Grounded in Research, Backed by Evidence
At Recorded Health, we believe in evidence-based innovation. Below are a selection of peer-reviewed articles, white papers, and case studies that support our work and demonstrate our expertise.
From Patient Consultations to Graphs: Leveraging LLMs for Patient Journey Knowledge Graph Construction
Abstract—The transition towards patient-centric healthcare necessitates a comprehensive understanding of patient journeys, which encompass all healthcare experiences and interactions across the care spectrum. Existing healthcare data systems are often fragmented and lack a holistic representation of patient trajectories, creating challenges for coordinated care and personalized interventions...
Patient Journey Ontology: Representing Medical Encounters for Enhanced Patient-Centric Applications
Abstract—The healthcare industry is moving towards a patient-centric paradigm that requires advanced methods for managing and representing patient data. This paper presents a Patient Journey Ontology (PJO), a framework that aims to capture the entirety of a patient’s healthcare encounters. Utilizing ontologies, the PJO integrates different patient data sources like medical histories, diagnoses, treatment pathways...
CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations
Abstract—This paper presents CLINICSUM, a novel frame- work designed to automatically generate clinical summaries from patient-doctor conversations. It utilizes a two-module architec- ture: a retrieval-based filtering module that extracts Subjec- tive, Objective, Assessment, and Plan (SOAP) information from Coconversation transcripts, and an inference module powered by fine-tuned Pre-trained Language Models (PLMs)...
Patient-centric Knowledge Graphs: a Survey of Current Methods, Challenges, and Applications
Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in healthcare that focuses on individualized patient care by mapping the patient’s health information holistically and multi-dimensionally. PCKGs integrate various types of health data to provide healthcare professionals with a comprehensive understanding of a patient’s health, enabling more personalized and effective care...
Medical-GAT: Cancer Document Classification Leveraging Graph-Based Residual Network for Scenarios with Limited Data
Abstract—Accurate classification of cancer-related medical ab- stracts is crucial for healthcare management and research. However, obtaining large, labeled datasets in the medical domain is challenging due to privacy concerns and the complexity of detailed clinical data. This scarcity of annotated data impedes the development of effective...
MedInsight: A Multi-Source Context Augmentation Framework for Generating Patient-Centric Medical Responses Using
Large Language Models
Providing contextual and comprehensive medical information tailored to individual patients is critical for enabling effective care in the healthcare domain. However, existing approaches often struggle to deliver personalized responses due to the distributed nature of medical data across multiple sources such as patient records, medical literature, and online...
Deep Representation Learning: Fundamentals, Technologies, Applications, and Open Challenges
Machine learning algorithms have had a profound impact on the field of computer science over the past few decades. The performance of these algorithms heavily depends on the representations derived from the data during the learning process. Successful learning processes aim to produce concise, discrete, meaningful representations that can be effectively applied to various tasks. Recent advancements in deep learning models have proven to be....
Factors associated with long-term mechanical ventilation in patients undergoing cardiovascular surgery
One of the main therapy for coronary artery disease is surgery. Prolonged mechanical ventilation in patients with cardiac surgery is associated with high mortality. This study aimed to determine the factors related to long-term mechanical ventilation (LTMV) in patients undergoing cardiovascular surgery. More than sixteen million people die each year from cardiovascular disease (CVD), which is currently the most prevalent severe, chronic, and life-threatening disease...
Designing an Artificial Immune System inspired Intrusion Detection System
The Human Immune System (HIS) works to protect a body from infection, illness, and disease. This system can inspire cybersecurity professionals to design an Artificial Immune System (AIS) based Intrusion Detection System (IDS). These biologically inspired algo- rithms using Self/Nonself and Danger Theory can directly augment IDS designs and implementations. In this paper, we include an examination into the elements of design necessary for building an AIS-IDS framework and present an architecture to create such systems.
A Study of Emergency Department Patient Admittance Predictors
We introduce and compare two prediction systems on the task of replicating human decisions regarding patient admittance in a typical American emergency department. The data-set used describes the patient trajectories in a 65,000 patient per-year emergency department in the United States. Among the descriptive attributes those of prime importance are the severity of the patient’s condition and the time they waited to be admitted from the waiting room to the department proper. A recurrent neural network (RNN) is developed to learn the task..
Predictive analytics for emergency department patient flow
In this work, we produce several prediction models for aspects of hospital emergency departments. Firstly, we demonstrate the use of a recurrent neural network to predict the rate of patient arrival at a hospital emergency department. The prediction is made on a per hour basis using date, time, calendar, and weather information. Then, we present our comparison of two prediction systems on the task of replicating the human decisions of patient admittance in a typical American emergency department. Again, a recurrent neural network...
Sepsis Prediction: An Attention-Based Interpretable Approach
Abstract—Sepsis is the leading cause of death in ICUs and a very costly medical phenomena. The earlier it is predicted, the less inpatient mortality and the less the length of ICU stay, thus a major cut in medical expenses. Although the current deep learning models are able to make predictions about the possibility of sepsis in the ICU, they still lack the ability to reveal the major factors that lead to the outcomes of the predictions. In this paper, we have explored the use of an attention-based model in prediction of sepsis which provides more details on...
Recent Approaches in Prognostics: State of the Art
Abstract—The need for Prognostics and Health Manage- ment (PHM) systems has increased along with an increase in domains that require intelligent predictive systems, which can help in decreasing the downtime of the assets thereby curbing increasing maintenance costs, to manage asset in- ventories and customer satisfaction. Many techniques have been developed in the field of Prognostics Maintenance. This paper reviews the recent approaches that were developed and implemented on NASA’s turbofan engine dataset, which was developed as a part of PHM’08 challenge and C-MAPSS dataset.