Learning to learn implicit queries from gaze patterns

Puolamäki, Kai; Ajanki, Antti; Kaski, Samuel

doi:10.1145/1390156.1390252

Cited by 13 publications

(6 citation statements)

References 19 publications

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“…However, important information regarding the severity of the patient's presentation remains restricted to free text. Table 2 Free-text and coded data missing from the feature set Data in free-text form Chronic anemia (27) Cured cancer as a chronic diagnosis (6)* y Cirrhosis (2) Advanced dementia/bedridden (12)* y Non-acute presentation (5)y Chronic infection (2) Known chronic kidney disease (11)* Active cancer (4) History of bleeding (2) Rectal exam (11) Active bleeding (3) Myelodysplastic syndrome (1) Previous endoscopy (10) History of cancer (3) Metrorharrgia (1) Gastrointestinal disease (7) Coomb's test/blood bank (2)z Recent procedure (1) Heart disease (7)y Anticoagulation (2) Coded data fields Diagnosis of general deterioration Diagnosis of hematuria Urine M spike Diagnosis of G6PD deficiency Treatment with carbamezapine* Numbers in parentheses denote the number of cases data were noted in. *Free-text data associated with a change in recommended action (observed only for endoscopy and bone marrow examinations).…”

Section: Discussionmentioning

confidence: 99%

Collaborative knowledge acquisition for the design of context-aware alert systems

Joffe

Havakuk

Herskovic

et al. 2012

J Am Med Inform Assoc

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Objective To present a framework for combining implicit knowledge acquisition from multiple experts with machine learning and to evaluate this framework in the context of anemia alerts. Materials and Methods Five internal medicine residents reviewed 18 anemia alerts, while 'talking aloud'. They identified features that were reviewed by two or more physicians to determine appropriate alert level, etiology and treatment recommendation. Based on these features, data were extracted from 100 randomlyselected anemia cases for a training set and an additional 82 cases for a test set. Two staff internists assigned an alert level, etiology and treatment recommendation before and after reviewing the entire electronic medical record. The training set of 118 cases (100 plus 18) and the test set of 82 cases were explored using RIDOR and JRip algorithms. Results The feature set was sufficient to assess 93% of anemia cases (intraclass correlation for alert level before and after review of the records by internists 1 and 2 were 0.92 and 0.95, respectively). High-precision classifiers were constructed to identify low-level alerts (precision p¼0.87, recall R¼0.4), iron deficiency (p¼1.0, R¼0.73), and anemia associated with kidney disease (p¼0.87, R¼0.77). Discussion It was possible to identify low-level alerts and several conditions commonly associated with chronic anemia. This approach may reduce the number of clinically unimportant alerts. The study was limited to anemia alerts. Furthermore, clinicians were aware of the study hypotheses potentially biasing their evaluation. Conclusion Implicit knowledge acquisition, collaborative filtering and machine learning were combined automatically to induce clinically meaningful and precise decision rules. BACKGROUND AND SIGNIFICANCE

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Section: Discussionmentioning

confidence: 99%

Collaborative knowledge acquisition for the design of context-aware alert systems

Joffe

Havakuk

Herskovic

et al. 2012

J Am Med Inform Assoc

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“…In this work, we investigate other modalities of evidence, which can be used to determine the factuality of a claim. Specifically, we consider whether data from eye-tracking can be used to infer the factuality of a claim, as eyetracking has previously been used in information retrieval to infer relevance [1,14,15,46,59,70]. Additionally, eye-tracking has been used to investigate how users engage with news content, where it has been observed that users tend to read false news faster [26], as well as putting more visual attention on credible news posts [78].…”

Section: Chapter 6: Factuality Checking In News Headlines With Eye Trackingmentioning

confidence: 99%

Shifting Consumption towards Diverse Content on Music Streaming Platforms

Hansen

Mehrotra²,

Hansen

et al. 2021

Proceedings of the 14th ACM International Conference on Web Search and Data Mining

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First and foremost, I am grateful to my three supervisors, Christina, Jakob, and Stephen, who have provided me with many lessons on both academia and science as a whole, as well as personal lessons I will keep forever. My weekly meetings with Christina and Jakob have always been a source of joy, as they were both scientifically enlightening but also simply entertaining. I would also like to thank my fellow PhD students, with whom I have worked during the years, Lucas, Dongsheng, Stephan, and Niklas.During my PhD, I have worked together with great people at Edulab to improve how mathematics is taught in Denmark. This has been an excellent collaboration where real-world problems could meet academia with exciting results. Of all the people at Edulab, I would especially like to thank Lotte, Kasper, Morten, Jakob, Stani, and Daniel.I was fortunate to spend a summer away from Denmark as an intern at Spotify Research in London. I got to work with many wonderful people, where I would especially like to thank Rishabh, Brian, Federico, Lucas, and Mounia.I also want to thank Rastin and Benjamin for a great collaboration at Danmarks Nationalbank. I realized how lucky I am that papers in computer science are so much shorter than in economics.From my personal life, I would first like to thank all my friends and family who have put up with my wacky work schedule during the last few years. Especially I would like to thank Mikkel, Helena, and David, who have been a great support. Lastly, I would like to thank the two most important people during my PhD, my twin brother Casper and my fiancée Spring. Casper has been a great source of inspiration in our work together and has been the best collaborator anyone could ever hope to work with. Spring has been a constant source of support and love, who not only accepts that I like to talk about my work, but also perfectly feigns interest in it.

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“…These features are then used for statistical inferences, classification, and prediction. For instance, some variants of aggregated fixation-count and fixation-duration were used in studies reported in [14,15,19,21,39,41,59,63]. Eye-dwell time and/or visit time was used by Fahey et al [14].…”

Section: Related Work 21 Information Relevance and Eye-trackingmentioning

confidence: 99%

Relevance Prediction from Eye-movements Using Semi-interpretable Convolutional Neural Networks

Bhattacharya

Rakshit

Gwizdka

et al. 2020

Proceedings of the 2020 Conference on Human Information Interaction and Retrieval

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We propose an image-classification method to predict the perceivedrelevance of text documents from eye-movements. An eye-tracking study was conducted where participants read short news articles, and rated them as relevant or irrelevant for answering a trigger question. We encode participants' eye-movement scanpaths as images, and then train a convolutional neural network classifier using these scanpath images. The trained classifier is used to predict participants' perceived-relevance of news articles from the corresponding scanpath images. This method is content-independent, as the classifier does not require knowledge of the screen-content, or the user's information-task. Even with little data, the image classifier can predict perceived-relevance with up to 80% accuracy. When compared to similar eye-tracking studies from the literature, this scanpath image classification method outperforms previously reported metrics by appreciable margins. We also attempt to interpret how the image classifier differentiates between scanpaths on relevant and irrelevant documents.

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Learning to learn implicit queries from gaze patterns

Cited by 13 publications

References 19 publications

Collaborative knowledge acquisition for the design of context-aware alert systems

Collaborative knowledge acquisition for the design of context-aware alert systems

Shifting Consumption towards Diverse Content on Music Streaming Platforms

Relevance Prediction from Eye-movements Using Semi-interpretable Convolutional Neural Networks

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