Projects and Authorship

A note on collaboration: Research is a collaborative activity and we encourage all students to collaborate and learn from each other. In general, when you put your name on something for research, you must: a) have materially contributed to the work, b) be able to defend the
research, and c) acknowledge the contribution of others. Keep this in mind when working together and submitting material for evaluation.

A note on authorship: As noted, the expectation is that by the end of the course the final project will be sufficiently developed to submit to a peer-reviewed journal. The author order can be a somewhat controversial issue and is left to the project participants to decide. We would strongly encourage you to discuss what the order will be, or what philosophy you will use to decide the order while forming groups. In the case of a dispute during or after the course, the instructors will likely not be able to mediate in any meaningful way. We would also recommend equal authorship (now more common), but the decision is left to each team.

For the clinicians: If you expect a certain level of authorship (first, last, etc.) you should mention this in your project pitch. Keep in mind that this is a two-way street involving both clinicians and data scientists. If a project fails to garner enough interest, it may not be able to be completed as part of the course.

A note on acknowledgement: Papers that result from work done during this course should recognize the contributions of the course in an acknowledgement or in other sections. The suggested language is: "This manuscript was composed by participants in the HST.953 course at the Massachusetts Institute of Technology, Fall 2022.'"

Project Descriptions

MIMIC-Based Projects:

1. Identifying History & Physical and Consent forms from the scanned documents: The prediction of these models have been integrated with the PIMS system and are being used by the pre-operative department to identify the forms before surgery. The performance of the models have been degraded lately due to change in the structure of scanned forms.
   

2. Predicting Ambulatory No-Show: The model to predict whether a patient will show up to an appointment or not  is developed and integrated with a dashboard. Prior to COVID, the ambulatory operations were using the dashboard and taking necessary intervention for high-risk no show patients. The appointment scheduling data and type of the appointment (more TeleHealth than In-person) have been changed drastically due to COVID. So, we will have to retrain the model and put it back into production.

3. Optimizing OR Blocks: The purpose of the model is to optimize the operating room schedules in order to address  the capacity challenges of inpatient census. This model was originally developed for BIDMC and there is a need to retrain the model with OR block data from Lahey Medical Center. The output of the model will be provided as a report to the OR Committee to change the schedules of surgeons.

    

St. Michael-Based Projects:

1. Predicting Relapse in Multiple Sclerosis (MS): MS is a chronic neurological condition where the immune system attacks nerve fibres and myelin sheathing in the brain and spinal cord. The condition can result in some patients being mildly affected to others who may lose their ability to communicate or walk. Many patients suffer relapses - often manifesting as symptoms of fatigue, tingling, blurred vision, weakness, and unsteady gait - during disease progression. A major goal of modern treatment is to prevent relapse and disease progression. The MS Clinic at St. Michael's Hospital is among the largest in the world with excellent patient follow-up. The goal of this project is to create a prediction algorithm that predicts MS relapse episodes among MS patients. Should an adequate algorithm be developed, it will be deployed into clinical practice to help guide patient care by our team of MS neurologists. A preliminary algorithm has already been developed with promising performance. 
2. Emergency Department Wait Time Prediction: A major predictor of inpatient satisfaction is the perceived wait time in the emergency department, yet many hospitals struggle to optimize their wait times. Knowing expected wait times for patients in the emergency department not helps with staffing, planning, and patient management. The purpose of this project is to develop 'real time' prediction algorithms that estimate overall and patient-specific wait times in the emergency department using a dataset from St. Michael's Hospital. The prediction will vary based on both patient characteristics and the ever-changing workload situation in the emergency department. Suitable algorithms will be deployed into clinical practice. 
3. Traumatic Brain Injury Outcomes: Patients experiencing traumatic brain injury often need urgent surgical intervention - the level of care needed is often determined by medical imaging findings on head computed tomography (CT) scans as well as certain clinical characteristics. Specialized neurosurgeons and neuroradiologists are needed to 'read' head CT scans and make the determination of whether or not a patient requires urgent surgery. Many hospitals do not have such specialists and rely on larger hospitals who do have this expertise to advise them on whether a patients needs to be urgently transferred to an appropriate hospital, placing significant burden on specialist hospitals and their staff. The purpose of this project is to develop an algorithm that identifies patterns on a head CT that are suggestive of whether or not a patient requires urgent surgical intervention. The available dataset consists of several thousand annotated head CT scans. Suitable algorithms will be deployed into clinical practice.