Use unsupervised machine learning approaches to extract insights from emergency department narratives about how, when, and why older adults (age 65+) fall.
Quick Facts
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artvolgin
1st PlaceFinding more efficient and accurate ways to extract insights from unstructured medical data helps public health researchers better understand the circumstances behind older adult falls. This helps drive communication, policy, and further research initiatives to reduce morbidity and mortality.
— Royal Law, Data Science Team Lead, CDC's National Center for Injury Prevention and Control
Why
Falls are the leading cause of injury-related death among adults 65 and older. Preventing unintentional older adult falls is a priority for the Centers for Disease Control and Prevention's National Center for Injury Prevention and Control (CDC/NCIPC), and they do this in part by researching the factors associated with the occurrence and severity of older adult falls.
An important source of information about older adult falls and other injuries is the National Electronic Injury Surveillance System (NEISS), which produces public, standardized data about injuries that present to the emergency departments of a sample of hospitals in the United States. NEISS data include a narrative field that describes the patients, their injuries, and their treatment. However, extracting insights about older adult falls and other injuries from such medical narratives typically involves manual coding procedures that are resource-intensive and difficult to scale.
The Solution
Machine learning techniques for natural language processing (NLP) have the potential to greatly expand the CDC/NCIPC’s capacity for analyzing medical record narratives and extracting insights that can inform fall prevention strategies. The goal in this challenge was to identify effective methods of using unsupervised machine learning and NLP to extract insights about older adult falls from NEISS emergency department narratives.
Solvers submitted solutions in the format of an analysis notebook (in R or Python) and a 1-3 page executive summary that highlighted their key findings, summarized their approach, and included select visualizations from their analyses. Solutions were evaluated by expert judges who considered how novel, well-communicated, rigorous, and insightful they were.
The Results
Across 45 submissions, participants explored a wide range of unsupervised NLP methodologies, including large language models (LLMs), clustering, dimensionality reduction, topic modeling, semantic search, named entity recognition, and more. In addition to the overall prizes, bonus prizes were awarded for the most novel approach, most compelling insight, best visualization, best null result, and most helpful community code post.
See the results announcement for more information on the winning approaches and the teams who developed them. All of the prize-winning notebooks and executive summaries from this competition are linked below and available for anyone to use and learn from.
On this page
Unsupervised Wisdom: Explore Medical Narratives on Older Adult Falls: Rules and Terms of Data Use
DrivenData Competition Rules
GUIDELINES
One account per participant
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Private sharing of code
Privately sharing code or data outside of teams is not permitted.
Winner License Type: Open Source License
Winning solutions need to be made available under The MIT License (an open source software license commonly described at http://opensource.org/licenses/MIT) in order to be eligible for recognition and prize money if offered.
Documentation for winning solutions
Winning solutions must be documented using the Winning Model Documentation Template provided to top-ranking participants to be eligible for recognition and prize money if offered.
Submission limits
The number of submissions per day is restricted to a fixed value on a per-competition basis. Any attempt to circumvent this limit will result in disqualification.
Use of test data
Unless otherwise specified on the website, each test data sample should be processed independently during inference without the use of information from other cases in the test set. By default, this precludes using information gathered across multiple test samples as feature inputs or target labels for model training, for instance through pseudo labeling or unsupervised learning on the test set. As a result, running model training code with the same training data but a different set of test data or no test data should produce the same model weights and fitted feature parameters. Eligible solutions must be able to run inference on new test data automatically, without retraining the model.
External data
External data is permitted as long as it is freely and publicly available to all Participants under a permissive open source license (e.g., CC0 or CC-BY) and complies with any external data restrictions stated on the Competition Website.
Questions?
The challenge rules are in place to promote fair competition and useful solutions. If you are ever unsure whether your solution meets the competition rules, ask the challenge organizers in the competition forum or send an email to info@drivendata.org.
FULL RULES
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Data use and code sharing
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Limitations of liability
By participating in the Competition, each Participant agrees that: (1) any and all disputes, claims, and causes of action arising out of or in connection with the Competition, or any prize awarded, shall be resolved individually without resort to any form of class action; (2) any claims, judgments and awards shall be limited to actual out-of-pocket costs incurred, including costs associated with entering the Competition, but in no event attorney's fees; and (3) under no circumstances will a Participant be permitted to obtain any award for, and each Participant hereby waives all rights to claim, punitive, incidental or consequential damages and any and all rights to have damages multiplied or otherwise increased and any other damages, other than damages for actual out-of-pocket expenses.
DrivenData, Inc., Competition Sponsor, and the employees, officers, directors, shareholders, agents, representatives, affiliates, subsidiaries, and advertising, promotional and legal advisors thereof (collectively, the “Released Parties”) are not responsible and shall not be liable for: (i) late, lost, delayed, damaged, misdirected, incomplete, illegible, or unintelligible entries; (ii) telephone, electronic, hardware, or software program, network, Internet, or computer malfunctions, failures, or difficulties; (iii) errors in transmission; (iv) any condition caused by events beyond the control of the Released Parties that may cause the Competition to be disrupted or corrupted; (v) any injuries, losses, or damages of any kind caused by a prize or resulting from acceptance, possession, or use of a prize, or from participation in the Competition (however (but only if required by law in your jurisdiction), this release and hold harmless commitment does not apply to cases of bodily injury or loss of life to the extent that any death or personal injury is caused by the negligence of DrivenData, Competition Sponsor, or other third parties, where liability to the injured party cannot be excluded by law); or (vi) any printing or typographical errors in any materials associated with the Competition.
Disclaimer of Warranties
You understand that we cannot and do not guarantee or warrant that the Data or other files available for downloading from the internet or the Competition Website will be free of viruses or other destructive code. You are responsible for implementing sufficient procedures and checkpoints to satisfy your particular requirements for anti-virus protection and accuracy of data input and output, and for maintaining a means external to our site for any reconstruction of any lost data. RELEASED PARTIES WILL NOT BE LIABLE FOR ANY LOSS OR DAMAGE CAUSED BY A DISTRIBUTED DENIAL-OF-SERVICE ATTACK, VIRUSES OR OTHER TECHNOLOGICALLY HARMFUL MATERIAL THAT MAY INFECT YOUR COMPUTER EQUIPMENT, COMPUTER PROGRAMS, DATA OR OTHER PROPRIETARY MATERIAL DUE TO YOUR USE OF THE WEBSITE OR ANY SERVICES OR ITEMS OBTAINED THROUGH THE WEBSITE OR TO YOUR DOWNLOADING OF ANY MATERIAL POSTED ON IT, OR ON ANY WEBSITE LINKED TO IT.
YOUR USE OF THE COMPETITION WEBSITE, ITS CONTENT AND ANY SERVICES OR ITEMS OBTAINED THROUGH THE WEBSITE AND RECEIPT OF ANY PRIZES IS AT YOUR OWN RISK. THE WEBSITE, ITS CONTENT AND ANY SERVICES OR ITEMS OBTAINED THROUGH THE WEBSITE ARE PROVIDED ON AN “AS IS” AND “AS AVAILABLE” BASIS, WITHOUT ANY WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, BEYOND ANY NON-EXCLUDABLE GUARANTEES (DEFINED BELOW). NEITHER THE RELEASED PARTIES NOR ANY PERSON ASSOCIATED WITH RELEASED PARTIES MAKES ANY WARRANTY OR REPRESENTATION WITH RESPECT TO THE COMPLETENESS, SECURITY, RELIABILITY, QUALITY, ACCURACY OR AVAILABILITY OF THE COMPETITION WEBSITE. WITHOUT LIMITING THE FOREGOING, NEITHER RELEASED PARTIES NOR ANYONE ASSOCIATED WITH THE RELEASED PARTIES REPRESENTS OR WARRANTS THAT THE COMPETITION WEBSITE, ITS CONTENT OR ANY SERVICES OR ITEMS OBTAINED THROUGH THE WEBSITE WILL BE ACCURATE, RELIABLE, ERROR-FREE OR UNINTERRUPTED, THAT DEFECTS WILL BE CORRECTED, THAT OUR SITE OR THE SERVER THAT MAKES IT AVAILABLE ARE FREE OF VIRUSES OR OTHER HARMFUL COMPONENTS OR THAT THE COMPETITION WEBSITE OR ANY SERVICES OR ITEMS OBTAINED THROUGH THE WEBSITE WILL OTHERWISE MEET YOUR NEEDS OR EXPECTATIONS.
NOTHING IN THESE COMPETITION RULES LIMITS, EXCLUDES, OR MODIFIES OR PURPORTS TO LIMIT, EXCLUDE, OR MODIFY ANY STATUTORY CONSUMER GUARANTEE OR ANY IMPLIED CONDITION OR WARRANTY, THE EXCLUSION OF WHICH FROM THESE COMPETITION RULES WOULD CONTRAVENE ANY STATUTE OR CAUSE ANY PART OF THESE TERMS TO BE VOID (“NON-EXCLUDABLE GUARANTEES”). SUBJECT TO THE LIMITATIONS IN THE PRECEDING SENTENCE AND TO THE MAXIMUM EXTENT PERMITTED BY ANY MANDATORY PROVISIONS OF APPLICABLE LAW, THE RELEASED PARTIES HEREBY DISCLAIM ALL WARRANTIES OF ANY KIND, WHETHER EXPRESS OR IMPLIED, STATUTORY OR OTHERWISE, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF MERCHANTABILITY, NON-INFRINGEMENT AND FITNESS FOR PARTICULAR PURPOSE.
THE FOREGOING DOES NOT AFFECT ANY WARRANTIES WHICH CANNOT BE EXCLUDED OR LIMITED UNDER APPLICABLE LAW.
Governing Law and Jurisdiction
DrivenData shall be the sole interpreter of these Competition Rules.
All matters relating to the Competition Rules or the Competition and any dispute or claim arising therefrom or related thereto (in each case, including non-contractual disputes or claims), shall be governed by and construed in accordance with the internal laws of the Commonwealth of Massachusetts without giving effect to any choice or conflict of law provision or rule (whether of the Commonwealth of Massachusetts or any other jurisdiction).
Any legal suit, action or proceeding arising out of, or related to, the Competition or the Competition Website shall be instituted exclusively in the federal courts of the United States or the courts of the Commonwealth of Massachusetts although we retain the right to bring any suit, action or proceeding against you for breach of these Terms of Use in your country of residence or any other relevant country. You waive any and all objections to the exercise of jurisdiction over you by such courts and to venue in such courts.
The invalidity or unenforceability of any provision of these Competition Rules shall not affect the validity or enforceability of any other provision. In the event that any provision is determined to be invalid or otherwise unenforceable or illegal, these Competition Rules shall otherwise remain in effect and be construed in accordance with their terms as if the invalid or illegal provision was not contained herein.