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Advanced Search Abstract Objective Mental illness is the leading cause of disability in the USA, but boundaries between different mental illnesses are notoriously difficult to define.
Electronic medical records EMRs have recently emerged as a powerful new source of information for defining the phenotypic signatures of specific diseases. We investigated how EMR-based text mining and statistical analysis could elucidate the phenotypic boundaries of three important neuropsychiatric illnesses—autism, bipolar disorder, and schizophrenia.
Methods We analyzed the medical records of over patients at two facilities using an automated text-processing pipeline to annotate the clinical notes with Unified Medical Language System codes and then searching for enriched codes, and associations among codes, that were representative of the three disorders.
We used dimensionality-reduction techniques on individual patient records to understand individual-level phenotypic variation within each disorder, as well as the degree of overlap among disorders.
Results We demonstrate that automated EMR mining can be used to extract relevant drugs and phenotypes associated with neuropsychiatric disorders and characteristic patterns of associations among them.
Patient-level analyses suggest a clear separation between autism and the other disorders, while revealing significant overlap between schizophrenia and bipolar disorder. Conclusions Because EMRs reflect the realities of patient care rather than idealized conceptualizations of disease states, we argue that automated EMR mining can help define the boundaries between different mental illnesses, facilitate cohort building for clinical and genomic studies, and reveal how clear expert-defined disease boundaries are in practice.
Background Fluid boundaries of mental illnesses Over the course of the 20th century, scientists have struggled to refine the mental health classification system. Even as diagnostic manuals swell with new disorders and additional definitions of existing conditions, the underlying complexity of the mental illness landscape has continued to handicap effective diagnosis and treatment.
Mental disorders are often characterized as distinct illnesses despite high rates of comorbidity, which indicate more fluid boundaries.
By revealing the phenotypic signatures of different disorders as they are observed and recorded by clinicians, EMRs could allow a more data-driven approach to the classification of Blooms taxonomy in congestive heart failure illnesses. After annotating these records with concepts from the Unified Medical Language System UMLS and other ontologies, we searched for concepts that were enriched in each disorder relative to the baseline population at each facility.
We also identified highly associated concept pairs in each disorder, and we applied dimensionality-reduction techniques to the individual patient records to determine variations in the clinical presentations for these disorders.
We present our findings with a focus on the specific areas of overlap among the different disorders.
We demonstrate that EMRs present a rich, objective and expert-sourced repository of information for studies seeking to delineate the clinical, phenotypic boundaries between important neuropsychiatric disorders.
Stanford Hospital is a bed teaching hospital and tertiary care facility; it admits over 24 patients per year and receives over outpatient visits. Because Stanford Hospital is a referral facility, its psychiatric patient population might not be representative of the general population of patients with autism, bipolar disorder, and schizophrenia.
We therefore considered data from both facilities in all parts of our analysis. All of the data in this study were deidentified using the method described in US Patent Application 13 We used this technology to process clinical notes that contained protected health information, keep only the medically relevant concepts, and transform the data into a deidentified patient—feature matrix.
The technology used to deidentify the data is freely available to academics, and can be licensed non-exclusively to commercial entities. Patient cohorts Our initial patient cohorts consisted of patients who had received International Classification of Diseases ICD-9 diagnosis codes of autism, bipolar disorder, or schizophrenia at any visit.
We then narrowed the cohorts, keeping patients who had received two separate diagnoses for the same disorder within 1 year and had more than 1 year between their earliest and latest notes.
While there is no standard way to identify patients with a specific mental diagnosis based on EMR data alone, the best-performing algorithm for extracting depressed patients from EMRs requires two separate diagnoses and at least 1 year of follow-up. Pathology results and radiology transcripts were excluded.
Detailed initial psychological evaluations were also excluded in the interest of patient privacy. Patient instructions and transcripts of phone calls were excluded.
Annotation pipeline The final set of notes was processed with the annotation pipeline described previously. Using NegEx and ConText rules to filter out negated terms and terms found in the family history.
Normalizing terms into concepts based on interontology mappings and semantic grouping by drug, disease, device, or procedure.
Brand name mentions were normalized to their generic names, and combination drugs to their constituent substances. Concept enrichment calculations We first identified those concepts that were a significantly enriched among patients with a particular mental disorder relative to the general population and b not indicative of the patient population at a particular facility, but rather, indicative of the disorder itself.
Because our six patient cohorts table 2 were of different sizes, statistical power calculations were critical: For each disorder, we carried out the following: Randomly selected notes from patients with that disorder.
This process involved first selecting a patient at random, then selecting one of that patient's notes again at random. Thus patients with longer histories were not over-represented.
Randomly selected notes from patients without a mental disorder diagnosis at the same facility in the same fashion. Repeated steps 1—3 twenty times for each facility.
It is important to note that this sampling procedure did not guarantee that each note in the sample came from a unique patient, even when the number of patients was much greater than ; it simply ensured that sampling was uniform across patients, regardless of length of history.
Finding associated concepts We defined the clinical presentation of a disease along two dimensions: Power and effect size calculations were less straightforward for the association analysis because of the widely varying numbers of notes in which each concept occurred.
We wanted to ensure that we did not see an increased number of significant concept—concept associations for a given disorder, such as bipolar disorder, simply because the larger number of notes for that disorder conferred greater power the ability to detect associations with smaller effect sizes.
If C1 and C2 were highly associated, we would expect C2 to occur in a high proportion of notes that contained C1 and a lower proportion of notes that did not contain C1.Oleandrin is a steroid saponin that consists of oleandrigenin having a 2,6-dideoxyO-methyl-alpha-L-arabino-hexopyranosyl residue attached to the oxygen function at position 3.
It is a cardenolide glycoside, a 14beta-hydroxy steroid, a steroid ester and a steroid saponin. Heart failure, sometimes known as congestive heart failure, occurs when your heart muscle doesn't pump blood as well as it should.
Certain conditions, such as narrowed arteries in your heart (coronary artery disease) or high blood pressure, gradually leave your heart too . A more qualitative evaluation using for example Bloom's taxonomy can provide a deeper and more complete understanding of knowledge Congestive heart failure and cognitive impairment in an older population.
and psychological status of patients with heart failure, Heart & Lung: The Journal of Acute and Critical Care, , 44, 5. Miller’s Pyramid of Assessment congestive heart failure. Ex3: Learner knows the indications, contraindications, and risks associated with the placement of a chest tube. KNOWS HOW uses knowledge in the acquisition, analysis, and interpretation of data and the.
Blooms Taxonomy In Congestive Heart Failure. Congestive Heart Failure: The Many Differences Kelly Strunk Wilmington University Comparison or Contrast Essay Congestive Heart Failure: The Many Differences Congestive heart failure is a very common disease. While many other forms of heart disease have become less common, heart failure has .
Aug 01, · Hawthorn remedies are also useful for cases of mild congestive heart failure and problems of irregular heartbeat or cardiac arrhythmia. Results usually take some months to show themselves, though the medication is known to work well in a large number of cases.