NLPIR/ICTCLA2018 ACADEMIC SEMINAR 6th ISSUE
INTRO
In the new semester, our Lab, Web Search Mining and Security Lab, plans to hold an academic seminar every Wednesdays, and each time a keynote speaker will share understanding of papers published in recent years with you.
Arrangement
This week’s seminar is organized as follows:
1. The seminar time is 1.pm, Wed, at Zhongguancun Technology Park ,Building 5, 1306.
2. The lecturer is Nada, the paper’s title is Machine Learning Approaches for Early DRG Classification and Resource Allocation.
3. The seminar will be hosted Zhaoyou Liu.
4. Attachment is the paper of this seminar, please download in advance
Everyone interested in this topic is welcomed to join us. the following is the abstract for this week’s pape
Machine Learning Approaches for Early DRG Classification and Resource Allocation
Daniel Gartne, Rainer Kolisch, Daniel B. Neill, Rema Padman
Abstract
Recent research has highlighted the need for upstream planning in healthcare service delivery systems, patient scheduling, and resource allocation in the hospital inpatient setting. This study examines the value of upstream planning within hospital-wide resource allocation decisions based on machine learning (ML) and mixed-integer programming (MIP), focusing on prediction of diagnosis-related groups (DRGs) and the use of these predictions for allocating scarce hospital resources. DRGs are a payment scheme employed at patients’ discharge, where the DRG and length of stay determine the revenue that the hospital obtains. We show that early and accurate DRG classification using ML methods, incorporated into an MIP-based resource allocation model, can increase the hospital’s contribution margin, the number of admitted patients, and the utilization of resources such as operating rooms and beds. We test these methods on hospital data containing more than 16,000 inpatient records and demonstrate improved DRG classification accuracy as compared to the hospital’s current approach. The largest improvements were observed at and before admission, when information such as procedures and diagnoses is typically incomplete, but performance was improved even after a substantial portion of the patient’s length of stay, and under multiple scenarios making different assumptions about the available information. Using the improved DRG predictions within our resource allocation model improves contribution margin by 2.9% and the utilization of scarce resources such as operating rooms and beds from 66.3% to 67.3% and from 70.7% to 71.7%, respectively. This enables 9.0% more nonurgent elective patients to be admitted as compared to the baseline.
