Clinical care guidelines recommend that newly diagnosed prostate cancer patients at high risk for metastatic spread receive a bone scan prior to treatment and that low risk patients not receive it. The objective was to develop an automated pipeline to interrogate heterogeneous data to evaluate the use of bone scans using a two different Natural Language Processing (NLP) approaches.
Algorithm to select subjects with "normal" electrocardiograms. Subjects do not have heart disease, interfering medications, or abnormal electrolytes at the time of the normal ECG. Individuals may, however, develop abnormalities later in life.
Hypothetical timeline for a single patient:
Please refer to attached documents.
Version 1.0, July 2020
Automated Phenotyping Tool for identifying DLD cases in health-systems data (APT-DLD) is an algorithm for classifying/identifying developmental language disorder cases in electronic health records system data. APT-DLD can be used to:
1. Identify pediatric DLD cases from electronic health record systems using ICD9 and ICD10 codes
2. Study epidemiology and population-level charateristics of DLD from EHRs
The How-To guide for using APT-DLD is provided in the files listed below.
Described in this document are the Stanford University algorithms for extracting both cases and controls of digital rectal examination (DRE) from electronic health records (EHR) of prostate cancer patients. DRE is a clinical procedure, part of a set of quality metrics used to determine quality care for these patients. In this regard, DRE is defined as quality care when it is performed within a time period of up to six months before first treatment for prostate cancer. For the purposes of this algorithm a case is defined as DRE documented, whereas a control is DRE not documented.
Algorithm to select patients with height measures unaffected by environmental factors (i.e. diseases & medications) that can cause an abnormal change in height. Comprehensive documentation of the algorithm can be found here on this PheKB page. Similar to our T2DM algorithm, you can install an executable version of the algorithm implemented as workflows for the Konstanz Information Miner (KNIME) data mining tool.
Algorithm to identify patients with low levels of high-density lipoproteins (HDL).
FLOWCHART of HDL Phenotyping Process