Treatment approaches and survival outcomes in elderly colorectal cancer patients: a single-center comparative study.

Background: Geriatric patients account for nearly half of new colorectal cancer (CRC) cases. This study compares clinicopathological features, treatments, outcomes, and frailty in elderly (≥ 70) and younger (< 70) CRC patients at our center.

Materials And Methods: Patients diagnosed with non-metastatic or de novo metastatic CRC between January 2015 and April 2024 were included.

Clinical features and prognostic factors in thymoma and thymic carcinoma.

Introduction: Thymic epithelial tumors represent the most common cause of anterior mediastinal masses in adults. Among these tumors, thymomas constitute the majority, whereas thymic carcinomas are less prevalent and associated with a poorer prognosis. This study seeks to investigate the clinical and prognostic factors of these rare entities, thymoma, and thymic carcinomas.

Clinical characteristics and disease course before and after SARS-CoV-2 infection in a large cohort of systemic sclerosis patients.

Background/aim: The objective of this study is to evaluate the clinical presentations and adverse outcomes of Coronavirus Disease 2019 (COVID-19) in patients with systemic sclerosis (SSc) and assess the impact of SSc features on the clinical course of COVID-19.

Materials And Methods: In this multicenter, retrospective study, SSc patients with COVID-19 were included. Clinical features of SSc, along with detailed COVID-19 data, were extracted from medical records and patient interviews.

Reliability and validity of the Turkish version of Scleroderma Skin Patient-Reported Outcome in patients with systemic sclerosis.

Objectives: This study aimed to translate the Scleroderma Skin Patient-Reported Outcome (SSPRO) questionnaire to the Turkish (SSPRO-T) language and to assess its validity and reliability.

Patients And Methods: Fifty-four systemic sclerosis (SSc) patients (51 females, 3 males; mean age: 49.8±10.

Sodium-Glucose Co-transporter Type-2 Inhibitors' Effect on Quality of Life in Older Adult Population.

Introduction The global elderly population is expanding, with chronic conditions like diabetes diminishing their quality of life. Sodium-glucose co-transporter type 2 (SGLT-2) inhibitors hold promise in improving quality of life by addressing hypervolemia, obesity, and lipid irregularities. However, these drugs can lead to adverse effects, such as polyuria, dehydration, and weight loss, which may detrimentally impact older patients.

Semaphorin 3A levels in vascular and nonvascular phenotypes in systemic sclerosis.

Objective: Semaphorin 3A (Sema3A) plays a regulatory role in immune responses. The aim of this study was to evaluate Sema3A levels in patients with systemic sclerosis (SSc), especially in major vascular involvements such as digital ulcer (DU), scleroderma renal crisis (SRC), pulmonary arterial hypertension (PAH), and to compare Sema3A level with SSc disease activity.

Methods: In SSc patients, patients with DU, SRC, or PAH were grouped as major vascular involvements and those without as nonvascular, and Sema3A levels were compared between the groups and with a healthy control group.

A Context-Enhanced De-identification System.

Many modern entity recognition systems, including the current state-of-the-art de-identification systems, are based on bidirectional long short-term memory (biLSTM) units augmented by a conditional random field (CRF) sequence optimizer. These systems process the input sentence by sentence. This approach prevents the systems from capturing dependencies over sentence boundaries and makes accurate sentence boundary detection a prerequisite.

Comparing Attention-Deficit/Hyperactivity Disorder and Oppositional Defiant Disorder Singly and Together in Terms of Behavioral Problems, Family Conflict, and Cognitive Functions.

Objectives: The purpose of this study is to profile three groups of children with attention-deficit/hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), and both ADHD and ODD, through analyzing their cognitive abilities, personality traits, and family characteristics.

Methods: The study included 60 patients, with 20 patients in each group. Patients were selected according to the DSM IV criteria.

Modes of De-identification.

De-identification of protected health information is an essential method for protecting patient privacy. Most institutes require de-identification of patient data prior to conducting scientific studies; therefore, it is important for clinical scientists to be cognizant of all modes of de-identification and all services provided by their de-identification tools. In this article, we discuss eight different modes of de-identification that yield de-identified data at different levels of quality.

Patient Privacy in the Era of Big Data.

Privacy was defined as a fundamental human right in the Universal Declaration of Human Rights at the 1948 United Nations General Assembly. However, there is still no consensus on what constitutes privacy. In this review, we look at the evolution of privacy as a concept from the era of Hippocrates to the era of social media and big data.

Challenges and Insights in Using HIPAA Privacy Rule for Clinical Text Annotation.

The Privacy Rule of Health Insurance Portability and Accountability Act (HIPAA) requires that clinical documents be stripped of personally identifying information before they can be released to researchers and others. We have been manually annotating clinical text since 2008 in order to test and evaluate an algorithmic clinical text de-identification tool, NLM Scrubber, which we have been developing in parallel. Although HIPAA provides some guidance about what must be de-identified, translating those guidelines into practice is not as straightforward, especially when one deals with free text.

Visualization of patient prescription history data in emergency care.

Interpreting patient's medication history from long textual data can be unwieldy especially in emergency care. We developed a real-time software application that converts one-year-long patient prescription history data into a visually appealing and information-rich timeline chart. The chart can be digested by healthcare providers quickly; hence, it could be an invaluable clinical tool when the rapid response time is crucial as in stroke or severe trauma cases.

De-identification of Address, Date, and Alphanumeric Identifiers in Narrative Clinical Reports.

Introduction: The Privacy Rule of Health Insurance Portability and Accountability Act requires that clinical documents be stripped of personally identifying information before they can be released to researchers and others. We have been developing a software application, NLM Scrubber, to de-identify narrative clinical reports.

Methods: We compared NLM Scrubber with MIT's and MITRE's de-identification systems on 3,093 clinical reports about 1,636 patients.

Piloting a deceased subject integrated data repository and protecting privacy of relatives.

Use of deceased subject Electronic Health Records can be an important piloting platform for informatics or biomedical research. Existing legal framework allows such research under less strict de-identification criteria; however, privacy of non-decedent must be protected. We report on creation of the decease subject Integrated Data Repository (dsIDR) at National Institutes of Health, Clinical Center and a pilot methodology to remove secondary protected health information or identifiable information (secondary PxI; information about persons other than the primary patient).

The Challenges of Creating a Gold Standard for De-identification Research.

We created a Gold Standard corpus comprised over 20,000 records of annotated narrative clinical reports for use in the training and evaluation of NLM Scrubber, a de-identification software system for medical records. Our experience with designing the corpus demonstrated the conceptual complexity of the task.

Extracting laboratory test information from biomedical text.

Background: No previous study reported the efficacy of current natural language processing (NLP) methods for extracting laboratory test information from narrative documents. This study investigates the pathology informatics question of how accurately such information can be extracted from text with the current tools and techniques, especially machine learning and symbolic NLP methods. The study data came from a text corpus maintained by the U.

The pattern of name tokens in narrative clinical text and a comparison of five systems for redacting them.

Objective: To understand the factors that influence success in scrubbing personal names from narrative text.

Materials And Methods: We developed a scrubber, the NLM Name Scrubber (NLM-NS), to redact personal names from narrative clinical reports, hand tagged words in a set of gold standard narrative reports as personal names or not, and measured the scrubbing success of NLM-NS and that of four other scrubbing/name recognition tools (MIST, MITdeid, LingPipe, and ANNIE/GATE) against the gold standard reports. We ran three comparisons which used increasingly larger name lists.

Comparison of electronic pharmacy prescription records with manually collected medication histories in an emergency department.

Study Objective: Medication history is an essential part of patient assessment in emergency care. Patient-reported medication history can be incomplete. We study whether an electronic pharmacy-sourced prescription record can supplement the patient-reported history.

ICU Outcome Predictions using Physiologic Trends in the First Two Days.

Aims: This study aims to accurately predict patient mortality in the ICU. Given all physiologic measurements in the first 48 hours of the ICU stay, the Bayesian model of the study predicts outcome with a posterior probability.

Methods: This study modeled the outcome as a binary random variable dependent on trends of daily physiologic measures of the patient, where trends were conditionally independent given the outcome.

Biological entity recognition with conditional random fields.

Due to the rapid evolution of molecular biology and the lack of naming standards, biological entity recognition (BER) remains a challenging task for information extraction and natural language understanding. In this study, we presented a statistical machine learning approach for extracting features, modeling, and predicting biological named entities. Our approach utilizes UMLS semantic types together with MetaMap, SemRep, and ABGene, as well as the conditional random fields (CRF) framework, and learns both the structure and parameters of a statistical model.

Interpretation of data and identification of information are sine qua non for modern digital information services.

Digital information is at the center of the new scientific endeavor and, if managed carefully, it may bridge scientists across disciplines. Scientists, clinicians, and the public need enabling tools to overcome barriers to communication of biomedical information. Biomedical information systems need to (1) interpret queries and the needs of users accurately, (2) identify, evaluate and combine all relevant information among a comprehensive set of sources, and (3) provide users the right information that they seek.

Why do we need probabilistic approaches to ontologies and the associated data?

Conventional ontologies comprise deterministically organized concepts. Certain ontological relations (e.g.

Semi-automatic indexing of full text biomedical articles.

The main application of U.S. National Library of Medicine's Medical Text Indexer (MTI) is to provide indexing recommendations to the Library's indexing staff.