Gender parity and homophily in the Drug and Alcohol Dependence editorial process.

Background: Despite efforts towards gender parity and some improvement over time, gender bias in peer review remains a pervasive issue. We examined gender representation and homophily in the peer review process for Drug and Alcohol Dependence (DAD).

Methods: We extracted data for papers submitted to DAD between 2004 and 2019, inclusive.

ChEMU 2020: Natural Language Processing Methods Are Effective for Information Extraction From Chemical Patents.

Chemical patents represent a valuable source of information about new chemical compounds, which is critical to the drug discovery process. Automated information extraction over chemical patents is, however, a challenging task due to the large volume of existing patents and the complex linguistic properties of chemical patents. The Cheminformatics Elsevier Melbourne University (ChEMU) evaluation lab 2020, part of the Conference and Labs of the Evaluation Forum 2020 (CLEF2020), was introduced to support the development of advanced text mining techniques for chemical patents.

Evaluation of a Pharmacist and Nurse Practitioner Smoking Cessation Program.

Purpose: To evaluate the efficacy of a smoking cessation program led by a pharmacist and a nurse practitioner.

Methods: During a 6-month period, patients attended 7 one-on-one face-to-face smoking cessation counseling sessions with a pharmacist and 1 to 2 one-on-one face-to-face smoking cessation counseling sessions with a nurse practitioner. The primary outcome was smoking cessation point prevalence rates at months 1, 3, and 5 post-quit date.

Chemical entity recognition in patents by combining dictionary-based and statistical approaches.

We describe the development of a chemical entity recognition system and its application in the CHEMDNER-patent track of BioCreative 2015. This community challenge includes a Chemical Entity Mention in Patents (CEMP) recognition task and a Chemical Passage Detection (CPD) classification task. We addressed both tasks by an ensemble system that combines a dictionary-based approach with a statistical one.

ContextD: an algorithm to identify contextual properties of medical terms in a Dutch clinical corpus.

Background: In order to extract meaningful information from electronic medical records, such as signs and symptoms, diagnoses, and treatments, it is important to take into account the contextual properties of the identified information: negation, temporality, and experiencer. Most work on automatic identification of these contextual properties has been done on English clinical text. This study presents ContextD, an adaptation of the English ConText algorithm to the Dutch language, and a Dutch clinical corpus.

Knowledge-based extraction of adverse drug events from biomedical text.

Background: Many biomedical relation extraction systems are machine-learning based and have to be trained on large annotated corpora that are expensive and cumbersome to construct. We developed a knowledge-based relation extraction system that requires minimal training data, and applied the system for the extraction of adverse drug events from biomedical text. The system consists of a concept recognition module that identifies drugs and adverse effects in sentences, and a knowledge-base module that establishes whether a relation exists between the recognized concepts.

Automatic generation of case-detection algorithms to identify children with asthma from large electronic health record databases.

Purpose: Most electronic health record databases contain unstructured free-text narratives, which cannot be easily analyzed. Case-detection algorithms are usually created manually and often rely only on using coded information such as International Classification of Diseases version 9 codes. We applied a machine-learning approach to generate and evaluate an automated case-detection algorithm that uses both free-text and coded information to identify asthma cases.

Improving sensitivity of machine learning methods for automated case identification from free-text electronic medical records.

Background: Distinguishing cases from non-cases in free-text electronic medical records is an important initial step in observational epidemiological studies, but manual record validation is time-consuming and cumbersome. We compared different approaches to develop an automatic case identification system with high sensitivity to assist manual annotators.

Methods: We used four different machine-learning algorithms to build case identification systems for two data sets, one comprising hepatobiliary disease patients, the other acute renal failure patients.

Using rule-based natural language processing to improve disease normalization in biomedical text.

Background And Objective: In order for computers to extract useful information from unstructured text, a concept normalization system is needed to link relevant concepts in a text to sources that contain further information about the concept. Popular concept normalization tools in the biomedical field are dictionary-based. In this study we investigate the usefulness of natural language processing (NLP) as an adjunct to dictionary-based concept normalization.

Using an ensemble system to improve concept extraction from clinical records.

Recognition of medical concepts is a basic step in information extraction from clinical records. We wished to improve on the performance of a variety of concept recognition systems by combining their individual results. We selected two dictionary-based systems and five statistical-based systems that were trained to annotate medical problems, tests, and treatments in clinical records.