Text Mining of Patient Demographics and Diagnoses from Psychiatric Assessments

Automatic extraction of patient demographics and psychiatric diagnoses from clinical notes allows for the collection of patient data on a large scale. This data could be used for a variety of research purposes including outcomes studies or developing clinical trials. However, current research has not yet discussed the automatic extraction of demographics and psychiatric diagnoses in detail. The aim of this study is to apply text mining to extract patient demographics - age, gender, marital status, education level, and admission diagnoses from the psychiatric assessments at a mental health hospital and also assign codes to each category. Gender is coded as either Male or Female, marital status is coded as either Single, Married, Divorced, or Widowed, and education level can be coded starting with Some High School through Graduate Degree (PhD/JD/MD etc. Level). Classifications for diagnoses are based on the DSM-IV. For each category, a rule-based approach was developed utilizing keyword-based regular expressions as well as constituency trees and typed dependencies. We employ a two-step approach that first maximizes recall through the development of keyword-based patterns and if necessary, maximizes precision by using NLP-based rules to handle the problem of ambiguity. To develop and evaluate our method, we annotated a corpus of 200 assessments, using a portion of the corpus for developing the method and the rest as a test set. F-score was satisfactory for each category (Age: 0.997; Gender: 0.989; Primary Diagnosis: 0.983; Marital Status: 0.875; Education Level: 0.851) as was coding accuracy (Age: 1.0; Gender: 0.989; Primary Diagnosis: 0.922; Marital Status: 0.889; Education Level: 0.778). These results indicate that a rule-based approach could be considered for extracting these types of information in the psychiatric field. At the same time, the results showed a drop in performance from the development set to the test set, which is partly due to the need for more generality in the rules developed

Utilizing Consumer Health Posts for Pharmacovigilance: Identifying Underlying Factors Associated with Patients’ Attitudes Towards Antidepressants

Non-adherence to antidepressants is a major obstacle to antidepressants therapeutic benefits, resulting in increased risk of relapse, emergency visits, and significant burden on individuals and the healthcare system. Several studies showed that non-adherence is weakly associated with personal and clinical variables, but strongly associated with patients’ beliefs and attitudes towards medications. The traditional methods for identifying the key dimensions of patients’ attitudes towards antidepressants are associated with some methodological limitations, such as concern about confidentiality of personal information. In this study, attempts have been made to address the limitations by utilizing patients’ self report experiences in online healthcare forums to identify underlying factors affecting patients attitudes towards antidepressants. The data source of the study was a healthcare forum called “askapatients.com”. 892 patients’ reviews were randomly collected from the forum for the four most commonly prescribed antidepressants including Sertraline (Zoloft) and Escitalopram (Lexapro) from SSRI class, and Venlafaxine (Effexor) and duloxetine (Cymbalta) from SNRI class. Methodology of this study is composed of two main phases: I) generating structured data from unstructured patients’ drug reviews and testing hypotheses concerning attitude, II) identification and normalization of Adverse Drug Reactions (ADRs), Withdrawal Symptoms (WDs) and Drug Indications (DIs) from the posts, and mapping them to both The UMLS and SNOMED CT concepts. Phase II also includes testing the association between ADRs and attitude. The result of the first phase of this study showed that “experience of adverse drug reactions”, “perceived distress received from ADRs”, “lack of knowledge about medication’s mechanism”, “withdrawal experience”, “duration of usage”, and “drug effectiveness” are strongly associated with patients attitudes. However, demographic variables including “age” and “gender” are not associated with attitude. Analysis of the data in second phase of the study showed that from 6,534 identified entities, 73% are ADRs, 12% are WDs, and 15 % are drug indications. In addition, psychological and cognitive expressions have higher variability than physiological expressions. All three types of entities were mapped to 811 UMLS and SNOMED CT concepts. Testing the association between ADRs and attitude showed that from twenty-one physiological ADRs specified in the ASEC questionnaire, “dry mouth”, “increased appetite”, “disorientation”, “yawning”, “weight gain”, and “problem with sexual dysfunction” are associated with attitude. A set of psychological and cognitive ADRs, such as “emotional indifference” and “memory problem were also tested that showed significance association between these types of ADRs and attitude. The findings of this study have important implications for designing clinical interventions aiming to improve patients\u27 adherence towards antidepressants. In addition, the dataset generated in this study has significant implications for improving performance of text-mining algorithms aiming to identify health related information from consumer health posts. Moreover, the dataset can be used for generating and testing hypotheses related to ADRs associated with psychiatric mediations, and identifying factors associated with discontinuation of antidepressants. The dataset and guidelines of this study are available at https://sites.google.com/view/pharmacovigilanceinpsychiatry/hom

A Descriptive Analysis of 1251 Solid Organ Transplant Visits to the Emergency Department

Background: As solid organ transplants become more common, recipients present more frequently to the emergency department (ED) for care.Methods: We performed a retrospective medical record review of ED visits of all patients who received an organ transplant at our medical center from 2000-2004, and included all visits following the patients’ transplant surgery through December 2005 or until failed graft, lost to follow up, or death. Clinically relevant demographic variables, confounding and outcome variables were recorded. Kidney, liver and combined kidney with other organ transplant recipients were included.Results: Five hundred ninety-three patients received kidney (395), liver (161), or combined renal (37) organ transplants during the study period, resulting in 1,251 ED visits. This represents 3.15 ED visits/patient followed over a mean of 30.8 months. Abdominal pain/gastrointestinal (GI) symptoms (31.3%) and infectious complaints (16.7%) were the most common presentations. The most common ED discharge diagnoses were fever/infection (36%), GI/Genitourinary (GU) pathology (20.4%) and dehydration (15%). Renal transplant recipients were diagnosed with infectious processes most often, despite time elapsed from transplant. Liver transplant patients had diagnoses of fever/infection most often in their first 30 days post transplant. Thereafter they were more likely to develop GI/GU pathology. After the first year of transplantation, cardiopulmonary and musculoskeletal pathology become more common in all transplant organ groups. Of the 1,251 ED visits, 762 (60.9%) resulted in hospitalization. Chief complaints of abdominal pain/GI symptoms, infectious complaints, cardiovascular and neurologic symptoms, and abnormal laboratory studies were significantly likely to result in hospitalization.Conclusions: This study demonstrates a significant utilization of the ED by transplant recipients, presenting with a wide variety of symptoms and diagnoses, and with a high hospitalization rate. As the transplant-recipient population grows, these complex patients continue to present diagnostic and treatment challenges to primary care and emergency physicians.[WestJEM. 2009;10:48-54.

Predicting Occurrence of the Term Sarcopenia with Semi-Supervised Machine Learning

Sarcopenia is a medical condition that involves loss of muscle mass. It has been difficult todefine and only recently assigned an official medical code, leading to many medical records lacking a coded diagnosis although the clinical note text may discuss it or symptoms of it. This thesis investigates the application of machine learning and natural language processing to analyze clinical note text to see how well the term ’sarcopenia’ can be predicted in clinical note text from records concerning the condition. A variety of machine learning models combined with different features and text processingare tested against training data that mentions the term and test data that is coded for the condition from small datasets from the Medical College of Wisconsin. This research showed that no tested configurations performed exceptionally well, nor combinations of features, based on the F1 score. Still, some models did show promise, especially those classifying with a support vector machine, as well as other classifiers such as decision trees, gradient boosting and logistic regression. Based on this initial research, while some of the ideas and approaches here did not perform great on the data studied, they provide many some insight and paths forward to extend them and apply them on larger and more precise datasets

Nurse-Driven Protocols for Abdominal Pain in the Emergency Department

Practice Problem: Emergency department (ED) crowding hinders the opportunity to deliver safe, quality care to abdominal pain patients and detrimentally affects clinical outcomes. Leadership of a rural community ED recognized a comparable issue by introducing a nurse-driven protocol (NDP) to reduce patient length of stay (LOS) and the rate of patients who leave the department prior to physician evaluation. PICOT: The PICOT question that guided this project was: For adult patients in an emergency department, how does a nurse-driven protocol for abdominal pain compared to no protocol use affect the LOS and left without being seen (LWBS) rate over 10 weeks? Evidence: Fourteen studies were identified and supported evidence of effective NDP use for reducing the LOS and LWBS rate amongst abdominal pain patients. Improved clinical outcomes, enhanced operational efficiencies, increased patient and staff satisfaction, and NDP utility in multiple disease states were themes recognized in the literature. Intervention: The evidence-based NDP empowered ED nurses to obtain laboratory diagnostic data and implement nursing interventions within a facility-approved protocol designed to improve throughput by decreasing the time from patient presentation to obtaining medical disposition. Outcome: A pre and post-implementation design found a clinically significant mean reduction of 28-minutes in LOS with the use of the NDP. Overall LWBS was reduced from 5.2 to 2.3 percent and found to be statistically significant. Conclusion: Implementation of an ED abdominal pain NDP was effective in decreasing ED LOS and LWBS. Emergency nurses reported a sense of empowerment with the use of the NDP