scholarly journals PPM11 USING ARTIFICIAL INTELLIGENCE TO IMPROVE CAPTURE OF METASTATIC BREAST CANCER (BC) STATUS IN ELECTRONIC HEALTH RECORDS (EHR)

2019 ◽  
Vol 22 ◽  
pp. S334-S335
Author(s):  
S. Agrawal ◽  
V. Colano ◽  
P. Chandrashekaraiah ◽  
V.P. Vaidya ◽  
O. Inbar ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Metastatic Breast Cancer ◽  
Electronic Health Records ◽  
Metastatic Breast ◽  
Health Records ◽  
Electronic Health

scholarly journals Predicting optimal treatment regimens for patients with HR+/HER2- breast cancer using machine learning based on electronic health records

2021 ◽  
Vol 10 (9) ◽  
pp. 777-795
Author(s):  
Zhanglin Lin Cui ◽  
Zbigniew Kadziola ◽  
Ilya Lipkovich ◽  
Douglas E Faries ◽  
Kristin M Sheffield ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Electronic Health Records ◽  
Metastatic Breast ◽  
Optimal Treatment ◽  
Health Records ◽  
Treatment Regimens ◽  
Her2 Breast Cancer ◽  
Electronic Health ◽  
Line Of Therapy

Aim: To predict optimal treatments maximizing overall survival (OS) and time to treatment discontinuation (TTD) for patients with metastatic breast cancer (MBC) using machine learning methods on electronic health records. Patients/methods: Adult females with HR+/HER2- MBC on first- or second-line systemic therapy were eligible. Random survival forest (RSF) models were used to predict optimal regimen classes for individual patients and each line of therapy based on baseline characteristics. Results: RSF models suggested greater use of CDK4 & 6 inhibitor-based therapies may maximize OS and TTD. RSF-predicted optimal treatments demonstrated longer OS and TTD compared with nonoptimal treatments across line of therapy (hazard ratios = 0.44∼0.79). Conclusion: RSF may help inform optimal treatment choices and improve outcomes for patients with HR+/HER2- MBC.

scholarly journals Perspective of Patients with Metastatic Breast Cancer on Electronic Access to Scan Results: Mixed-Methods Study

10.2196/15723 ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. e15723
Author(s):  
Christina Baun ◽  
Marianne Vogsen ◽  
Marie Konge Nielsen ◽  
Poul Flemming Høilund-Carlsen ◽  
Malene Grubbe Hildebrandt
Keyword(s):  
Breast Cancer ◽  
Computed Tomography ◽  
Metastatic Breast Cancer ◽  
Mixed Methods ◽  
Electronic Health Records ◽  
Metastatic Breast ◽  
Mixed Methods Study ◽  
Health Records ◽  
Electronic Access ◽  
Online Access

Background Patient-accessible electronic health records give patients quick and easy access to their health care data, enabling them to view their test results online prior to a clinic visit. Hospital reports can be difficult for patients to understand, however, and can lead to unnecessary anxiety. Objective We aimed to investigate the attitudes and experiences of Danish patients with metastatic breast cancer in using electronic health records to view their own scan results. Methods We conducted a prospective mixed-methods study in a sequential design at our institution during 2018. Participants were women with metastatic breast cancer who were having scans every 3 months (combined positron emission tomography and computed tomography or computed tomography alone) to monitor treatment effects. Participants first received an online questionnaire about their knowledge and use of online access to scan results. We then conducted semistructured interviews with 4 women who used the online access to view their scan results. Results A total of 46 patients received the questionnaire (median age 66, SD 11.8, range 34-84 years). Of these women, 38 (83%) completed the survey (median age 69, SD 10.7, range 42-84 years). Most patients (34/38) were aware of the opportunity to access their reports online, but only 40% (15/38) used this access to read their scan results. Barriers to online access were (1) anxiety over reading the scan results in the absence of clinician support, and (2) a preference to receive all disease information at their next hospital appointment. The patients who read their scan result found that facilitators were greater transparency and empowerment, and barriers were the consequences of reading bad news, the feeling of dilemma about the access, and the medical terminology. Conclusions Patients with metastatic breast cancer generally had a positive attitude toward electronic access to their scan results, and those who used this opportunity played a greater participatory role in their disease and its management. Others described the potential distress this opportunity caused. The study findings suggest that immediate online access to scan results should be available to patients, but it needs a support function alongside that ensures optimal patient care.

Research and application of artificial intelligence based on electronic health record data from patients with cancer: a systematic review (Preprint)

2021 ◽  
Author(s):  
Xinyu Yang ◽  
Dongmei Mu ◽  
Hao Peng ◽  
Hua Li ◽  
Ying Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Electronic Health Records ◽  
Ensemble Methods ◽  
Future Research ◽  
Electronic Health Record Data ◽  
Health Records ◽  
Patients With Cancer ◽  
Cancer Management ◽  
Treatment And Prevention ◽  
Electronic Health

BACKGROUND With the accumulation of electronic health records data and the development of artificial intelligence, patients with cancer urgently need new evidence of more personalized clinical and demographic characteristics and more sophisticated treatment and prevention strategies. However, no research has systematically analyzed the application and significance of electronic health records and artificial intelligence in cancer care. OBJECTIVE In this study, we reviewed the literature on the application of AI based on EHR data from patients with cancer, hoping to provide reference for subsequent researchers, and help accelerate the application of EHR data and AI technology in the field of cancer, so as to help patients get more scientific and accurate treatment. METHODS Three databases were systematically searched to retrieve potentially relevant articles published from January 2009 to October 2020. A combination of terms related to "electronic health records", "artificial intelligence" and "cancer" was used to search for these publications. RESULTS Of the 1034 articles considered, 148 met the inclusion criteria. The review has shown that ensemble methods and deep learning were on the rise. It presented the representative literatures on the subfield of cancer diagnosis, treatment and care. In addition, the vast majority of studies in this area were based on private institutional databases, resulting in poor portability of the proposed methodology process. CONCLUSIONS The use of new methods and electronic health records data sharing and fusion were recommended for future research. With the help of specialists, artificial intelligence and the mining of massive electronic medical records could provide great opportunities for improving cancer management.

Abstract P148: Heart Disease Among Breast Cancer Patients

Circulation ◽  
2018 ◽  
Vol 137 (suppl_1) ◽  
Author(s):  
Tekeda F Ferguson ◽  
Sunayana Kumar ◽  
Denise Danos
Keyword(s):  
Breast Cancer ◽  
Heart Disease ◽  
Electronic Health Records ◽  
Cancer Patients ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Health Concern ◽  
Breast Cancer Patients ◽  
Health Records ◽  
Electronic Health

Purpose: In conjunction with women being diagnosed earlier with breast cancer and a rapidly aging population, advances in cancer therapies have swiftly propelled cardiotoxicity as a major health concern for breast cancer patients. Frequent cardiotoxicity outcomes include: reduced left ventricular ejection fraction (LVEF), myocardial infarction, asymptomatic or hospitalized heart failure, arrhythmias, hypertension, and thromboembolism. The purpose of this study was to use an electronic health records system determine if an increased odds of heart disease was present among women with breast cancer. Methods: Data from the Research Action for Health Network (REACHnet) was used for the analysis. REACHnet is a clinical data research network that uses the common data model to extract electronic health records (EHR) from health networks in Louisiana (n=100,000).Women over the age of 30 with data (n=35,455) were included in the analysis. ICD-9 diagnosis codes were used to classify heart disease (HD) (Hypertensive HD, Ischemic HD, Pulmonary HD, and Other HD) and identify breast cancer patients. Additional EHR variables considered were smoking status, and patient vitals. Chi-square tests, crude, and adjusted logistic regression models were computed utilizing SAS 9.4. Results: Utilizing diagnoses codes our study team has estimated 28.6% of women over the age of 30 with a breast cancer diagnosis (n=816) also had a heart disease diagnosis, contrasted with 15.6% of women without a breast cancer diagnosis. Among patients with heart disease, there was no significant difference in the distribution of the type of heart disease diagnoses by breast cancer status (p=0.87). There was a 2.21 (1.89, 2.58) crude odds ratio of having a CVD diagnoses among breast cancer cases when referenced to cancer free women. After adjusting for age (30-49, 50-64, 65+), race (black/white), and comorbidities (obesity/overweight, diabetes, current smoker) there was an increased risk of heart disease (OR: 1.24 (1.05, 1.47)). Conclusion: The short-term and long-term consequences of cardiotoxicity on cancer treatment risk-to-benefit ratio, survivorship issues, and competing causes of mortality are increasingly being acknowledged. Our next efforts will include making advances in predictive risk modeling. Maximizing benefits while reducing cardiac risks needs to become a priority in oncologic management and monitoring for late-term toxic effects.

Sign in / Sign up

Export Citation Format

Share Document