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A Pilot Study of Biomedical Text Comprehension using an Attention-Based Deep Neural Reader: Design and Experimental Analysis

Machine comprehension tasks can be defined as testing the ability of a machine to answer a question based on context.

Seongsoon Kim, Donghyeon Park, Yonghwa Choi, Kyubum Lee, Byounggun Kim, Minji Jeon, Jihye Kim, Aik Choon Tan, Jaewoo Kang

JMIR Med Inform 2018;6(1):e2

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Consumer Health Search on the Web: Study of Web Page Understandability and Its Integration in Ranking Algorithms

work has systematically analyzed the influence of the components in this study—we show that these greatly influence retrieval effectiveness and, thus, delivery of relevant and understandable health advice.Related WorkUnderstandability refers to the ease of comprehension

Joao Palotti, Guido Zuccon, Allan Hanbury

J Med Internet Res 2019;21(1):e10986

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Assessing the Readability of Medical Documents: A Ranking Approach

To this end, we developed a machine learning model to compare the relative difficulty of documents using data collected from Amazon Mechanical Turk (AMT) users.

Jiaping Zheng, Hong Yu

JMIR Med Inform 2018;6(1):e17

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Patient Perception of Plain-Language Medical Notes Generated Using Artificial Intelligence Software: Pilot Mixed-Methods Study

This approach harnesses machine learning and natural language processing (NLP) to simplify clinician written notes into plain-language notes.

Sandeep Bala, Angela Keniston, Marisha Burden

JMIR Form Res 2020;4(6):e16670

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Impact of Game-Inspired Infographics on User Engagement and Information Processing in an eHealth Program

For people with low health literacy, the beneficial effect of visuals as an aid to comprehension is even more pronounced [17].

Maria Leonora G Comello, Xiaokun Qian, Allison M Deal, Kurt M Ribisl, Laura A Linnan, Deborah F Tate

J Med Internet Res 2016;18(9):e237

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Learning to Read by Learning to Write: Evaluation of a Serious Game to Foster Business Process Model Comprehension

game to investigate its impact on the comprehension of process models.

Michael Winter, Rüdiger Pryss, Thomas Probst, Manfred Reichert

JMIR Serious Games 2020;8(1):e15374

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Implementation Intention and Reminder Effects on Behavior Change in a Mobile Health System: A Predictive Cognitive Model

Peter Pirolli, Shiwali Mohan, Anusha Venkatakrishnan, Les Nelson, Michael Silva, Aaron Springer

J Med Internet Res 2017;19(11):e397

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Let Visuals Tell the Story: Medication Adherence in Patients with Type II Diabetes Captured by a Novel Ingestion Sensor Platform

Ultimately, we anticipate that machine-learning techniques will make predictive interventions possible to support longitudinal medication adherence and patient self-management.LimitationsThe major limitation of this work is the study size.

Sara H Browne, Yashar Behzadi, Gwen Littlewort

JMIR Mhealth Uhealth 2015;3(4):e108

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Improving Electronic Health Record Note Comprehension With NoteAid: Randomized Trial of Electronic Health Record Note Comprehension Interventions With Crowdsourced Workers

In this work, we use the Amazon Mechanical Turk (AMT) microtask crowdsourcing platform to give AMT workers (Turkers) the ComprehENotes EHR note comprehension test [20], a set of questions designed to test EHR note comprehension.

John P Lalor, Beverly Woolf, Hong Yu

J Med Internet Res 2019;21(1):e10793

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Privacy Policies for Apps Targeted Toward Youth: Descriptive Analysis of Readability

Use of terms such as “cookies” and “third-party site” may contribute to comprehension difficulties, as well as complex phrases that use other jargon not in common parlance.

Gitanjali Das, Cynthia Cheung, Camille Nebeker, Matthew Bietz, Cinnamon Bloss

JMIR Mhealth Uhealth 2018;6(1):e3

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