Using weak supervision and deep learning to classify clinical notes for identification of current suicidal ideation

Cusick, Marika; Adekkanattu, Prakash; Campion, Thomas R.; Sholle, Evan; Myers, Annie; Banerjee, Samprit; Alexopoulos, George S.; Wang, Yanshan; Pathak, Jyotishman

doi:10.1016/j.jpsychires.2021.01.052

Cited by 50 publications

(36 citation statements)

References 29 publications

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“…SVM had a comparable performance for sleep problem and daytime sleepiness in terms of sensitivity and specificity and couldn't accurately identify positive cases, which resulted in low PPVs and F1 scores. These results are consistent with previous studies 37,38 that machine learning models might not be effective in clinical text classification when the size of the annotated training dataset is small, and the concepts of interest are sparse and infrequent in teh dcouments.…”

Section: Resultssupporting

confidence: 92%

Extraction of Sleep Information from Clinical Notes of Patients with Alzheimer’s Disease Using Natural Language Processing

Mohammad

Sivarajkumar

Viggiano

et al. 2022

Preprint

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Alzheimer’s Disease (AD) is the most common form of dementia in the United States. Sleep is one of the lifestyle-related factors that has been shown critical for optimal cognitive function in old age.. However, there is a lack of research studying the association between sleep and AD incidence. A major bottleneck for conducting such research is that the traditional way to acquire sleep information is time-consuming, inefficient, non-scalable, and limited to patients’ subjective experience. In this study, we developed a rule-based NLP algorithm and machine learning models to automate the extraction of sleep-related concepts, including snoring, napping, sleep problem, bad sleep quality, daytime sleepiness, night wakings, and sleep duration, from the clinical notes of patients diagnosed with AD. We trained and validated the proposed models on the clinical notes retrieved from the University of Pittsburgh of Medical Center (UPMC). The results show that the rule-based NLP algorithm consistently achieved the best performance for all sleep concepts.

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Section: Resultssupporting

confidence: 92%

Extraction of Sleep Information from Clinical Notes of Patients with Alzheimer’s Disease Using Natural Language Processing

Mohammad

Sivarajkumar

Viggiano

et al. 2022

Preprint

Self Cite

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“…As described in Table 1 , investigators have used scientific functions enabled by ARCH tools to support numerous measures of research activity. Driven by clinical use cases, ARCH NLP efforts have supported acquisition of left ventricular ejection fraction, 26 depression severity, 27 suicidal ideation, 28 and race and ethnicity 29 among other elements from progress notes and pathology reports. ARCH infrastructure has also grown support of multi-institutional data sharing initiatives overtime to deliver regular data set updates (eg, quarterly, monthly, weekly) to PCORnet, ACT, N3C, All of Us Research Program, and TriNetX.…”

Section: Resultsmentioning

confidence: 99%

An architecture for research computing in health to support clinical and translational investigators with electronic patient data

Campion

Sholle

Pathak

et al. 2021

Journal of the American Medical Informatics Association

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Objective Obtaining electronic patient data, especially from electronic health record (EHR) systems, for clinical and translational research is difficult. Multiple research informatics systems exist but navigating the numerous applications can be challenging for scientists. This article describes Architecture for Research Computing in Health (ARCH), our institution’s approach for matching investigators with tools and services for obtaining electronic patient data. Materials and Methods Supporting the spectrum of studies from populations to individuals, ARCH delivers a breadth of scientific functions—including but not limited to cohort discovery, electronic data capture, and multi-institutional data sharing—that manifest in specific systems—such as i2b2, REDCap, and PCORnet. Through a consultative process, ARCH staff align investigators with tools with respect to study design, data sources, and cost. Although most ARCH services are available free of charge, advanced engagements require fee for service. Results Since 2016 at Weill Cornell Medicine, ARCH has supported over 1200 unique investigators through more than 4177 consultations. Notably, ARCH infrastructure enabled critical coronavirus disease 2019 response activities for research and patient care. Discussion ARCH has provided a technical, regulatory, financial, and educational framework to support the biomedical research enterprise with electronic patient data. Collaboration among informaticians, biostatisticians, and clinicians has been critical to rapid generation and analysis of EHR data. Conclusion A suite of tools and services, ARCH helps match investigators with informatics systems to reduce time to science. ARCH has facilitated research at Weill Cornell Medicine and may provide a model for informatics and research leaders to support scientists elsewhere.

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“…In clinical NLP, studies use lexical or concept filtering rules to create labelled data to extract nuanced categories (e.g. suicidal ideation [27] or lifestyle factors for Alzheimer's Disease [28]) from clinical texts. We extend over this line of research by using ontologies and a medical concept labelling tool with two specific rules to create reliable weak data to extract rare diseases.…”

Section: Background and Related Workmentioning

confidence: 99%

Ontology-Based and Weakly Supervised Rare Disease Phenotyping from Clinical Notes

Dong¹,

Suárez-Paniagua²,

Zhang³

et al. 2022

Preprint

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Computational text phenotyping is the practice of identifying patients with certain disorders and traits from clinical notes. Rare diseases are challenging to be identified due to few cases available for machine learning and the need for data annotation from domain experts. We propose a method using ontologies and weak supervision, with recent pre-trained contextual representations from Bidirectional Transformers (e.g. BERT). The ontology-based framework includes two steps: (i) Text-to-UMLS, extracting phenotypes by contextually linking mentions to concepts in Unified Medical Language System (UMLS), with a Named Entity Recognition and Linking (NER+L) tool, SemEHR, and weak supervision with customised rules and contextual mention representation; (ii) UMLS-to-ORDO, matching UMLS concepts to rare diseases in Orphanet Rare Disease Ontology (ORDO). The weakly supervised approach is proposed to learn a phenotype confirmation model to improve Text-to-UMLS linking, without annotated data from domain experts. We evaluated the approach on three clinical datasets of discharge summaries and radiology reports from two institutions in the US and the UK. Our best weakly supervised method achieved 81.4% precision and 91.4% recall on extracting rare disease UMLS phenotypes from MIMIC-III discharge summaries. The overall pipeline processing clinical notes can surface rare disease cases, mostly uncaptured in structured data (manually assigned ICD codes). Results on radiology reports from MIMIC-III and NHS Tayside were consistent with the discharge summaries. We discuss the usefulness of the weak supervision approach and propose directions for future studies.

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Using weak supervision and deep learning to classify clinical notes for identification of current suicidal ideation

Cited by 50 publications

References 29 publications

Extraction of Sleep Information from Clinical Notes of Patients with Alzheimer’s Disease Using Natural Language Processing

Extraction of Sleep Information from Clinical Notes of Patients with Alzheimer’s Disease Using Natural Language Processing

An architecture for research computing in health to support clinical and translational investigators with electronic patient data

Ontology-Based and Weakly Supervised Rare Disease Phenotyping from Clinical Notes

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