Machine learning is an often-used term that has been promised to do everything from making workers more productive to taking over individuals' jobs entirely. Frankly, it will likely be many years before anyone should be concerned about being replaced by artificial intelligence (AI) at their job. However, doctors might find AI impinging upon their jobs sooner rather than later. The medical field has some characteristics that make it an attractive target for machine learning. The high stakes nature of correct disease diagnosis, coupled with over-worked and fatigued doctors, can lead to cases where patients with easily treatable diseases go undiagnosed and suffer greatly from this.
Identifying a patient's important medical problems requires broad and deep medical expertise, as well as significant time to gather all the relevant facts from the patient's medical record and assess the clinical importance of the facts in reaching the final conclusion. A patient's medical problem list is by far the most critical information that a physician uses in treatment and care of a patient. In spite of its critical role, its curation, manual or automated, has been an unmet need in clinical practice. We developed a machine learning technique in IBM Watson to automatically generate a patient's medical problem list. The machine learning model uses lexical and medical features extracted from a patient's record using NLP techniques. We show that the automated method achieves 70% recall and 67% precision based on the gold standard that medical experts created on a set of de-identified patient records from a major hospital system in the US. To the best of our knowledge this is the first successful machine learning/NLP method of extracting an open-ended patient's medical problems from an Electronic Medical Record (EMR). This paper also contributes a methodology for assessing accuracy of a medical problem list generation technique.
Machine learning research on medical images has lagged similar work on conventional visible light images due to the added complexity of medical images and the lack of available annotated large image sets. To address this limitation, Stanford researchers are creating a massive clinical imaging research resource, containing de-identified versions of all Stanford radiology images, annotated with concepts from a medical imaging ontology, and linked to genomic data, tissue banks, and information from patients' electronic medical records. This dataset contains 0.5 petabyte of clinical radiology data, comprising 4.5 million studies, and over 1 billion images. The broad long-term objective of this resource is to dramatically reduce diagnostic imaging errors by: (1) facilitating reproducible science through standardization of data and algorithms for medical image machine learning research, (2) enabling patients to participate in the scientific enterprise by volunteering their data for these experiments, (3) spurring innovation by hosting competitions on clinically validated image sets, and (4) disseminating the resulting data, informatics tools, and decision support algorithms to the widest possible scientific audience. We'll review progress toward creation of the Stanford Medical ImageNet, including details of database structure and contents, and recent results from deep learning experiments on the data it contains.
There is a famous scene in the movie "Harry Potter and the Half‐Blood Prince": A student has been cursed, investigations are under way. All at once, Harry shouts "It was Malfoy." McGonagall replies "This is a very serious accusation, Potter." "Indeed," agrees Snape and continues "Your evidence?" Harry immediately responds, "I just know."
When MIT successfully created AI that can diagnose skin cancer it was a massive step in the right direction for medical science. A neural-network can process huge amounts of data. More data means better research, more accurate diagnosis, and the potential to save lives by the thousands or millions. In the future medical technicians will become data-scientists to support the AI-powered diagnostics departments that every hospital will need. Radiologists are going to need a different education than the one they have now -- they're gonna need help from Silicon Valley.