In International journal of medical informatics ; h5-index 49.0

BACKGROUND : Secondary use of Electronic Health Records (EHRs) has mostly focused on health conditions (diseases and drugs). Function is an important health indicator in addition to morbidity and mortality. Nevertheless, function has been overlooked in accessing patients' health status. The World Health Organization (WHO)'s International Classification of Functioning, Disability and Health (ICF) is considered the international standard for describing and coding function and health states. We pioneer the first comprehensive analysis and identification of functioning concepts in the Mobility domain of the ICF.

RESULTS : Using physical therapy notes at the National Institutes of Health's Clinical Center, we induced a hierarchical order of mobility-related entities including 5 entities types, 3 relations, 8 attributes, and 33 attribute values. Two domain experts manually curated a gold standard corpus of 14,281 nested entity mentions from 400 clinical notes. Inter-annotator agreement (IAA) of exact matching averaged 92.3 % F1-score on mention text spans, and 96.6 % Cohen's kappa on attributes assignments. A high-performance Ensemble machine learning model for named entity recognition (NER) was trained and evaluated using the gold standard corpus. Average F1-score on exact entity matching of our Ensemble method (84.90 %) outperformed popular NER methods: Conditional Random Field (80.4 %), Recurrent Neural Network (81.82 %), and Bidirectional Encoder Representations from Transformers (82.33 %).

CONCLUSIONS : The results of this study show that mobility functioning information can be reliably captured from clinical notes once adequate resources are provided for sequence labeling methods. We expect that functioning concepts in other domains of the ICF can be identified in similar fashion.

Thieu Thanh, Maldonado Jonathan Camacho, Ho Pei-Shu, Ding Min, Marr Alex, Brandt Diane, Newman-Griffis Denis, Zirikly Ayah, Chan Leighton, Rasch Elizabeth

2020-Dec-24

Clinical notes, Functioning information, Mobility, Named entity recognition, Natural language processing, Text mining

In Journal of affective disorders ; h5-index 79.0

BACKGROUND : The status of melancholia as a categorical or dimensional condition remains unclear, and no measure of melancholia has achieved definitive status. This study aimed to use a machine learning approach to assess whether a pre-established cut-off score on the Sydney Melancholia Prototype Index (SMPI) provided clear differentiation of melancholic/non-melancholic depression, and to identify the items making the most distinct contribution.

METHODS : We analysed amalgamated data sets of 1513 clinically depressed patients assessed via the clinician-rated version of the SMPI (SMPI-CR). We also evaluated the self-report version of the SMPI (SMPI-SR) in a combined clinical/community sample of 2025 depressed patients and senior high school students. Rule ensembles were derived in which the outcome measure was the presence/absence of melancholia (defined as scoring at or above a SMPI cut-off score that had been established in previous studies) and the predictive variables were the individual SMPI items.

RESULTS : The pre-established SMPI cut-off score was confirmed as differentiating melancholic/non-melancholic with near perfect accuracy for the SMPI-CR, and with very high accuracy for the SMPI-SR. The relative importance of all SMPI items was quantified.

LIMITATIONS : It is difficult to validate SMPI-assigned diagnoses due to the lack of any similar measures.

CONCLUSIONS : The SMPI-CR was confirmed to be a highly precise instrument for differentiating melancholic and non-melancholic depression. Its use will advance clinical decision making and studies evaluating causes, mechanisms and treatments for the two depressive sub-types, as well as assist clarification as to whether melancholia is categorically or dimensionally distinct from non-melancholic depression.

Parker Gordon, Spoelma Michael J

2020-Dec-29

Categorical versus spectrum models, Depressive disorders, Diagnosis, Machine learning, Melancholia, Psychiatric classification

In Journal of affective disorders ; h5-index 79.0

BACKGROUND : Recent studies have demonstrated that passive smartphone and wearable sensor data collected throughout daily life can predict anxiety symptoms cross-sectionally. However, to date, no research has demonstrated the capacity for these digital biomarkers to predict long-term prognosis.

METHODS : We utilized deep learning models based on wearable sensor technology to predict long-term (17-18-year) deterioration in generalized anxiety disorder and panic disorder symptoms from actigraphy data on daytime movement and nighttime sleeping patterns. As part of Midlife in the United States (MIDUS), a national longitudinal study of health and well-being, subjects (N = 265) (i) completed a phone-based interview that assessed generalized anxiety disorder and panic disorder symptoms at enrollment, (ii) participated in a one-week actigraphy study 9-14 years later, and (iii) completed a long-term follow-up, phone-based interview to quantify generalized anxiety disorder and panic disorder symptoms 17-18 years from initial enrollment. A deep auto-encoder paired with a multi-layered ensemble deep learning model was leveraged to predict whether participants experienced increased anxiety disorder symptoms across this 17-18 year period.

RESULTS : Out-of-sample cross-validated results suggested that wearable movement data could significantly predict which individuals would experience symptom deterioration (AUC = 0.696, CI [0.598, 0.793], 84.6% sensitivity, 52.7% specificity, balanced accuracy = 68.7%).

CONCLUSIONS : Passive wearable actigraphy data could be utilized to predict long-term deterioration of anxiety disorder symptoms. Future studies should examine whether these methods could be implemented to prevent deterioration of anxiety disorder symptoms.

Jacobson Nicholas C, Lekkas Damien, Huang Raphael, Thomas Natalie

2020-Dec-27

anxiety disorders, artficial intelligence, deep learning, digital phenotyping, passive sensing, wearable movement

In Neural networks : the official journal of the International Neural Network Society

Zhang Kaishuo, Robinson Neethu, Lee Seong-Whan, Guan Cuntai

2020-Dec-23

Brain–computer interface (BCI), Convolutional Neural Network (CNN), Electroencephalography (EEG), Transfer learning

In Microbial pathogenesis

Krishnan G Sunil, Joshi Amit, Akhtar Nahid, Kaushik Vikas

2021-Jan-02

Dengue, Docking, Epitope, Population coverage, Simulation, Vaccine

In International journal of cardiology ; h5-index 68.0

OBJECTIVE : To validate a novel artificial-intelligence electrocardiogram algorithm (AI-ECG) to detect left ventricular systolic dysfunction (LVSD) in an external population.

BACKGROUND : LVSD, even when asymptomatic, confers increased morbidity and mortality. We recently derived AI-ECG to detect LVSD using ECGs based on a large sample of patients treated at the Mayo Clinic.

METHODS : We performed an external validation study with subjects from the Know Your Heart Study, a cross-sectional study of adults aged 35-69 years residing in two cities in Russia, who had undergone both ECG and transthoracic echocardiography. LVSD was defined as left ventricular ejection fraction ≤ 35%. We assessed the performance of the AI-ECG to identify LVSD in this distinct patient population.

RESULTS : Among 4277 subjects in this external population-based validation study, 0.6% had LVSD (compared to 7.8% of the original clinical derivation study). The overall performance of the AI-ECG to detect LVSD was robust with an area under the receiver operating curve of 0.82. When using the LVSD probability cut-off of 0.256 from the original derivation study, the sensitivity, specificity, and accuracy in this population were 26.9%, 97.4%, 97.0%, respectively. Other probability cut-offs were analysed for different sensitivity values.

CONCLUSIONS : The AI-ECG detected LVSD with robust test performance in a population that was very different from that used to develop the algorithm. Population-specific cut-offs may be necessary for clinical implementation. Differences in population characteristics, ECG and echocardiographic data quality may affect test performance.

Attia Itzhak Zachi, Tseng Andrew S, Benavente Ernest Diez, Inojosa Jose Medina, Clark Taane G, Malyutina Sofia, Kapa Suraj, Schirmer Henrik, Kudryavtsev Alexander V, Noseworthy Peter A, Carter Rickey E, Ryabikov Audrey, Perel Pablo, Friedman Paul A, Leon David A, Lopez-Jimenez Francisco

2021-Jan-02

Artificial intelligence, Electrocardiogram, Left ventricular systolic dysfunction, Machine learning