Accuracy of Electronic Medical Record Follow-Up Data for Estimating the Survival Time of Patients With Cancer.

Purpose: For real-world evidence, it is convenient to use routinely collected data from the electronic medical record (EMR) to measure survival outcomes. However, patients can become lost to follow-up, causing incomplete data and biased survival time estimates. We quantified this issue for patients with metastatic cancer seen in an academic health system by comparing survival estimates from EMR data only and from EMR data combined with high-quality cancer registry data.

Natural Language Processing to Identify Cancer Treatments With Electronic Medical Records.

Purpose: Knowing the treatments administered to patients with cancer is important for treatment planning and correlating treatment patterns with outcomes for personalized medicine study. However, existing methods to identify treatments are often lacking. We develop a natural language processing approach with structured electronic medical records and unstructured clinical notes to identify the initial treatment administered to patients with cancer.

Automated model versus treating physician for predicting survival time of patients with metastatic cancer.

Objective: Being able to predict a patient's life expectancy can help doctors and patients prioritize treatments and supportive care. For predicting life expectancy, physicians have been shown to outperform traditional models that use only a few predictor variables. It is possible that a machine learning model that uses many predictor variables and diverse data sources from the electronic medical record can improve on physicians' performance.

Dynamin impacts homology-directed repair and breast cancer response to chemotherapy.

After the initial responsiveness of triple-negative breast cancers (TNBCs) to chemotherapy, they often recur as chemotherapy-resistant tumors, and this has been associated with upregulated homology-directed repair (HDR). Thus, inhibitors of HDR could be a useful adjunct to chemotherapy treatment of these cancers. We performed a high-throughput chemical screen for inhibitors of HDR from which we obtained a number of hits that disrupted microtubule dynamics.

Automated Survival Prediction in Metastatic Cancer Patients Using High-Dimensional Electronic Medical Record Data.

Background: Oncologists use patients' life expectancy to guide decisions and may benefit from a tool that accurately predicts prognosis. Existing prognostic models generally use only a few predictor variables. We used an electronic medical record dataset to train a prognostic model for patients with metastatic cancer.

Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) Utilizing Free-Text Clinical Narratives.

We propose a deep learning model - Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) for estimating short-term life expectancy (>3 months) of the patients by analyzing free-text clinical notes in the electronic medical record, while maintaining the temporal visit sequence. In a single framework, we integrated semantic data mapping and neural embedding technique to produce a text processing method that extracts relevant information from heterogeneous types of clinical notes in an unsupervised manner, and we designed a recurrent neural network to model the temporal dependency of the patient visits. The model was trained on a large dataset (10,293 patients) and validated on a separated dataset (1818 patients).

Increased Risk of Cutaneous Squamous Cell Carcinoma After Vismodegib Therapy for Basal Cell Carcinoma.

Importance: Smoothened inhibitors (SIs) are a new type of targeted therapy for advanced basal cell carcinoma (BCC), and their long-term effects, such as increased risk of subsequent malignancy, are still being explored.

Objective: To evaluate the risk of developing a non-BCC malignancy after SI exposure in patients with BCC.

Design, Setting, And Participants: A case-control study at Stanford Medical Center, an academic hospital.

Asymmetry in noise-induced hearing loss: evaluation of two competing theories.

Competing theories exist about why asymmetry is observed in noise-induced hearing loss (NIHL). We evaluated these theories using a cohort of young workers studied over 16 years. The study aim was to describe and evaluate patterns of hearing loss and asymmetry by gender, agricultural exposure and gunfire exposure.

Determinants of early-stage hearing loss among a cohort of young workers with 16-year follow-up.

Objectives: The authors had a unique opportunity to study the early impacts of occupational and recreational exposures on the development of noise-induced hearing loss (NIHL) in a cohort of 392 young workers. The objectives of this study were to estimate strength of associations between occupational and recreational exposures and occurrence of early-stage NIHL and to determine the extent to which relationships between specific noise exposures and early-stage NIHL were mitigated through the use of hearing protection.

Methods: Participants were young adults who agreed to participate in a follow-up of a randomised controlled trial.

Randomized trial of a hearing conservation intervention for rural students: long-term outcomes.

Objectives: We had the rare opportunity to conduct a cluster-randomized controlled trial to observe the long-term (16-year) effects of a well-designed hearing conservation intervention for rural high school students. This trial assessed whether the intervention resulted in (1) reduced prevalence of noise-induced hearing loss (NIHL) assessed clinically and/or (2) sustained use of hearing protection devices.

Methods: In 1992-1996, 34 rural Wisconsin schools were recruited and 17 were assigned randomly to receive a comprehensive, 3-year, hearing conservation intervention.

Hearing conservation program for agricultural students: short-term outcomes from a cluster-randomized trial with planned long-term follow-up.

Objectives: (1) To conduct a contemporary analysis of historical data on short-term efficacy of a 3-year hearing conservation program conducted from 1992 to 1996 in Wisconsin, USA, with 753 high school students actively involved in farm work; (2) to establish procedures for assessment of hearing loss for use in a recently funded follow-up of this same hearing conservation program cohort.

Methods: We analyzed a pragmatic cluster-randomized controlled trial, with schools as the unit of randomization. Thirty-four rural schools were recruited and randomized to intervention or control.

New models and online calculator for predicting non-sentinel lymph node status in sentinel lymph node positive breast cancer patients.

Background: Current practice is to perform a completion axillary lymph node dissection (ALND) for breast cancer patients with tumor-involved sentinel lymph nodes (SLNs), although fewer than half will have non-sentinel node (NSLN) metastasis. Our goal was to develop new models to quantify the risk of NSLN metastasis in SLN-positive patients and to compare predictive capabilities to another widely used model.

Methods: We constructed three models to predict NSLN status: recursive partitioning with receiver operating characteristic curves (RP-ROC), boosted Classification and Regression Trees (CART), and multivariate logistic regression (MLR) informed by CART.