# Ideation Challenge: Data

In July 2017, Reactor issued a data ideation challenge: “Good Questions Meet Big Data.” Applicants were asked to identify a human health problem that might be resolved using big data and a computational solution. Entries were required to be problems that could be addressed with clinical and translational research in areas such as diagnostics, therapeutics, public health, technology, or outcomes.

## Feverprints: Crowdsourcing temperatures in health and disease

#### Winner: Jonathan Hausmann

Our study seeks to leverage modern technology, including continuous temperature monitoring and crowdsourcing, to collect temperatures from thousands of participants throughout the country to reassess what is “normal” and what temperature constitutes a “fever.” Second, we will use machine learning to discover unique fever patterns (“feverprints”) for febrile illnesses that can be used for rapid and accurate diagnosis. Finally, we aim to show whether the use of antipyretics improves or worsens the course of febrile illnesses.

## Incorporating the Patient’s Voice into Cancer Care and Research

#### Winner: Charlotta Lindvall

Electronic Health Records (EHR) contain enormous amounts of data that may be used to facilitate cancer treatment discovery, guide quality and safety initiatives, and enhance patient satisfaction. Currently, data from clinical visits (i.e., the conversation between clinician and patient) are interpreted and compressed by clinicians who write it directly into the medical record. This process misses much that transpires in the clinical encounter, risks error, and results in lost opportunities to deeply understand how our care impacts outcomes. We propose to use advanced computer science methods to embed audio-recordings of clinical encounters into the EHR, employ machine-learning analytics to harness this rich data source, ensure that what actually transpires between patients and clinicians in the exam room is included in outcome analyses, and to thus transform clinical care by capturing, measuring, and accounting for the full breadth of patient experience.

## Automatic classification of clear cell renal tumors using deep learning: implications for diagnosis and prediction of metastasis

#### Winner: Jan Heng

The abstract for this prize has not been published at the request of the award winner.

## Computational approaches to assessing breast asymmetry for early detection of breast cancer

#### Winner: Rulla Tamimi

Wide-spread mammographic screening as well as improvements in breast cancer treatment have greatly improved 5-year survival rates for breast cancer. However, the sensitivity of mammography is still not ideal and is lower in younger women and women with dense breasts. It may be possible to use big data and computational solutions to improve the sensitivity of mammographic screening and improve early detection of breast cancer. We and others have observed that the patterns of breast density are very similar in comparing the right and left breast from the same woman. However, when women are diagnosed with breast cancer it is primarily in only one breast. We hypothesize that there are early divergences in breast density patterns between the breasts that are detectable through imaging that could be early predictors of breast cancer. If there were automated ways to compare asymmetry in breast tissue patterns (i.e., spatial variation in breast density and features) between breasts over time, we may be able to detect breast cancers even earlier. Additionally, comparing breasts may help to control for differences that influence patterns at different time points such as technical issues (e.g., compression, mammographic machine, positioning on machine), and individual differences over time (e.g., changes in weight, hormone use, menopausal status).

## Mental Health Related Visits in Pediatric Emergency Departments Over Time: Differences in age groups, socioeconomic status, and race/ethnicity

#### Winner: Anna Abrams

The abstract for this prize has not been published at the request of the award winner.