{"id":2159,"date":"2018-10-31T12:06:04","date_gmt":"2018-10-31T19:06:04","guid":{"rendered":"http:\/\/intuitblog.com\/?p=2159"},"modified":"2025-10-08T12:33:42","modified_gmt":"2025-10-08T19:33:42","slug":"tech-meets-philanthropy-using-deep-learning-to-predict-medical-emergency-recurrence","status":"publish","type":"post","link":"https:\/\/www.intuit.com\/blog\/social-responsibility\/tech-meets-philanthropy-using-deep-learning-to-predict-medical-emergency-recurrence\/","title":{"rendered":"Tech Meets Philanthropy: Using Deep Learning to Predict Medical Emergency Recurrence"},"content":{"rendered":"

Few things inspire gratitude as intensely as having someone save the life of your parent.<\/span><\/p>\n

So after her mother had life-changing surgery in Israel recently, Intuit\u2019s Sigalit Bechler was fueled with a desire to give back. Specifically, Bechler wanted to contribute to medical research at the facility that helped her mother, Sheba Medical Center \u2013 the biggest hospital in the Middle East.<\/span><\/p>\n

And so began a first-of-its-kind collaboration between Intuit and the hospital. Bechler, a data scientist in Israel, reached out to her innovation team and to tech-savvy doctors at the hospital. The doctors suggested several projects that would fill unmet needs at the hospital, and the innovation team decided to devote their \u201cWe Care and Give Back\u201d time to finding solutions.<\/span><\/p>\n

\u201cI felt fortunate to be part of a company that really enables such a massive contribution to the community,\u201d Bechler says. \u201cI also felt grateful to have the opportunity to be part of this group of great researchers. It was an extremely wonderful feeling to turn from being passive to being active, both regarding my mother\u2019s situation and helping other people.\u201d<\/span><\/p>\n

Tech meets philanthropy<\/h2>\n

The collaboration led to the development of three models centering on patient traffic through the extremely busy emergency room (ER). The models used machine learning (ML) and Natural Language Processing (NLP) to analyze the data of a half-million patients who visited the ER. The models focused on predicting the mortality of arriving patients, cataloging radiologists\u2019 interpretation of CAT scans, and \u2013 most importantly \u2013 predicting return trips back to the ER.<\/span><\/p>\n

Predicting the mortality of patients arriving at the ER<\/h2>\n

This is critical for prioritizing treatment in the ER (acute patients get priority). Currently, an ER nurse scores the acuteness of a patient\u2019s condition on a 5-point scale based on limited data. <\/span><\/p>\n

The Intuit team\u2019s model took far more data into account \u2013 including triglyceride level, demographic data and vital measures \u2013 to determine which patients were in a life-threatening condition. As a result, the Intuit team was able to predict who would pass away within two days of arriving in the ER with a much higher rate of accuracy than previously (0.8<\/span> AUC<\/span><\/a>, a measure of an algorithm\u2019s performance).<\/span><\/p>\n

Automatic cataloging of radiologists’ interpretation of CAT scans<\/h2>\n

Radiologists interpret CAT scans in natural language without specifying if there is an acute condition. This interpretation is sent to the patient\/treating doctor (the doctor may not be staff at the hospital where the imaging was done). The Intuit team used NLP algorithms to transform medical text in Hebrew into structured information that could be quantified as <\/span>acute<\/span><\/i>, <\/span>chronic <\/span><\/i>or <\/span>nothing wrong<\/span><\/i>. This was a challenge because available modules for NLP are usually for English tex.<\/span><\/p>\n

The team achieved 90 percent accuracy in their severity predictions \u2014 outperforming the 85 percent accuracy benchmark the doctors provided using data in English.<\/span><\/p>\n

Predicting patients returning to the ER<\/h2>\n

About 13 percent of patients return to the ER after discharge. Predicting who those patients will be matters because in some cases (e.g., if a patient returns within 30 days) the hospital will have to foot the bill.<\/span><\/p>\n

So after examining 170 different parameters registered during a patient\u2019s stay to predict outcomes for released patients (including re-hospitalization within 30 days), the Intuit team achieved an AUC accuracy score of 0.7.<\/span><\/p>\n

\u201cThere were a lot of challenges,\u201d says Bechler. \u201cThere are a lot of issues with the data \u2014 most of it is very confidential and sensitive. But we found a solution to that and it turned out to be amazing.\u201d<\/span><\/p>\n

Using deep learning to predict breast cancer recurrence<\/h2>\n

ER visit predictions aren\u2019t the only medical encounter deep tech can help improve. At the recent Grace Hopper Celebration, Intuit\u2019s Noah Eyal Altman unveiled her research showing how machine learning can improve breast cancer recurrence prediction.<\/span><\/p>\n

Chemotherapy does indeed reduce recurrence and mortality for early stage breast cancer; without it, almost half of the women over all breast cancer subtypes will recur. But it also brings with it a great cost to patients and caregivers alike \u2013 both economically and emotionally.<\/span><\/p>\n

Therefore, estimating recurrence risk is clinically important; it allows caregivers the opportunity to offer adjuvant therapy only to patients at high risk. This is done by assessing individual risk through measuring gene expression fro\u05e3m the primary tumor.<\/span><\/p>\n

For this study, the team identified a dataset of 1,519 women with early-stage breast cancer for which whole transcriptome analysis was done on the primary tumor and a long-term follow-up was available. They showed that coupling deep learning and random forest can achieve higher accuracies than ever reported in a large population (84.79%) and that this accuracy is higher than obtained with each algorithm alone.<\/span><\/p>\n

Long story short \u2013 deep learning can be used in biomedical research even with training sets of moderate size, and in a supervised setting, should be further explored as a dimensionality reduction method in biomedical research.<\/span><\/p>\n

A deeply satisfying experience<\/h2>\n

Thirteen Intuit\u00a0<\/span>data <\/span>scientists<\/span> in four teams took part in the Aug. 5 \u2013 7 hackathon. Those who participated said they found the experience fulfilling and satisfying, and were excited to be able to demonstrate Intuit\u2019s WCGB value. The partner doctors were amazed at how fast the team was able to achieve meaningful results, and said they plan to publish papers about them.<\/span><\/p>\n

\u201cEvery year the population is getting older so emergency rooms need to deal with more patients,\u201d says Dr. Eyal Zimlichman, deputy director of Sheba hospital. \u201cThat\u2019s why we need to find out-of-the-box solutions to help make the process and results better in scale. We wanted to provide the Intuit team with a GitHub equivalent for medical knowledge and resources. That\u2019s something that we tried to spark in this hackathon, and this is why this pilot is so important.\u201d<\/span><\/p>\n

\u201cI was surprised to see how the team members felt connected right from the start to the project and instantly asked to join it,\u201d says Shimon Shahar, a distinguished data scientist at Intuit Israel. \u201cMost of our efforts were dedicated to analyze and help triage patients in the emergency room. And although many companies have healthcare divisions, we hardly see this type of unique activity between tech companies and hospitals.\u201d<\/span><\/p>\n

As for Bechler, she says she hopes to make the hackathon an annual event. \u201cI hope that this will be a catalyst for other companies to get involved in philanthropic technical work,\u201d she says. \u201cToday more than ever, I believe that collaborations with a scientific interest can significantly advance medicine.\u201d<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Few things inspire gratitude as intensely as having someone save the life of your parent. So after her mother had life-changing surgery in Israel recently, Intuit\u2019s Sigalit Bechler was fueled with a desire to give back. Specifically, Bechler wanted to contribute to medical research at the facility that helped her mother, Sheba Medical Center \u2013<\/p>\n","protected":false},"author":151418277,"featured_media":7939,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"rkv_browse_by_id":0,"rkv_cta_id":0,"rkv_optimize_for_pagespeed":false,"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"useModifiedDate":false,"customPublishDate":"","_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[237496403,1367,237496409],"tags":[237495718,40978],"intuit_collection":[],"intuit_series":[],"coauthors":[237496056],"class_list":["post-2159","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-innovative-thinking","category-social-responsibility","category-tech-innovation","tag-deep-learning","tag-machine-learning"],"yoast_head":"\nTech Meets Philanthropy: Using Deep Learning to Predict Medical Emergency Recurrence - Intuit Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.intuit.com\/blog\/social-responsibility\/tech-meets-philanthropy-using-deep-learning-to-predict-medical-emergency-recurrence\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Tech Meets Philanthropy: Using Deep Learning to Predict Medical Emergency Recurrence - Intuit Blog\" \/>\n<meta property=\"og:description\" content=\"Few things inspire gratitude as intensely as having someone save the life of your parent. 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