evaluation and Correlation with Automatic Metrics in Consultation Note Generation

Francesco Moramarco, Alex Papadopoulos Korfiatis, Mark Perera, Damir Juric, Jack Flann, Ehud Reiter +2
ACL 2022long

In recent years, machine learning models have rapidly become better at generating clinical consultation notes; yet, there is little work on how to properly evaluate the generated consultation notes to understand the impact they may have on both the clinician using them and the patient’s clinical saf…

Cited by 54Source ↗PDF ↗code ↗Scholar ↗
2022

PriMock57: A Dataset Of Primary Care Mock Consultations

Alex Papadopoulos Korfiatis, Francesco Moramarco, Radmila Sarac, Aleksandar Savkov
ACL 2022short

Recent advances in Automatic Speech Recognition (ASR) have made it possible to reliably produce automatic transcripts of clinician-patient conversations. However, access to clinical datasets is heavily restricted due to patient privacy, thus slowing down normal research practices. We detail the deve…

Cited by 29Source ↗PDF ↗code ↗Scholar ↗
2022

User-Driven Research of Medical Note Generation Software

Tom Knoll, Francesco Moramarco, Alex Papadopoulos Korfiatis, Rachel Young, Claudia Ruffini, Mark Perera +4
NAACL 2022long

A growing body of work uses Natural Language Processing (NLP) methods to automatically generate medical notes from audio recordings of doctor-patient consultations. However, there are very few studies on how such systems could be used in clinical practice, how clinicians would adjust to using them,…

Cited by 22Source ↗PDF ↗Scholar ↗
2019

Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors

Vitalii Zhelezniak, Aleksandar Savkov, April Shen, Francesco Moramarco, Jack Flann, Nils Y. Hammerla
ICLR 2019poster

Recent literature suggests that averaged word vectors followed by simple post-processing outperform many deep learning methods on semantic textual similarity tasks. Furthermore, when averaged word vectors are trained supervised on large corpora of paraphrases, they achieve state-of-the-art results o…

Cited by 60Source ↗PDF ↗code ↗Scholar ↗