Artificial intelligence in oncology: Path to implementation.

In recent years, the field of artificial intelligence (AI) in oncology has grown exponentially. AI solutions have been developed to tackle a variety of cancer-related challenges. Medical institutions, hospital systems, and technology companies are developing AI tools aimed at supporting clinical decision making, increasing access to cancer care, and improving clinical efficiency while delivering safe, high-value oncology care.

Facilitating information extraction without annotated data using unsupervised and positive-unlabeled learning.

Information extraction (IE), the distillation of specific information from unstructured data, is a core task in natural language processing. For rare entities (<1% prevalence), collection of positive examples required to train a model may require an infeasibly large sample of mostly negative ones. We combined unsupervised- with biased positive-unlabeled (PU) learning methods to: 1) facilitate positive example collection while maintaining the assumptions needed to 2) learn a binary classifier from the biased positive-unlabeled data alone.

Unsupervised clinical relevancy ranking of structured medical records to retrieve condition-specific information in the emergency department.

Background: Decision making in the Emergency Department (ED) requires timely identification of clinical information relevant to the complaints. Existing information retrieval solutions for the electronic health record (EHR) focus on patient cohort identification and lack clinical relevancy ranking. We aimed to compare knowledge-based (KB) and unsupervised statistical methods for ranking EHR information by relevancy to a chief complaint of chest or back pain among ED patients.

A dynamic reaction picklist for improving allergy reaction documentation in the electronic health record.

Objective: Incomplete and static reaction picklists in the allergy module led to free-text and missing entries that inhibit the clinical decision support intended to prevent adverse drug reactions. We developed a novel, data-driven, "dynamic" reaction picklist to improve allergy documentation in the electronic health record (EHR).

Materials And Methods: We split 3 decades of allergy entries in the EHR of a large Massachusetts healthcare system into development and validation datasets.

Mining clinical phrases from nursing notes to discover risk factors of patient deterioration.

Objective: Early identification and treatment of patient deterioration is crucial to improving clinical outcomes. To act, hospital rapid response (RR) teams often rely on nurses' clinical judgement typically documented narratively in the electronic health record (EHR). We developed a data-driven, unsupervised method to discover potential risk factors of RR events from nursing notes.

Unsupervised Machine Learning of Topics Documented by Nurses about Hospitalized Patients Prior to a Rapid-Response Event.

Background: In the hospital setting, it is crucial to identify patients at risk for deterioration before it fully develops, so providers can respond rapidly to reverse the deterioration. Rapid response (RR) activation criteria include a subjective component ("worried about the patient") that is often documented in nurses' notes and is hard to capture and quantify, hindering active screening for deteriorating patients.

Objectives: We used unsupervised machine learning to automatically discover RR event risk/protective factors from unstructured nursing notes.