Detecting Devastating Diseases in Search Logs

Paparrizos, John; White, Ryen W.; Horvitz, Eric

doi:10.1145/2939672.2939722

Cited by 15 publications

(10 citation statements)

References 46 publications

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“…Including the nonexperiential pancreatic adenocarcinoma searchers may add noise to model training. 38 We acknowledge that this study has several limitations. Per log anonymity, we lack explicit ground truth about diagnoses and rely on implicit self-reporting in queries.…”

Section: Discussionmentioning

confidence: 95%

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Screening for Pancreatic Adenocarcinoma Using Signals From Web Search Logs: Feasibility Study and Results

Paparrizos

White

Horvitz

2016

JOP

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Section: Discussionmentioning

confidence: 95%

“…Regularization methods were used to minimize the risk of overfitting. See Paparrizos et al 38 for details on the construction of the classifier. We used the statistical classifier to study our ability to perform early identification of searchers who would later make experiential diagnostic queries for pancreatic adenocarcinoma.…”

Section: Early Detectionmentioning

confidence: 99%

Screening for Pancreatic Adenocarcinoma Using Signals From Web Search Logs: Feasibility Study and Results

Paparrizos

White

Horvitz

2016

JOP

Self Cite

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“…Previous research efforts also lead to semantic representation program SemRep (Rindflesh and Fiszman, 2003), which exploits biomedical domain knowledge and linguistic analysis of biomedical text. Other unconventional resource such as web query logs are also utilized (Paparrizos et al, 2016) to provide early warnings about the presence of devastating diseases.…”

Section: Related Workmentioning

confidence: 99%

An Insight Extraction System on BioMedical Literature with Deep Neural Networks

Ganjam

Jain

et al. 2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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Mining biomedical text offers an opportunity to automatically discover important facts and infer associations among them. As new scientific findings appear across a large collection of biomedical publications, our aim is to tap into this literature to automate biomedical knowledge extraction and identify important insights from them. Towards that goal, we develop a system with novel deep neural networks to extract insights on biomedical literature. Evaluation shows our system is able to provide insights with competitive accuracy of human acceptance and its relation extraction component outperforms previous work.

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“…Prior work has highlighted opportunities to utilize large-scale anonymized search logs to detect signals associated with the emergence and progression of medical illnesses [ 22 ]. For example, search activity, including content and patterns of use, has been used to identify individuals with lung cancer, Parkinson disease, and pancreatic cancer with high degrees of accuracy up to a year in advance of the diagnosis [ 23 - 25 ].…”

Section: Introductionmentioning

confidence: 99%

Utilizing Machine Learning on Internet Search Activity to Support the Diagnostic Process and Relapse Detection in Young Individuals With Early Psychosis: Feasibility Study

Birnbaum¹,

Kulkarni²,

Meter³

et al. 2020

JMIR Ment Health

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Background Psychiatry is nearly entirely reliant on patient self-reporting, and there are few objective and reliable tests or sources of collateral information available to help diagnostic and assessment procedures. Technology offers opportunities to collect objective digital data to complement patient experience and facilitate more informed treatment decisions. Objective We aimed to develop computational algorithms based on internet search activity designed to support diagnostic procedures and relapse identification in individuals with schizophrenia spectrum disorders. Methods We extracted 32,733 time-stamped search queries across 42 participants with schizophrenia spectrum disorders and 74 healthy volunteers between the ages of 15 and 35 (mean 24.4 years, 44.0% male), and built machine-learning diagnostic and relapse classifiers utilizing the timing, frequency, and content of online search activity. Results Classifiers predicted a diagnosis of schizophrenia spectrum disorders with an area under the curve value of 0.74 and predicted a psychotic relapse in individuals with schizophrenia spectrum disorders with an area under the curve of 0.71. Compared with healthy participants, those with schizophrenia spectrum disorders made fewer searches and their searches consisted of fewer words. Prior to a relapse hospitalization, participants with schizophrenia spectrum disorders were more likely to use words related to hearing, perception, and anger, and were less likely to use words related to health. Conclusions Online search activity holds promise for gathering objective and easily accessed indicators of psychiatric symptoms. Utilizing search activity as collateral behavioral health information would represent a major advancement in efforts to capitalize on objective digital data to improve mental health monitoring.

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Detecting Devastating Diseases in Search Logs

Cited by 15 publications

References 46 publications

Screening for Pancreatic Adenocarcinoma Using Signals From Web Search Logs: Feasibility Study and Results

Screening for Pancreatic Adenocarcinoma Using Signals From Web Search Logs: Feasibility Study and Results

An Insight Extraction System on BioMedical Literature with Deep Neural Networks

Utilizing Machine Learning on Internet Search Activity to Support the Diagnostic Process and Relapse Detection in Young Individuals With Early Psychosis: Feasibility Study

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