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Stanhope V, Yoo N, Matthews E, Baslock D, Hu Y. The Impact of Collaborative Documentation on Person-Centered Care: Textual Analysis of Clinical Notes. JMIR Med Inform 2024; 12:e52678. [PMID: 39302636 DOI: 10.2196/52678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 06/07/2024] [Accepted: 06/26/2024] [Indexed: 09/22/2024] Open
Abstract
Background Collaborative documentation (CD) is a behavioral health practice involving shared writing of clinic visit notes by providers and consumers. Despite widespread dissemination of CD, research on its effectiveness or impact on person-centered care (PCC) has been limited. Principles of PCC planning, a recovery-based approach to service planning that operationalizes PCC, can inform the measurement of person-centeredness within clinical documentation. Objective This study aims to use the clinical informatics approach of natural language processing (NLP) to examine the impact of CD on person-centeredness in clinic visit notes. Using a dictionary-based approach, this study conducts a textual analysis of clinic notes from a community mental health center before and after staff were trained in CD. Methods This study used visit notes (n=1981) from 10 providers in a community mental health center 6 months before and after training in CD. LIWC-22 was used to assess all notes using the Linguistic Inquiry and Word Count (LIWC) dictionary, which categorizes over 5000 linguistic and psychological words. Twelve LIWC categories were selected and mapped onto PCC planning principles through the consensus of 3 domain experts. The LIWC-22 contextualizer was used to extract sentence fragments from notes corresponding to LIWC categories. Then, fixed-effects modeling was used to identify differences in notes before and after CD training while accounting for nesting within the provider. Results Sentence fragments identified by the contextualizing process illustrated how visit notes demonstrated PCC. The fixed effects analysis found a significant positive shift toward person-centeredness; this was observed in 6 of the selected LIWC categories post CD. Specifically, there was a notable increase in words associated with achievement (β=.774, P<.001), power (β=.831, P<.001), money (β=.204, P<.001), physical health (β=.427, P=.03), while leisure words decreased (β=-.166, P=.002). Conclusions By using a dictionary-based approach, the study identified how CD might influence the integration of PCC principles within clinical notes. Although the results were mixed, the findings highlight the potential effectiveness of CD in enhancing person-centeredness in clinic notes. By leveraging NLP techniques, this research illuminated the value of narrative clinical notes in assessing the quality of care in behavioral health contexts. These findings underscore the promise of NLP for quality assurance in health care settings and emphasize the need for refining algorithms to more accurately measure PCC.
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Affiliation(s)
- Victoria Stanhope
- Silver School of Social Work, New York University, 1 Washington Square N, New York, NY, 10003, United States, 1 3016931203
| | - Nari Yoo
- Silver School of Social Work, New York University, 1 Washington Square N, New York, NY, 10003, United States, 1 3016931203
| | - Elizabeth Matthews
- Graduate School of Service, Fordham University, New York, NY, United States
| | - Daniel Baslock
- School of Social Work, Virginia Commonwealth University, Richmond, VA, United States
| | - Yuanyuan Hu
- School of Social Work, University of Minnesota, St Paul, MN, United States
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2
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Shin D, Kim H, Lee S, Cho Y, Jung W. Using Large Language Models to Detect Depression From User-Generated Diary Text Data as a Novel Approach in Digital Mental Health Screening: Instrument Validation Study. J Med Internet Res 2024; 26:e54617. [PMID: 39292502 DOI: 10.2196/54617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 05/17/2024] [Accepted: 08/11/2024] [Indexed: 09/19/2024] Open
Abstract
BACKGROUND Depressive disorders have substantial global implications, leading to various social consequences, including decreased occupational productivity and a high disability burden. Early detection and intervention for clinically significant depression have gained attention; however, the existing depression screening tools, such as the Center for Epidemiologic Studies Depression Scale, have limitations in objectivity and accuracy. Therefore, researchers are identifying objective indicators of depression, including image analysis, blood biomarkers, and ecological momentary assessments (EMAs). Among EMAs, user-generated text data, particularly from diary writing, have emerged as a clinically significant and analyzable source for detecting or diagnosing depression, leveraging advancements in large language models such as ChatGPT. OBJECTIVE We aimed to detect depression based on user-generated diary text through an emotional diary writing app using a large language model (LLM). We aimed to validate the value of the semistructured diary text data as an EMA data source. METHODS Participants were assessed for depression using the Patient Health Questionnaire and suicide risk was evaluated using the Beck Scale for Suicide Ideation before starting and after completing the 2-week diary writing period. The text data from the daily diaries were also used in the analysis. The performance of leading LLMs, such as ChatGPT with GPT-3.5 and GPT-4, was assessed with and without GPT-3.5 fine-tuning on the training data set. The model performance comparison involved the use of chain-of-thought and zero-shot prompting to analyze the text structure and content. RESULTS We used 428 diaries from 91 participants; GPT-3.5 fine-tuning demonstrated superior performance in depression detection, achieving an accuracy of 0.902 and a specificity of 0.955. However, the balanced accuracy was the highest (0.844) for GPT-3.5 without fine-tuning and prompt techniques; it displayed a recall of 0.929. CONCLUSIONS Both GPT-3.5 and GPT-4.0 demonstrated relatively reasonable performance in recognizing the risk of depression based on diaries. Our findings highlight the potential clinical usefulness of user-generated text data for detecting depression. In addition to measurable indicators, such as step count and physical activity, future research should increasingly emphasize qualitative digital expression.
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Affiliation(s)
- Daun Shin
- Department of Psychiatry, Anam Hospital, Korea University, Seoul, Republic of Korea
- Doctorpresso, Seoul, Republic of Korea
| | | | | | - Younhee Cho
- Doctorpresso, Seoul, Republic of Korea
- Department of Design, Seoul National University, Seoul, Republic of Korea
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3
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Reutens S, Dandolo C, Looi RCH, Karystianis GC, Looi JCL. The uses and misuses of artificial intelligence in psychiatry: Promises and challenges. Australas Psychiatry 2024:10398562241280348. [PMID: 39222479 DOI: 10.1177/10398562241280348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Affiliation(s)
- Sharon Reutens
- School of Population Health, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia; and
- Consortium of Australian-Academic Psychiatrists for Independent Policy and Research Analysis (CAPIPRA), Canberra, ACT, Australia
| | - Christopher Dandolo
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
| | | | - George C Karystianis
- School of Population Health, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
| | - Jeffrey C L Looi
- Consortium of Australian-Academic Psychiatrists for Independent Policy and Research Analysis (CAPIPRA), Canberra, ACT, Australia; and
- Academic Unit of Psychiatry and Addiction Medicine, School of Medicine and Psychology, The Australian National University, Canberra Hospital, Canberra, ACT, Australia
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Barruel D, Hilbey J, Charlet J, Chaumette B, Krebs MO, Dauriac-Le Masson V. Predicting treatment resistance in schizophrenia patients: Machine learning highlights the role of early pathophysiologic features. Schizophr Res 2024; 270:1-10. [PMID: 38823319 DOI: 10.1016/j.schres.2024.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 05/10/2024] [Accepted: 05/13/2024] [Indexed: 06/03/2024]
Abstract
Detecting patients with a high-risk profile for treatment-resistant schizophrenia (TRS) can be beneficial for implementing individually adapted therapeutic strategies and better understanding the TRS etiology. The aim of this study was to explore, with machine learning methods, the impact of demographic and clinical patient characteristics on TRS prediction, for already established risk factors and unexplored ones. This was a retrospective study of 500 patients admitted during 2020 to the University Hospital Group for Paris Psychiatry. We hypothesized potential TRS risk factors. The selected features were coded into structured variables in a new dataset, by processing patients discharge summaries and medical narratives with natural-language processing methods. We compared three machine learning models (XGBoost, logistic elastic net regression, logistic regression without regularization) for predicting TRS outcome. We analysed feature impact on the models, suggesting the following factors as markers of a high-risk TRS profile: early age at first contact with psychiatry, antipsychotic treatment interruptions due to non-adherence, absence of positive symptoms at baseline, educational problems and adolescence mental disorders in the personal psychiatric history. Specifically, we found a significant association with TRS outcome for age at first contact with psychiatry and medication non-adherence. Our findings on TRS risk factors are consistent with the review of the literature and suggest potential in using early pathophysiologic features for TRS prediction. Results were encouraging with the use of natural-langage processing techniques to leverage raw data provided by discharge summaries, combined with machine leaning models. These findings are a promising step for helping clinicians adapt their guidelines to early detection of TRS.
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Affiliation(s)
- David Barruel
- GHU Paris Psychiatrie et Neurosciences, Hôpital Sainte Anne, 1, rue Cabanis, 75014 Paris, France.
| | - Jacques Hilbey
- Sorbonne Université, Paris, France; Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé, LIMICS, Paris, France
| | - Jean Charlet
- Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé, LIMICS, Paris, France; Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Boris Chaumette
- GHU Paris Psychiatrie et Neurosciences, Hôpital Sainte Anne, 1, rue Cabanis, 75014 Paris, France; Université de Paris, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM, U1266 Paris, France; Department of Psychiatry, McGill University, Montréal, QC, Canada
| | - Marie-Odile Krebs
- GHU Paris Psychiatrie et Neurosciences, Hôpital Sainte Anne, 1, rue Cabanis, 75014 Paris, France; Université de Paris, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM, U1266 Paris, France
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5
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Nunez JJ, Leung B, Ho C, Ng RT, Bates AT. Predicting which patients with cancer will see a psychiatrist or counsellor from their initial oncology consultation document using natural language processing. COMMUNICATIONS MEDICINE 2024; 4:69. [PMID: 38589545 PMCID: PMC11001970 DOI: 10.1038/s43856-024-00495-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Accepted: 03/28/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Patients with cancer often have unmet psychosocial needs. Early detection of who requires referral to a counsellor or psychiatrist may improve their care. This work used natural language processing to predict which patients will see a counsellor or psychiatrist from a patient's initial oncology consultation document. We believe this is the first use of artificial intelligence to predict psychiatric outcomes from non-psychiatric medical documents. METHODS This retrospective prognostic study used data from 47,625 patients at BC Cancer. We analyzed initial oncology consultation documents using traditional and neural language models to predict whether patients would see a counsellor or psychiatrist in the 12 months following their initial oncology consultation. RESULTS Here, we show our best models achieved a balanced accuracy (receiver-operating-characteristic area-under-curve) of 73.1% (0.824) for predicting seeing a psychiatrist, and 71.0% (0.784) for seeing a counsellor. Different words and phrases are important for predicting each outcome. CONCLUSION These results suggest natural language processing can be used to predict psychosocial needs of patients with cancer from their initial oncology consultation document. Future research could extend this work to predict the psychosocial needs of medical patients in other settings.
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Affiliation(s)
- John-Jose Nunez
- BC Cancer, Vancouver, BC, Canada.
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada.
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada.
| | | | | | - Raymond T Ng
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Alan T Bates
- BC Cancer, Vancouver, BC, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
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Yoo N, Matthews E, Baslock D, Stanhope V. Impact of Collaborative Documentation on Completeness and Length of Clinical Notes in Behavioral Health Settings. Psychiatr Serv 2024; 75:186-190. [PMID: 37528697 DOI: 10.1176/appi.ps.20230118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/03/2023]
Abstract
OBJECTIVE This study aimed to examine differences in community mental health visit notes before and after initiation of collaborative documentation, a practice in which clinicians and consumers jointly document clinical encounters. METHODS Using a clinical informatics approach, the authors sampled visit notes (N=1,875) from nine providers in one mental health clinic. The authors compared notes from before and after the implementation of collaborative documentation by using fixed-effects regression models, controlling for therapist-level effects. RESULTS Significant changes in visit note structure were found after the implementation of collaborative documentation. Most sections (N=6 of 10) contained more information (i.e., higher word and character counts) after collaborative documentation implementation, but sections describing a client's feelings were less likely to have any content (OR=0.01, p<0.001). CONCLUSIONS These findings demonstrate that collaborative documentation influences clinical notes, providing much-needed research about a widely adopted practice in community mental health settings.
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Affiliation(s)
- Nari Yoo
- Silver School of Social Work, New York University, New York City (Yoo, Baslock, Stanhope); Graduate School of Social Service, Fordham University, New York City (Matthews)
| | - Elizabeth Matthews
- Silver School of Social Work, New York University, New York City (Yoo, Baslock, Stanhope); Graduate School of Social Service, Fordham University, New York City (Matthews)
| | - Daniel Baslock
- Silver School of Social Work, New York University, New York City (Yoo, Baslock, Stanhope); Graduate School of Social Service, Fordham University, New York City (Matthews)
| | - Victoria Stanhope
- Silver School of Social Work, New York University, New York City (Yoo, Baslock, Stanhope); Graduate School of Social Service, Fordham University, New York City (Matthews)
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Romano MF, Shih LC, Paschalidis IC, Au R, Kolachalama VB. Large Language Models in Neurology Research and Future Practice. Neurology 2023; 101:1058-1067. [PMID: 37816646 PMCID: PMC10752640 DOI: 10.1212/wnl.0000000000207967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 09/06/2023] [Indexed: 10/12/2023] Open
Abstract
Recent advancements in generative artificial intelligence, particularly using large language models (LLMs), are gaining increased public attention. We provide a perspective on the potential of LLMs to analyze enormous amounts of data from medical records and gain insights on specific topics in neurology. In addition, we explore use cases for LLMs, such as early diagnosis, supporting patient and caregivers, and acting as an assistant for clinicians. We point to the potential ethical and technical challenges raised by LLMs, such as concerns about privacy and data security, potential biases in the data for model training, and the need for careful validation of results. Researchers must consider these challenges and take steps to address them to ensure that their work is conducted in a safe and responsible manner. Despite these challenges, LLMs offer promising opportunities for improving care and treatment of various neurologic disorders.
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Affiliation(s)
- Michael F Romano
- From the Department of Medicine (M.F.R., R.A., V.B.K.), Boston University Chobanian & Avedisian School of Medicine, MA; Department of Radiology and Biomedical Imaging (M.F.R.), University of California, San Francisco; Department of Neurology (L.C.S., R.A.), Boston University Chobanian & Avedisian School of Medicine; Department of Electrical and Computer Engineering (I.C.P.), Division of Systems Engineering, and Department of Biomedical Engineering; Faculty of Computing and Data Sciences (I.C.P., V.B.K.), Boston University; Department of Anatomy and Neurobiology (R.A.); The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine; Department of Epidemiology, Boston University School of Public Health; Boston University Alzheimer's Disease Research Center (R.A.); and Department of Computer Science (V.B.K.), Boston University, MA
| | - Ludy C Shih
- From the Department of Medicine (M.F.R., R.A., V.B.K.), Boston University Chobanian & Avedisian School of Medicine, MA; Department of Radiology and Biomedical Imaging (M.F.R.), University of California, San Francisco; Department of Neurology (L.C.S., R.A.), Boston University Chobanian & Avedisian School of Medicine; Department of Electrical and Computer Engineering (I.C.P.), Division of Systems Engineering, and Department of Biomedical Engineering; Faculty of Computing and Data Sciences (I.C.P., V.B.K.), Boston University; Department of Anatomy and Neurobiology (R.A.); The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine; Department of Epidemiology, Boston University School of Public Health; Boston University Alzheimer's Disease Research Center (R.A.); and Department of Computer Science (V.B.K.), Boston University, MA
| | - Ioannis C Paschalidis
- From the Department of Medicine (M.F.R., R.A., V.B.K.), Boston University Chobanian & Avedisian School of Medicine, MA; Department of Radiology and Biomedical Imaging (M.F.R.), University of California, San Francisco; Department of Neurology (L.C.S., R.A.), Boston University Chobanian & Avedisian School of Medicine; Department of Electrical and Computer Engineering (I.C.P.), Division of Systems Engineering, and Department of Biomedical Engineering; Faculty of Computing and Data Sciences (I.C.P., V.B.K.), Boston University; Department of Anatomy and Neurobiology (R.A.); The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine; Department of Epidemiology, Boston University School of Public Health; Boston University Alzheimer's Disease Research Center (R.A.); and Department of Computer Science (V.B.K.), Boston University, MA
| | - Rhoda Au
- From the Department of Medicine (M.F.R., R.A., V.B.K.), Boston University Chobanian & Avedisian School of Medicine, MA; Department of Radiology and Biomedical Imaging (M.F.R.), University of California, San Francisco; Department of Neurology (L.C.S., R.A.), Boston University Chobanian & Avedisian School of Medicine; Department of Electrical and Computer Engineering (I.C.P.), Division of Systems Engineering, and Department of Biomedical Engineering; Faculty of Computing and Data Sciences (I.C.P., V.B.K.), Boston University; Department of Anatomy and Neurobiology (R.A.); The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine; Department of Epidemiology, Boston University School of Public Health; Boston University Alzheimer's Disease Research Center (R.A.); and Department of Computer Science (V.B.K.), Boston University, MA
| | - Vijaya B Kolachalama
- From the Department of Medicine (M.F.R., R.A., V.B.K.), Boston University Chobanian & Avedisian School of Medicine, MA; Department of Radiology and Biomedical Imaging (M.F.R.), University of California, San Francisco; Department of Neurology (L.C.S., R.A.), Boston University Chobanian & Avedisian School of Medicine; Department of Electrical and Computer Engineering (I.C.P.), Division of Systems Engineering, and Department of Biomedical Engineering; Faculty of Computing and Data Sciences (I.C.P., V.B.K.), Boston University; Department of Anatomy and Neurobiology (R.A.); The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine; Department of Epidemiology, Boston University School of Public Health; Boston University Alzheimer's Disease Research Center (R.A.); and Department of Computer Science (V.B.K.), Boston University, MA.
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Chafjiri FMA, Reece L, Voke L, Landschaft A, Clark J, Kimia AA, Loddenkemper T. Natural language processing for identification of refractory status epilepticus in children. Epilepsia 2023; 64:3227-3237. [PMID: 37804085 DOI: 10.1111/epi.17789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 10/03/2023] [Accepted: 10/03/2023] [Indexed: 10/08/2023]
Abstract
OBJECTIVE Pediatric status epilepticus is one of the most frequent pediatric emergencies, with high mortality and morbidity. Utilizing electronic health records (EHRs) permits analysis of care approaches and disease outcomes at a lower cost than prospective research. However, reviewing EHR manually is time intensive. We aimed to compare refractory status epilepticus (rSE) cases identified by human EHR review with a natural language processing (NLP)-assisted rSE screen followed by a manual review. METHODS We used the NLP screening tool Document Review Tool (DrT) to generate regular expressions, trained a bag-of-words NLP classifier on EHRs from 2017 to 2019, and then tested our algorithm on data from February to December 2012. We compared results from manual review to NLP-assisted search followed by manual review. RESULTS Our algorithm identified 1528 notes in the test set. After removing notes pertaining to the same event by DrT, the user reviewed a total number of 400 notes to find patients with rSE. Within these 400 notes, we identified 31 rSE cases, including 12 new cases not found in manual review, and 19 of the 20 previously identified cases. The NLP-assisted model found 31 of 32 cases, with a sensitivity of 96.88% (95% CI = 82%-99.84%), whereas manual review identified 20 of 32 cases, with a sensitivity of 62.5% (95% CI = 43.75%-78.34%). SIGNIFICANCE DrT provided a highly sensitive model compared to human review and an increase in patient identification through EHRs. The use of DrT is a suitable application of NLP for identifying patients with a history of recent rSE, which ultimately contributes to the implementation of monitoring techniques and treatments in near real time.
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Affiliation(s)
- Fatemeh Mohammad Alizadeh Chafjiri
- Department of Neurology, Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Latania Reece
- Department of Neurology, Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Nexamp, Boston, Massachusetts, USA
| | - Lillian Voke
- Department of Neurology, Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Justice Clark
- Department of Neurology, Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Amir A Kimia
- Department of Medicine, Division of Emergency Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Connecticut Children's Hospital, Hartford, Connecticut, USA
| | - Tobias Loddenkemper
- Department of Neurology, Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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Zantvoort K, Scharfenberger J, Boß L, Lehr D, Funk B. Finding the Best Match - a Case Study on the (Text-)Feature and Model Choice in Digital Mental Health Interventions. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2023; 7:447-479. [PMID: 37927375 PMCID: PMC10620349 DOI: 10.1007/s41666-023-00148-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 08/29/2023] [Indexed: 11/07/2023]
Abstract
With the need for psychological help long exceeding the supply, finding ways of scaling, and better allocating mental health support is a necessity. This paper contributes by investigating how to best predict intervention dropout and failure to allow for a need-based adaptation of treatment. We systematically compare the predictive power of different text representation methods (metadata, TF-IDF, sentiment and topic analysis, and word embeddings) in combination with supplementary numerical inputs (socio-demographic, evaluation, and closed-question data). Additionally, we address the research gap of which ML model types - ranging from linear to sophisticated deep learning models - are best suited for different features and outcome variables. To this end, we analyze nearly 16.000 open-text answers from 849 German-speaking users in a Digital Mental Health Intervention (DMHI) for stress. Our research proves that - contrary to previous findings - there is great promise in using neural network approaches on DMHI text data. We propose a task-specific LSTM-based model architecture to tackle the challenge of long input sequences and thereby demonstrate the potential of word embeddings (AUC scores of up to 0.7) for predictions in DMHIs. Despite the relatively small data set, sequential deep learning models, on average, outperform simpler features such as metadata and bag-of-words approaches when predicting dropout. The conclusion is that user-generated text of the first two sessions carries predictive power regarding patients' dropout and intervention failure risk. Furthermore, the match between the sophistication of features and models needs to be closely considered to optimize results, and additional non-text features increase prediction results. Supplementary Information The online version contains supplementary material available at 10.1007/s41666-023-00148-z.
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Affiliation(s)
- Kirsten Zantvoort
- Institute of Information Systems, Leuphana University, Lüneburg, Germany
| | | | - Leif Boß
- Institute of Psychology, Leuphana University, Lüneburg, Germany
| | - Dirk Lehr
- Institute of Psychology, Leuphana University, Lüneburg, Germany
| | - Burkhardt Funk
- Institute of Information Systems, Leuphana University, Lüneburg, Germany
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Niu H, Pan Q, Xu K. Hybrid deep learning models with multi-classification investor sentiment to forecast the prices of China's leading stocks. PLoS One 2023; 18:e0294460. [PMID: 38011183 PMCID: PMC10681238 DOI: 10.1371/journal.pone.0294460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 10/31/2023] [Indexed: 11/29/2023] Open
Abstract
The prediction of stock prices has long been a captivating subject in academic research. This study aims to forecast the prices of prominent stocks in five key industries of the Chinese A-share market by leveraging the synergistic power of deep learning techniques and investor sentiment analysis. To achieve this, a sentiment multi-classification dataset is for the first time constructed for China's stock market, based on four types of sentiments in modern psychology. The significant heterogeneity of sentiment changes in the sectors' leading stock markets is trained and mined using the Bi-LSTM-ATT model. The impact of multi-classification investor sentiment on stock price prediction was analyzed using the CNN-Bi-LSTM-ATT model. It finds that integrating sentiment indicators into the prediction of industry leading stock prices can enhance the accuracy of the model. Drawing upon four fundamental sentiment types derived from modern psychology, our dataset provides a comprehensive framework for analyzing investor sentiment and its impact on forecasting the stock prices of China's A-share market.
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Affiliation(s)
- Hongli Niu
- School of Economics and Management, University of Science and Technology Beijing, Beijing, China
| | - Qiaoying Pan
- School of Economics and Management, University of Science and Technology Beijing, Beijing, China
- Price Monitoring Center of National Development and Reform Commission, Beijing, China
| | - Kunliang Xu
- School of Economics and Management, University of Science and Technology Beijing, Beijing, China
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11
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Crubezy M, Douay C, Michel P, Haesebaert J. Using patient comments from a standardised experience survey to investigate their perceptions and prioritise improvement actions: a thematic and syntactic analysis. BMC Health Serv Res 2023; 23:988. [PMID: 37710317 PMCID: PMC10503051 DOI: 10.1186/s12913-023-09953-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 08/22/2023] [Indexed: 09/16/2023] Open
Abstract
BACKGROUND Although patient experience surveys flourish in many countries with the aim to improve quality of care, questions remain concerning their ability to become effective drivers of change within institutions. The patient comments from the French national patient experience hospital survey were analysed using an innovative structured approach to characterise patient experience and identify field actions for the institutions. METHODS The comments were taken from the two open-ended questions comprised in the patient experience survey of the Hospices Civils de Lyon between 2018 and 2019. The comments analysis methodology consisted in three steps: thematic analysis; syntactic analysis; generation of statistics for the creation of a patient journey and prioritisation of sub-themes. The STROBE statement checklist was followed. RESULTS Over a year, 79.7% of the 7 362 respondents left at least one comment at the end of the survey and were included in the study, for a total of 5 868 surveys and 10 061 comments. These led to the identification of 28 general themes and 184 specific sub-themes. From the patient journey created, 23 sub-themes were prioritised and gathered into four key categories: relationship between patient and staff; environment; surgery and pain management; information and care coordination. For each of them, the actions and expectations formulated by the respondents were described. CONCLUSIONS The analysis of patient comments obtained from a standardised survey allowed to characterise the patient journey using data that describes patient experience, enabling a prioritisation of actions aiming to improve practice and quality of care at the institution, department, and staff level.
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Affiliation(s)
- Marion Crubezy
- Research On Healthcare Performance (RESHAPE), INSERM U1290, Université Claude Bernard Lyon 1, Lyon, France.
- Institut d'études KPAM, Paris, France.
| | | | - Philippe Michel
- Research On Healthcare Performance (RESHAPE), INSERM U1290, Université Claude Bernard Lyon 1, Lyon, France
- Direction Qualité Usagers Et Santé Populationnelle, Hospices Civils de Lyon, Lyon, France
| | - Julie Haesebaert
- Research On Healthcare Performance (RESHAPE), INSERM U1290, Université Claude Bernard Lyon 1, Lyon, France
- Pôle de Santé Publique, Service de Recherche Et Epidémiologie Cliniques, Hospices Civils de Lyon, Lyon, France
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12
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Washington P, Wall DP. A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism. Annu Rev Biomed Data Sci 2023; 6:211-228. [PMID: 37137169 PMCID: PMC11093217 DOI: 10.1146/annurev-biodatasci-020722-125454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Autism spectrum disorder (autism) is a neurodevelopmental delay that affects at least 1 in 44 children. Like many neurological disorder phenotypes, the diagnostic features are observable, can be tracked over time, and can be managed or even eliminated through proper therapy and treatments. However, there are major bottlenecks in the diagnostic, therapeutic, and longitudinal tracking pipelines for autism and related neurodevelopmental delays, creating an opportunity for novel data science solutions to augment and transform existing workflows and provide increased access to services for affected families. Several efforts previously conducted by a multitude of research labs have spawned great progress toward improved digital diagnostics and digital therapies for children with autism. We review the literature on digital health methods for autism behavior quantification and beneficial therapies using data science. We describe both case-control studies and classification systems for digital phenotyping. We then discuss digital diagnostics and therapeutics that integrate machine learning models of autism-related behaviors, including the factors that must be addressed for translational use. Finally, we describe ongoing challenges and potential opportunities for the field of autism data science. Given the heterogeneous nature of autism and the complexities of the relevant behaviors, this review contains insights that are relevant to neurological behavior analysis and digital psychiatry more broadly.
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Affiliation(s)
- Peter Washington
- Department of Information and Computer Sciences, University of Hawai'i at Mānoa, Honolulu, Hawai'i, USA
| | - Dennis P Wall
- Departments of Pediatrics (Systems Medicine), Biomedical Data Science, and Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, USA;
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What users’ musical preference on Twitter reveals about psychological disorders. Inf Process Manag 2023. [DOI: 10.1016/j.ipm.2023.103269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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14
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Crubezy M, Haesebaert J, Geig A, Michel P. [E-Satis : A new method for analysis of Patient-Reported Outcome Measures (PROMs)]. Rev Epidemiol Sante Publique 2023; 71:101839. [PMID: 37120979 DOI: 10.1016/j.respe.2023.101839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 03/13/2023] [Accepted: 03/13/2023] [Indexed: 05/02/2023] Open
Abstract
OBJECTIVE Almost 80% of the patients responding to the nationwide French patient experience and satisfaction survey (e-Satis) provided free text comments. The objective of this article is to describe an innovative methodology for analysis of this qualitative data. METHODOLOGY This methodological approach is based on analysis of qualitative data from the comments (verbatims) of respondents to the e-Satis survey. Analysis of the verbatims consists in three main steps: (i) analysis of the meaning of the words, with constitution of a thematic dictionary through exploratory research without preconceived notions; (ii) analysis of the syntax, i.e., the way in which the ideas are articulated, which will enable calculation of a linguistic indicator of speakers' involvement in their speech; (iii) production of statistics and characterisation of the themes, which will include three indicators: occurrence of the themes, the average satisfaction shown in the respondents' discourse, and the positive and negative involvement with which they express themselves. Given these results, a priority matrix of four categories of action is established: strong points, priority areas, good practices, and weak signals. RESULTS This methodological approach was applied to 5868 e-Satis questionnaires out of a total of 10,061 verbatims by respondents hospitalised at the Hospices Civils de Lyon between 2018 and 2019. The analysis identified 28 major themes with 184 sub-themes. An extract is presented in this article for illustration purposes. DISCUSSION A methodological approach based on analysis of qualitative data will enable transformation of unstructured data (verbatims) into measurable and comparable data. This methodology is structured to overcome the limitations of closed questions; open questions allow respondents to describe their experiences and perceptions in their own words. Moreover, it is a first step toward comparability of results over time with those of other establishments. This approach is unique in France on account of (a) its exploratory thematic research without preconceived notions and (b) its syntactic analysis of verbatims. CONCLUSIONS This verbatim analysis methodology should enable precise and operational characterization of Patient Experience and induce prioritized improvement actions in healthcare institutions.
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Affiliation(s)
- M Crubezy
- Research on Healthcare Performance (RESHAPE), Inserm U1290, Université Claude Bernard Lyon 1, 69008, Lyon, France; Institut d'études KPAM, 75002, Paris, France.
| | - J Haesebaert
- Research on Healthcare Performance (RESHAPE), Inserm U1290, Université Claude Bernard Lyon 1, 69008, Lyon, France; Pôle de santé publique et Service recherche épidémiologie cliniques, Hospices Civils de Lyon, 69002, Lyon, France
| | - A Geig
- Institut d'études KPAM, 75002, Paris, France
| | - P Michel
- Research on Healthcare Performance (RESHAPE), Inserm U1290, Université Claude Bernard Lyon 1, 69008, Lyon, France; Direction Qualité usagers et santé populationnelle, Hospices Civils de Lyon, 69002, Lyon, France
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Gauld C, Pignon B, Fourneret P, Dubertret C, Tebeka S. Comparison of relative areas of interest between major depression disorder and postpartum depression. Prog Neuropsychopharmacol Biol Psychiatry 2023; 121:110671. [PMID: 36341842 DOI: 10.1016/j.pnpbp.2022.110671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 10/11/2022] [Accepted: 10/26/2022] [Indexed: 11/06/2022]
Abstract
INTRODUCTION Postpartum depression (PPD) is defined as a major depressive disorder (MDD) beginning after childbirth. Wide debates aim to better understand PPD's specificities compared with MDD. One of the keys in differentiating PPD from MDD is to systematically study scientific "Areas Of Interest" (AOIs) of these disorders. METHODS In November 2021, we performed an extraction and textual computational analysis of associated terms for PPD and MDD, using the biomedical database PubMed. We performed an undirected lexical network analysis to map the 150 first terms in space. Then, we used an unsupervised machine learning technique to detect word patterns and automatically cluster AOIs with a topic-modeling analysis. RESULTS We identified 30,000 articles of the 554,724 articles for MDD and 15,642 articles for PPD. Four AOIs were detected in the MDD network: mood disorders and their treatments, risk factors, consequences and quality of life, and mental health and comorbidities. Five AOIs were detected in the PPD network: mood disorders and treatments, risk factors, consequences and child health, patient's background, and the challenges of screening. DISCUSSION AND CONCLUSION Limitations are both methodological, in particular due to the qualitative interpretation of AOIs, and are also related to the difficult transferability of these research results to the clinical practice. The partial overlap between AOIs for MDD and for PPD suggest that the latter is a particular form of the former.
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Affiliation(s)
- Christophe Gauld
- Department of Psychopathology of Child and Adolescent Development, Hospices Civils de Lyon, Lyon 1, France; UMR CNRS 8590 IHPST, Sorbonne University, Paris 1, France.
| | - Baptiste Pignon
- Univ Paris-Est-Créteil (UPEC), AP-HP, Hôpitaux Universitaires « H. Mondor », France; DMU IMPACT, INSERM, IMRB, Translational Neuropsychiatry, Fondation FondaMental, F-94010 Creteil, France
| | - Pierre Fourneret
- Department of Psychopathology of Child and Adolescent Development, Hospices Civils de Lyon, Lyon 1, France; Marc Jeannerod Institute of Cognitive Sciences UMR 5229, CNRS & Claude Bernard University, Lyon 1, France
| | - Caroline Dubertret
- Université de Paris, INSERM UMR1266, Institute of Psychiatry and Neurosciences, Team 1, Paris, France; Department of Psychiatry, AP-HP, Louis Mourier Hospital, F-92700 Colombes, France
| | - Sarah Tebeka
- Université de Paris, INSERM UMR1266, Institute of Psychiatry and Neurosciences, Team 1, Paris, France; Department of Psychiatry, AP-HP, Louis Mourier Hospital, F-92700 Colombes, France
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16
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Yew ANJ, Schraagen M, Otte WM, van Diessen E. Transforming epilepsy research: A systematic review on natural language processing applications. Epilepsia 2023; 64:292-305. [PMID: 36462150 PMCID: PMC10108221 DOI: 10.1111/epi.17474] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 11/23/2022] [Accepted: 12/01/2022] [Indexed: 12/05/2022]
Abstract
Despite improved ancillary investigations in epilepsy care, patients' narratives remain indispensable for diagnosing and treatment monitoring. This wealth of information is typically stored in electronic health records and accumulated in medical journals in an unstructured manner, thereby restricting complete utilization in clinical decision-making. To this end, clinical researchers increasing apply natural language processing (NLP)-a branch of artificial intelligence-as it removes ambiguity, derives context, and imbues standardized meaning from free-narrative clinical texts. This systematic review presents an overview of the current NLP applications in epilepsy and discusses the opportunities and drawbacks of NLP alongside its future implications. We searched the PubMed and Embase databases with a "natural language processing" and "epilepsy" query (March 4, 2022) and included original research articles describing the application of NLP techniques for textual analysis in epilepsy. Twenty-six studies were included. Fifty-eight percent of these studies used NLP to classify clinical records into predefined categories, improving patient identification and treatment decisions. Other applications of NLP had structured clinical information retrieval from electronic health records, scientific papers, and online posts of patients. Challenges and opportunities of NLP applications for enhancing epilepsy care and research are discussed. The field could further benefit from NLP by replicating successes in other health care domains, such as NLP-aided quality evaluation for clinical decision-making, outcome prediction, and clinical record summarization.
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Affiliation(s)
- Arister N J Yew
- University College Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Marijn Schraagen
- Department of Information and Computing Sciences, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Willem M Otte
- Department of Child Neurology, Brain Center, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
| | - Eric van Diessen
- Department of Child Neurology, Brain Center, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
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Luo L, You W, DelBello MP, Gong Q, Li F. Recent advances in psychoradiology. Phys Med Biol 2022; 67. [PMID: 36279868 DOI: 10.1088/1361-6560/ac9d1e] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 10/24/2022] [Indexed: 11/24/2022]
Abstract
Abstract
Psychiatry, as a field, lacks objective markers for diagnosis, progression, treatment planning, and prognosis, in part due to difficulties studying the brain in vivo, and diagnoses are based on self-reported symptoms and observation of patient behavior and cognition. Rapid advances in brain imaging techniques allow clinical investigators to noninvasively quantify brain features at the structural, functional, and molecular levels. Psychoradiology is an emerging discipline at the intersection of psychiatry and radiology. Psychoradiology applies medical imaging technologies to psychiatry and promises not only to improve insight into structural and functional brain abnormalities in patients with psychiatric disorders but also to have potential clinical utility. We searched for representative studies related to recent advances in psychoradiology through May 1, 2022, and conducted a selective review of 165 references, including 75 research articles. We summarize the novel dynamic imaging processing methods to model brain networks and present imaging genetics studies that reveal the relationship between various neuroimaging endophenotypes and genetic markers in psychiatric disorders. Furthermore, we survey recent advances in psychoradiology, with a focus on future psychiatric diagnostic approaches with dimensional analysis and a shift from group-level to individualized analysis. Finally, we examine the application of machine learning in psychoradiology studies and the potential of a novel option for brain stimulation treatment based on psychoradiological findings in precision medicine. Here, we provide a summary of recent advances in psychoradiology research, and we hope this review will help guide the practice of psychoradiology in the scientific and clinical fields.
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Cellini P, Pigoni A, Delvecchio G, Moltrasio C, Brambilla P. Machine learning in the prediction of postpartum depression: A review. J Affect Disord 2022; 309:350-357. [PMID: 35460742 DOI: 10.1016/j.jad.2022.04.093] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 03/29/2022] [Accepted: 04/13/2022] [Indexed: 02/06/2023]
Abstract
BACKGROUND Current screening options in the setting of postpartum depression (PPD) are firmly rooted in self-report symptom-based tools. The implementation of the modern machine learning (ML) approaches might, in this context, represent a way to refine patient screening by precisely identifying possible PPD predictors and, subsequently, a population at risk of developing the disease, in an effort to lower its morbidity, mortality and its economic burden. METHODS We performed a bibliographic search on PubMed and Embase looking for studies aimed at the identification of PPD predictors using ML techniques. RESULTS Among the 482 articles retrieved, 11 met the inclusion criteria. The most used algorithm was the support vector machine. Notably, all studies reached an area under the curve above 0.7, ultimately suggesting that the prediction of PPD could be feasible. Variables obtained from sociodemographic and clinical aspects (psychiatric and gynecological factors) seem to be the most reliable. Only three studies employed biological variables, in the form of blood, genetic and epigenetic predictors, while no study employed imaging techniques. LIMITATIONS The literature on PPD prediction via ML techniques is currently scarce, with most studies employing different variables selection and ML algorithms, ultimately reducing the generalizability of the results. CONCLUSIONS The identification of a population at risk of developing PPD might be feasible with current technology and clinical knowledge. Further studies are necessary to clarify how such an approach could be implemented into clinical practice.
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Affiliation(s)
- Paolo Cellini
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Alessandro Pigoni
- Social and Affective Neuroscience Group, MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy; Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Giuseppe Delvecchio
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy.
| | - Chiara Moltrasio
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
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Walsh J, Dwumfour C, Cave J, Griffiths F. Spontaneously generated online patient experience data - how and why is it being used in health research: an umbrella scoping review. BMC Med Res Methodol 2022; 22:139. [PMID: 35562661 PMCID: PMC9106384 DOI: 10.1186/s12874-022-01610-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 04/13/2022] [Indexed: 11/10/2022] Open
Abstract
PURPOSE Social media has led to fundamental changes in the way that people look for and share health related information. There is increasing interest in using this spontaneously generated patient experience data as a data source for health research. The aim was to summarise the state of the art regarding how and why SGOPE data has been used in health research. We determined the sites and platforms used as data sources, the purposes of the studies, the tools and methods being used, and any identified research gaps. METHODS A scoping umbrella review was conducted looking at review papers from 2015 to Jan 2021 that studied the use of SGOPE data for health research. Using keyword searches we identified 1759 papers from which we included 58 relevant studies in our review. RESULTS Data was used from many individual general or health specific platforms, although Twitter was the most widely used data source. The most frequent purposes were surveillance based, tracking infectious disease, adverse event identification and mental health triaging. Despite the developments in machine learning the reviews included lots of small qualitative studies. Most NLP used supervised methods for sentiment analysis and classification. Very early days, methods need development. Methods not being explained. Disciplinary differences - accuracy tweaks vs application. There is little evidence of any work that either compares the results in both methods on the same data set or brings the ideas together. CONCLUSION Tools, methods, and techniques are still at an early stage of development, but strong consensus exists that this data source will become very important to patient centred health research.
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Affiliation(s)
- Julia Walsh
- Warwick Medical School, University of Warwick, Coventry, UK.
| | | | - Jonathan Cave
- Department of Economics, University of Warwick, Coventry, UK
| | - Frances Griffiths
- Warwick Medical School, University of Warwick, Coventry, UK.,Centre for Health Policy, University of the Witwatersrand, Johannesburg, South Africa
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20
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Seo HY, Song GY, Ku JW, Park HY, Myung W, Kim HJ, Baek CH, Lee N, Sohn JH, Yoo HJ, Park JE. Perceived barriers to psychiatric help-seeking in South Korea by age groups: text mining analyses of social media big data. BMC Psychiatry 2022; 22:332. [PMID: 35562709 PMCID: PMC9102713 DOI: 10.1186/s12888-022-03969-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 04/11/2022] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND The psychiatric treatment gap is substantial in Korea, implying barriers in seeking help. OBJECTIVES This study aims to explore barriers of seeing psychiatrists, expressed on the internet by age groups. METHODS A corpus of data was garnered extensively from internet communities, blogs and social network services from 1 January 2016 to 31 July 2019. Among the texts collected, texts containing words linked to psychiatry were selected. Then the corpus was dismantled into words by using natural language processing. Words linked to barriers to seeking help were identified and classified. Then the words from web communities that we were able to identify the age groups were additionally organized by age groups. RESULTS 97,730,360 articles were identified and 6,097,369 were included in the analysis. Words implying the barriers were selected and classified into four groups of structural discrimination, public prejudice, low accessibility, and adverse drug effects. Structural discrimination was the greatest barrier occupying 34%, followed by public prejudice (27.8%), adverse drug effects (18.6%), and cost/low accessibility (16.1%). In the analysis by age groups, structural discrimination caused teenagers (51%), job seekers (64%) and mothers with children (43%) the most concern. In contrast, the public prejudice (49%) was the greatest barriers in the senior group. CONCLUSIONS Although structural discrimination may most contribute to barriers to visiting psychiatrists in Korea, variation by generations may exist. Along with the general attempt to tackle the discrimination, customized approach might be needed.
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Affiliation(s)
- Hwo Yeon Seo
- Division of Public Health and Medical Service, Seoul National University Hospital, Seoul, Korea
| | | | | | - Hye Yoon Park
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Korea
- Department of Psychiatry, Seoul National University College of Medicine, Daehak-ro 103, Chongno-gu, Seoul, 03080, Korea
| | - Woojae Myung
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Hee Jung Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Korea
| | - Chang Hyeon Baek
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Korea
| | - Nami Lee
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Korea
- Department of Psychiatry, Seoul National University College of Medicine, Daehak-ro 103, Chongno-gu, Seoul, 03080, Korea
| | - Jee Hoon Sohn
- Division of Public Health and Medical Service, Seoul National University Hospital, Seoul, Korea
- Department of Psychiatry, Seoul National University College of Medicine, Daehak-ro 103, Chongno-gu, Seoul, 03080, Korea
| | - Hee Jeong Yoo
- Department of Psychiatry, Seoul National University College of Medicine, Daehak-ro 103, Chongno-gu, Seoul, 03080, Korea
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Jee Eun Park
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Korea.
- Department of Psychiatry, Seoul National University College of Medicine, Daehak-ro 103, Chongno-gu, Seoul, 03080, Korea.
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Natural Language Processing and Machine Learning Supporting the Work of a Psychologist and Its Evaluation on the Example of Support for Psychological Diagnosis of Anorexia. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12094702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Objective: This study sought to address the use of computer-aided diagnosis and therapy for anorexia nervosa. This paper presents the means by which the use of natural language processing methods can augment the work of psychologists. Method: We evaluated this method based on its efficacy when diagnosing anorexia nervosa. Using natural language processing and machine learning, we developed methods for analyzing five basic emotions, analyzing a patient’s body perception, and detecting six potential areas of difficulties for computer support of psychological diagnosis of anorexia. We surveyed 43 psychologists to obtain feedback on these tools. Results: We evaluated efficacy in terms of patient relationship, substantive aspects of the diagnosis, and diagnostic procedures. In terms of patient relationship, we found a noticeable decrease in the patient’s resistance and better support in verifying the substantive scope of the diagnostic thesis. Discussion: The presented methods can be a supporting tool for monitoring the diagnostic process and increasing the degree of self-diagnosis and self-reflection by the patient. This tool can increase the accuracy of the diagnostic process by reducing patient resistance. This will increase access to the patient’s psychopathology.
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Wiegersma S, Hidajat M, Schrieken B, Veldkamp B, Olff M. Improving Web-Based Treatment Intake for Multiple Mental and Substance Use Disorders by Text Mining and Machine Learning: Algorithm Development and Validation. JMIR Ment Health 2022; 9:e21111. [PMID: 35404261 PMCID: PMC9039807 DOI: 10.2196/21111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 11/01/2020] [Accepted: 09/28/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Text mining and machine learning are increasingly used in mental health care practice and research, potentially saving time and effort in the diagnosis and monitoring of patients. Previous studies showed that mental disorders can be detected based on text, but they focused on screening for a single predefined disorder instead of multiple disorders simultaneously. OBJECTIVE The aim of this study is to develop a Dutch multi-class text-classification model to screen for a range of mental disorders to refer new patients to the most suitable treatment. METHODS On the basis of textual responses of patients (N=5863) to a questionnaire currently used for intake and referral, a 7-class classifier was developed to distinguish among anxiety, panic, posttraumatic stress, mood, eating, substance use, and somatic symptom disorders. A linear support vector machine was fitted using nested cross-validation grid search. RESULTS The highest classification rate was found for eating disorders (82%). The scores for panic (55%), posttraumatic stress (52%), mood (50%), somatic symptom (50%), anxiety (35%), and substance use disorders (33%) were lower, likely because of overlapping symptoms. The overall classification accuracy (49%) was reasonable for a 7-class classifier. CONCLUSIONS A classification model was developed that could screen text for multiple mental health disorders. The screener resulted in an additional outcome score that may serve as input for a formal diagnostic interview and referral. This may lead to a more efficient and standardized intake process.
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Affiliation(s)
- Sytske Wiegersma
- Department of Research Methodology, Measurement and Data Analysis, University of Twente, Enschede, Netherlands
| | | | | | - Bernard Veldkamp
- Department of Research Methodology, Measurement and Data Analysis, University of Twente, Enschede, Netherlands
| | - Miranda Olff
- Department of Psychiatry, Amsterdam University Medical Centres, location Academic Medical Centre, Amsterdam, Netherlands
- ARQ National Psychotrauma Centre, Diemen, Netherlands
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Tagliazucchi E. Language as a Window Into the Altered State of Consciousness Elicited by Psychedelic Drugs. Front Pharmacol 2022; 13:812227. [PMID: 35392561 PMCID: PMC8980225 DOI: 10.3389/fphar.2022.812227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 03/01/2022] [Indexed: 11/22/2022] Open
Abstract
Psychedelics are drugs capable of eliciting profound alterations in the subjective experience of the users, sometimes with long-lasting consequences. Because of this, psychedelic research tends to focus on human subjects, given their capacity to construct detailed narratives about the contents of their consciousness experiences. In spite of its relevance, the interaction between serotonergic psychedelics and language production is comparatively understudied in the recent literature. This review is focused on two aspects of this interaction: how the acute effects of psychedelic drugs impact on speech organization regardless of its semantic content, and how to characterize the subjective effects of psychedelic drugs by analyzing the semantic content of written retrospective reports. We show that the computational characterization of language production is capable of partially predicting the therapeutic outcome of individual experiences, relate the effects elicited by psychedelics with those associated with other altered states of consciousness, draw comparisons between the psychedelic state and the symptomatology of certain psychiatric disorders, and investigate the neurochemical profile and mechanism of action of different psychedelic drugs. We conclude that researchers studying psychedelics can considerably expand the range of their potential scientific conclusions by analyzing brief interviews obtained before, during and after the acute effects. Finally, we list a series of questions and open problems that should be addressed to further consolidate this approach.
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Affiliation(s)
- Enzo Tagliazucchi
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibanez, Santiago, Chile
- Departamento de Física, Universidad de Buenos Aires and Instituto de Física de Buenos Aires (IFIBA, CONICET), Pabellón I, Ciudad Universitaria (1428), Buenos Aires, Argentina
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Rubeis G. iHealth: The ethics of artificial intelligence and big data in mental healthcare. Internet Interv 2022; 28:100518. [PMID: 35257003 PMCID: PMC8897624 DOI: 10.1016/j.invent.2022.100518] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 01/11/2022] [Accepted: 02/24/2022] [Indexed: 01/13/2023] Open
Abstract
The concept of intelligent health (iHealth) in mental healthcare integrates artificial intelligence (AI) and Big Data analytics. This article is an attempt to outline ethical aspects linked to iHealth by focussing on three crucial elements that have been defined in the literature: self-monitoring, ecological momentary assessment (EMA), and data mining. The material for the analysis was obtained by a database search. Studies and reviews providing outcome data for each of the three elements were analyzed. An ethical framing of the results was conducted that shows the chances and challenges of iHealth. The synergy between self-monitoring, EMA, and data mining might enable the prevention of mental illness, the prediction of its onset, the personalization of treatment, and the participation of patients in the treatment process. Challenges arise when it comes to the autonomy of users, privacy and data security of users, and potential bias.
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Bastiaansen JAJ, Veldhuizen EE, De Schepper K, Scheepers FE. Experiences of Siblings of Children With Neurodevelopmental Disorders: Comparing Qualitative Analysis and Machine Learning to Study Narratives. Front Psychiatry 2022; 13:719598. [PMID: 35573373 PMCID: PMC9096451 DOI: 10.3389/fpsyt.2022.719598] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 04/06/2022] [Indexed: 11/13/2022] Open
Abstract
INTRODUCTION Relatively few studies have focused on the wellbeing, experiences and needs of the siblings of children with a psychiatric diagnosis. However, the studies that have been conducted suggest that the impact of such circumstances on these siblings is significant. Studying narratives of diagnosed children or relatives has proven to be a successful approach to gain insights that could help improve care. Only a few attempts have been made to study narratives in psychiatry utilizing a machine learning approach. METHOD In this current study, 13 narratives of the experiences of siblings of children with a neurodevelopmental disorders were collected through largely unstructured interviews. The interviews were analyzed using the traditional qualitative, hermeneutic phenomenology method as well as latent Dirichlet allocation (LDA), an unsupervised machine learning method clustering words from documents into topics. One aim of this study was to evaluate the experiences of the siblings in order to find leads to improve care and support for these siblings. Furthermore, the outcomes of both analyses were compared to evaluate the role of machine learning in analyzing narratives. RESULTS Qualitative analysis of the interviews led to the formulation of nine main themes: confrontation with conflicts, coping strategies siblings, need for rest and time for myself, need for support and attention from personal circle, wish for normality, influence on personal choices and possibilities for development, doing things together, recommendations and advices, ambivalence and loyalty. Using unsupervised machine learning (LDA) 24 topics were formed that mostly overlapped with the qualitative themes found. Both the qualitative analysis and the LDA analysis detected themes that were unique to the respective analysis. CONCLUSION The present study found that studying narratives of siblings of children with a neurodevelopmental disorder contributes to a better understanding of the subjects' experiences. Siblings cope with ambivalent feelings toward their brother or sister and this emotional conflict often leads to adapted behavior. Several coping strategies are developed to deal with the behavior of their brother or sister like seeking support or ignoring. Devoted support, time and attention from close relatives, especially parents, is needed. The LDA analysis didn't appear useful to distract meaning and context from the narratives, but it was proposed that machine learning could be a valuable and quick addition to the traditional qualitative methods by finding overlooked topics and giving a rudimental overview of topics in narratives.
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Affiliation(s)
- Jort A J Bastiaansen
- Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
| | - Elien E Veldhuizen
- Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
| | - Kees De Schepper
- Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
| | - Floortje E Scheepers
- Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
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Crema C, Attardi G, Sartiano D, Redolfi A. Natural language processing in clinical neuroscience and psychiatry: A review. Front Psychiatry 2022; 13:946387. [PMID: 36186874 PMCID: PMC9515453 DOI: 10.3389/fpsyt.2022.946387] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/22/2022] [Indexed: 11/13/2022] Open
Abstract
Natural language processing (NLP) is rapidly becoming an important topic in the medical community. The ability to automatically analyze any type of medical document could be the key factor to fully exploit the data it contains. Cutting-edge artificial intelligence (AI) architectures, particularly machine learning and deep learning, have begun to be applied to this topic and have yielded promising results. We conducted a literature search for 1,024 papers that used NLP technology in neuroscience and psychiatry from 2010 to early 2022. After a selection process, 115 papers were evaluated. Each publication was classified into one of three categories: information extraction, classification, and data inference. Automated understanding of clinical reports in electronic health records has the potential to improve healthcare delivery. Overall, the performance of NLP applications is high, with an average F1-score and AUC above 85%. We also derived a composite measure in the form of Z-scores to better compare the performance of NLP models and their different classes as a whole. No statistical differences were found in the unbiased comparison. Strong asymmetry between English and non-English models, difficulty in obtaining high-quality annotated data, and train biases causing low generalizability are the main limitations. This review suggests that NLP could be an effective tool to help clinicians gain insights from medical reports, clinical research forms, and more, making NLP an effective tool to improve the quality of healthcare services.
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Affiliation(s)
- Claudio Crema
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Daniele Sartiano
- Istituto di Informatica e Telematica, Consiglio Nazionale delle Ricerche, Pisa, Italy
| | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
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Kung B, Chiang M, Perera G, Pritchard M, Stewart R. Identifying subtypes of depression in clinician-annotated text: a retrospective cohort study. Sci Rep 2021; 11:22426. [PMID: 34789827 PMCID: PMC8599474 DOI: 10.1038/s41598-021-01954-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 11/08/2021] [Indexed: 11/23/2022] Open
Abstract
Current criteria for depression are imprecise and do not accurately characterize its distinct clinical presentations. As a result, its diagnosis lacks clinical utility in both treatment and research settings. Data-driven efforts to refine criteria have typically focused on a limited set of symptoms that do not reflect the disorder's heterogeneity. By contrast, clinicians often write about patients in depth, creating descriptions that may better characterize depression. However, clinical text is not commonly used to this end. Here we show that clinically relevant depressive subtypes can be derived from unstructured electronic health records. Five subtypes were identified amongst 18,314 patients with depression treated at a large mental healthcare provider by using unsupervised machine learning: severe-typical, psychotic, mild-typical, agitated, and anergic-apathetic. Subtypes were used to place patients in groups for validation; groups were found to be associated with future outcomes and characteristics that were consistent with the subtypes. These associations suggest that these categorizations are actionable due to their validity with respect to disease prognosis. Moreover, they were derived with automated techniques that might theoretically be widely implemented, allowing for future analyses in more varied populations and settings. Additional research, especially with respect to treatment response, may prove useful in further evaluation.
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Affiliation(s)
| | | | - Gayan Perera
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Megan Pritchard
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - Robert Stewart
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
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28
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Kesler SR, Henneghan AM, Thurman W, Rao V. Identifying themes for assessing cancer-related cognitive impairment identified by topic modeling and qualitative content analysis of public online comments (Preprint). JMIR Cancer 2021; 8:e34828. [PMID: 35612878 PMCID: PMC9178450 DOI: 10.2196/34828] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 04/28/2022] [Accepted: 05/01/2022] [Indexed: 11/28/2022] Open
Abstract
Background Cancer-related cognitive impairment (CRCI) is a common and significant adverse effect of cancer and its therapies. However, its definition and assessment remain difficult due to limitations of currently available measurement tools. Objective This study aims to evaluate qualitative themes related to the cognitive effects of cancer to help guide development of assessments that are more specific than what is currently available. Methods We applied topic modeling and inductive qualitative content analysis to 145 public online comments related to cognitive effects of cancer. Results Topic modeling revealed 2 latent topics that we interpreted as representing internal and external factors related to cognitive effects. These findings lead us to hypothesize regarding the potential contribution of locus of control to CRCI. Content analysis suggested several major themes including symptoms, emotional/psychological impacts, coping, “chemobrain” is real, change over time, and function. There was some conceptual overlap between the 2 methods regarding internal and external factors related to patient experiences of cognitive effects. Conclusions Our findings indicate that coping mechanisms and locus of control may be important themes to include in assessments of CRCI. Future directions in this field include prospective acquisition of free-text responses to guide development of assessments that are more sensitive and specific to cognitive function in patients with cancer.
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Affiliation(s)
- Shelli R Kesler
- School of Nursing, University of Texas at Austin, Austin, TX, United States
| | - Ashley M Henneghan
- School of Nursing, University of Texas at Austin, Austin, TX, United States
| | - Whitney Thurman
- School of Nursing, University of Texas at Austin, Austin, TX, United States
| | - Vikram Rao
- School of Nursing, University of Texas at Austin, Austin, TX, United States
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Walsh J, Cave J, Griffiths F. Spontaneously Generated Online Patient Experience of Modafinil: A Qualitative and NLP Analysis. Front Digit Health 2021; 3:598431. [PMID: 34713085 PMCID: PMC8521895 DOI: 10.3389/fdgth.2021.598431] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 01/27/2021] [Indexed: 11/16/2022] Open
Abstract
Objective: To compare the findings from a qualitative and a natural language processing (NLP) based analysis of online patient experience posts on patient experience of the effectiveness and impact of the drug Modafinil. Methods: Posts (n = 260) from 5 online social media platforms where posts were publicly available formed the dataset/corpus. Three platforms asked posters to give a numerical rating of Modafinil. Thematic analysis: data was coded and themes generated. Data were categorized into PreModafinil, Acquisition, Dosage, and PostModafinil and compared to identify each poster's own view of whether taking Modafinil was linked to an identifiable outcome. We classified this as positive, mixed, negative, or neutral and compared this with numerical ratings. NLP: Corpus text was speech tagged and keywords and key terms extracted. We identified the following entities: drug names, condition names, symptoms, actions, and side-effects. We searched for simple relationships, collocations, and co-occurrences of entities. To identify causal text, we split the corpus into PreModafinil and PostModafinil and used n-gram analysis. To evaluate sentiment, we calculated the polarity of each post between −1 (negative) and +1 (positive). NLP results were mapped to qualitative results. Results: Posters had used Modafinil for 33 different primary conditions. Eight themes were identified: the reason for taking (condition or symptom), impact of symptoms, acquisition, dosage, side effects, other interventions tried or compared to, effectiveness of Modafinil, and quality of life outcomes. Posters reported perceived effectiveness as follows: 68% positive, 12% mixed, 18% negative. Our classification was consistent with poster ratings. Of the most frequent 100 keywords/keyterms identified by term extraction 88/100 keywords and 84/100 keyterms mapped directly to the eight themes. Seven keyterms indicated negation and temporal states. Sentiment was as follows 72% positive sentiment 4% neutral 24% negative. Matching of sentiment between the qualitative and NLP methods was accurate in 64.2% of posts. If we allow for one category difference matching was accurate in 85% of posts. Conclusions: User generated patient experience is a rich resource for evaluating real world effectiveness, understanding patient perspectives, and identifying research gaps. Both methods successfully identified the entities and topics contained in the posts. In contrast to current evidence, posters with a wide range of other conditions found Modafinil effective. Perceived causality and effectiveness were identified by both methods demonstrating the potential to augment existing knowledge.
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Affiliation(s)
- Julia Walsh
- Warwick Medical School, University of Warwick, Coventry, United Kingdom
| | - Jonathan Cave
- Department of Economics, University of Warwick, Coventry, United Kingdom
| | - Frances Griffiths
- Warwick Medical School, University of Warwick, Coventry, United Kingdom
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Hudon A, Beaudoin M, Phraxayavong K, Dellazizzo L, Potvin S, Dumais A. Use of Automated Thematic Annotations for Small Data Sets in a Psychotherapeutic Context: Systematic Review of Machine Learning Algorithms. JMIR Ment Health 2021; 8:e22651. [PMID: 34677133 PMCID: PMC8571689 DOI: 10.2196/22651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Revised: 10/06/2020] [Accepted: 07/27/2021] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND A growing body of literature has detailed the use of qualitative analyses to measure the therapeutic processes and intrinsic effectiveness of psychotherapies, which yield small databases. Nonetheless, these approaches have several limitations and machine learning algorithms are needed. OBJECTIVE The objective of this study is to conduct a systematic review of the use of machine learning for automated text classification for small data sets in the fields of psychiatry, psychology, and social sciences. This review will identify available algorithms and assess if automated classification of textual entities is comparable to the classification done by human evaluators. METHODS A systematic search was performed in the electronic databases of Medline, Web of Science, PsycNet (PsycINFO), and Google Scholar from their inception dates to 2021. The fields of psychiatry, psychology, and social sciences were selected as they include a vast array of textual entities in the domain of mental health that can be reviewed. Additional records identified through cross-referencing were used to find other studies. RESULTS This literature search identified 5442 articles that were eligible for our study after the removal of duplicates. Following abstract screening, 114 full articles were assessed in their entirety, of which 107 were excluded. The remaining 7 studies were analyzed. Classification algorithms such as naive Bayes, decision tree, and support vector machine classifiers were identified. Support vector machine is the most used algorithm and best performing as per the identified articles. Prediction classification scores for the identified algorithms ranged from 53%-91% for the classification of textual entities in 4-7 categories. In addition, 3 of the 7 studies reported an interjudge agreement statistic; these were consistent with agreement statistics for text classification done by human evaluators. CONCLUSIONS A systematic review of available machine learning algorithms for automated text classification for small data sets in several fields (psychiatry, psychology, and social sciences) was conducted. We compared automated classification with classification done by human evaluators. Our results show that it is possible to automatically classify textual entities of a transcript based solely on small databases. Future studies are nevertheless needed to assess whether such algorithms can be implemented in the context of psychotherapies.
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Affiliation(s)
- Alexandre Hudon
- Centre de recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montréal, QC, Canada
- Department of Psychiatry and Addictology, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
| | - Mélissa Beaudoin
- Centre de recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montréal, QC, Canada
- Department of Psychiatry and Addictology, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
| | | | - Laura Dellazizzo
- Centre de recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montréal, QC, Canada
- Department of Psychiatry and Addictology, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
| | - Stéphane Potvin
- Centre de recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montréal, QC, Canada
- Department of Psychiatry and Addictology, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
| | - Alexandre Dumais
- Centre de recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montréal, QC, Canada
- Department of Psychiatry and Addictology, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
- Services et Recherches Psychiatriques AD, Montréal, QC, Canada
- Institut national de psychiatrie légale Philippe-Pinel, Montréal, QC, Canada
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Ji M, Xie W, Huang R, Qian X. Forecasting the Suitability of Online Mental Health Information for Effective Self-Care Developing Machine Learning Classifiers Using Natural Language Features. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph181910048. [PMID: 34639348 PMCID: PMC8507671 DOI: 10.3390/ijerph181910048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 09/10/2021] [Accepted: 09/16/2021] [Indexed: 11/16/2022]
Abstract
Background: Online mental health information represents important resources for people living with mental health issues. Suitability of mental health information for effective self-care remains understudied, despite the increasing needs for more actionable mental health resources, especially among young people. Objective: We aimed to develop Bayesian machine learning classifiers as data-based decision aids for the assessment of the actionability of credible mental health information for people with mental health issues and diseases. Methods: We collected and classified creditable online health information on mental health issues into generic mental health (GEN) information and patient-specific (PAS) mental health information. GEN and PAS were both patient-oriented health resources developed by health authorities of mental health and public health promotion. GENs were non-classified online health information without indication of targeted readerships; PASs were developed purposefully for specific populations (young, elderly people, pregnant women, and men) as indicated by their website labels. To ensure the generalisability of our model, we chose to develop a sparse Bayesian machine learning classifier using Relevance Vector Machine (RVM). Results: Using optimisation and normalisation techniques, we developed a best-performing classifier through joint optimisation of natural language features and min-max normalisation of feature frequencies. The AUC (0.957), sensitivity (0.900), and specificity (0.953) of the best model were statistically higher (p < 0.05) than other models using parallel optimisation of structural and semantic features with or without feature normalisation. We subsequently evaluated the diagnostic utility of our model in the clinic by comparing its positive (LR+) and negative likelihood ratios (LR−) and 95% confidence intervals (95% C.I.) as we adjusted the probability thresholds with the range of 0.1 and 0.9. We found that the best pair of LR+ (18.031, 95% C.I.: 10.992, 29.577) and LR− (0.100, 95% C.I.: 0.068, 0.148) was found when the probability threshold was set to 0.45 associated with a sensitivity of 0.905 (95%: 0.867, 0.942) and specificity of 0.950 (95% C.I.: 0.925, 0.975). These statistical properties of our model suggested its applicability in the clinic. Conclusion: Our study found that PAS had significant advantage over GEN mental health information regarding information actionability, engagement, and suitability for specific populations with distinct mental health issues. GEN is more suitable for general mental health information acquisition, whereas PAS can effectively engage patients and provide more effective and needed self-care support. The Bayesian machine learning classifier developed provided automatic tools to support decision making in the clinic to identify more actionable resources, effective to support self-care among different populations.
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Affiliation(s)
- Meng Ji
- School of Languages and Cultures, University of Sydney, Sydney 2006, Australia;
- Correspondence:
| | - Wenxiu Xie
- Department of Computer Science, City University of Hong Kong, Hong Kong 518057, China;
| | - Riliu Huang
- School of Languages and Cultures, University of Sydney, Sydney 2006, Australia;
| | - Xiaobo Qian
- School of Computer Science, South China Normal University, Guangzhou 510631, China;
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Grzenda A, Kraguljac NV, McDonald WM, Nemeroff C, Torous J, Alpert JE, Rodriguez CI, Widge AS. Evaluating the Machine Learning Literature: A Primer and User's Guide for Psychiatrists. Am J Psychiatry 2021; 178:715-729. [PMID: 34080891 DOI: 10.1176/appi.ajp.2020.20030250] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Adrienne Grzenda
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, and Olive View-UCLA Medical Center, Sylmar (Grzenda); Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Kraguljac); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald); Department of Psychiatry, University of Texas Dell Medical School, Austin (Nemeroff); Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston (Torous); Department of Psychiatry and Behavioral Sciences, Albert Einstein School of Medicine, Bronx, N.Y. (Alpert); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif., and Veterans Affairs Palo Alto Health Care System, Palo Alto, Calif. (Rodriguez); Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis (Widge)
| | - Nina V Kraguljac
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, and Olive View-UCLA Medical Center, Sylmar (Grzenda); Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Kraguljac); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald); Department of Psychiatry, University of Texas Dell Medical School, Austin (Nemeroff); Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston (Torous); Department of Psychiatry and Behavioral Sciences, Albert Einstein School of Medicine, Bronx, N.Y. (Alpert); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif., and Veterans Affairs Palo Alto Health Care System, Palo Alto, Calif. (Rodriguez); Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis (Widge)
| | - William M McDonald
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, and Olive View-UCLA Medical Center, Sylmar (Grzenda); Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Kraguljac); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald); Department of Psychiatry, University of Texas Dell Medical School, Austin (Nemeroff); Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston (Torous); Department of Psychiatry and Behavioral Sciences, Albert Einstein School of Medicine, Bronx, N.Y. (Alpert); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif., and Veterans Affairs Palo Alto Health Care System, Palo Alto, Calif. (Rodriguez); Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis (Widge)
| | - Charles Nemeroff
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, and Olive View-UCLA Medical Center, Sylmar (Grzenda); Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Kraguljac); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald); Department of Psychiatry, University of Texas Dell Medical School, Austin (Nemeroff); Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston (Torous); Department of Psychiatry and Behavioral Sciences, Albert Einstein School of Medicine, Bronx, N.Y. (Alpert); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif., and Veterans Affairs Palo Alto Health Care System, Palo Alto, Calif. (Rodriguez); Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis (Widge)
| | - John Torous
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, and Olive View-UCLA Medical Center, Sylmar (Grzenda); Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Kraguljac); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald); Department of Psychiatry, University of Texas Dell Medical School, Austin (Nemeroff); Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston (Torous); Department of Psychiatry and Behavioral Sciences, Albert Einstein School of Medicine, Bronx, N.Y. (Alpert); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif., and Veterans Affairs Palo Alto Health Care System, Palo Alto, Calif. (Rodriguez); Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis (Widge)
| | - Jonathan E Alpert
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, and Olive View-UCLA Medical Center, Sylmar (Grzenda); Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Kraguljac); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald); Department of Psychiatry, University of Texas Dell Medical School, Austin (Nemeroff); Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston (Torous); Department of Psychiatry and Behavioral Sciences, Albert Einstein School of Medicine, Bronx, N.Y. (Alpert); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif., and Veterans Affairs Palo Alto Health Care System, Palo Alto, Calif. (Rodriguez); Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis (Widge)
| | - Carolyn I Rodriguez
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, and Olive View-UCLA Medical Center, Sylmar (Grzenda); Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Kraguljac); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald); Department of Psychiatry, University of Texas Dell Medical School, Austin (Nemeroff); Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston (Torous); Department of Psychiatry and Behavioral Sciences, Albert Einstein School of Medicine, Bronx, N.Y. (Alpert); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif., and Veterans Affairs Palo Alto Health Care System, Palo Alto, Calif. (Rodriguez); Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis (Widge)
| | - Alik S Widge
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, and Olive View-UCLA Medical Center, Sylmar (Grzenda); Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Kraguljac); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald); Department of Psychiatry, University of Texas Dell Medical School, Austin (Nemeroff); Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston (Torous); Department of Psychiatry and Behavioral Sciences, Albert Einstein School of Medicine, Bronx, N.Y. (Alpert); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif., and Veterans Affairs Palo Alto Health Care System, Palo Alto, Calif. (Rodriguez); Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis (Widge)
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AlSaieedi A, Salhi A, Tifratene F, Raies AB, Hungler A, Uludag M, Van Neste C, Bajic VB, Gojobori T, Essack M. DES-Tcell is a knowledgebase for exploring immunology-related literature. Sci Rep 2021; 11:14344. [PMID: 34253812 PMCID: PMC8275784 DOI: 10.1038/s41598-021-93809-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 06/24/2021] [Indexed: 12/02/2022] Open
Abstract
T-cells are a subtype of white blood cells circulating throughout the body, searching for infected and abnormal cells. They have multifaceted functions that include scanning for and directly killing cells infected with intracellular pathogens, eradicating abnormal cells, orchestrating immune response by activating and helping other immune cells, memorizing encountered pathogens, and providing long-lasting protection upon recurrent infections. However, T-cells are also involved in immune responses that result in organ transplant rejection, autoimmune diseases, and some allergic diseases. To support T-cell research, we developed the DES-Tcell knowledgebase (KB). This KB incorporates text- and data-mined information that can expedite retrieval and exploration of T-cell relevant information from the large volume of published T-cell-related research. This KB enables exploration of data through concepts from 15 topic-specific dictionaries, including immunology-related genes, mutations, pathogens, and pathways. We developed three case studies using DES-Tcell, one of which validates effective retrieval of known associations by DES-Tcell. The second and third case studies focuses on concepts that are common to Grave’s disease (GD) and Hashimoto’s thyroiditis (HT). Several reports have shown that up to 20% of GD patients treated with antithyroid medication develop HT, thus suggesting a possible conversion or shift from GD to HT disease. DES-Tcell found miR-4442 links to both GD and HT, and that miR-4442 possibly targets the autoimmune disease risk factor CD6, which provides potential new knowledge derived through the use of DES-Tcell. According to our understanding, DES-Tcell is the first KB dedicated to exploring T-cell-relevant information via literature-mining, data-mining, and topic-specific dictionaries.
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Affiliation(s)
- Ahdab AlSaieedi
- Department of Medical Laboratory Technology (MLT), Faculty of Applied Medical Sciences (FAMS), King Abdulaziz University (KAU), Jeddah, 21589-80324, Saudi Arabia
| | - Adil Salhi
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Faroug Tifratene
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Arwa Bin Raies
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Arnaud Hungler
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Mahmut Uludag
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Christophe Van Neste
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Vladimir B Bajic
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Takashi Gojobori
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Magbubah Essack
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
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Withall A, Karystianis G, Duncan D, Hwang YI, Hagos Kidane A, Butler T. Domestic Violence in Residential Care Facilities in New South Wales, Australia: A Text Mining Study. THE GERONTOLOGIST 2021; 62:223-231. [PMID: 34023902 DOI: 10.1093/geront/gnab068] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND AND OBJECTIVES The police are often the first to attend domestic violence events in New South Wales (NSW), Australia, recording related details as structured information (e.g., date of the event, type of incident, premises type) and as text narratives which contain important information (e.g., mental health status, abuse types) for victims and perpetrators. This study examined the characteristics of victims and persons of interest (POIs) suspected and/or charged with perpetrating a domestic violence related crime in residential care facilities. RESEARCH DESIGN AND METHODS The study employed a text mining method that extracted key information from 700 police recorded domestic violence events in NSW residential care facilities. RESULTS Victims were mostly female (65.4%) and older adults (median age 80.3). POIs were predominantly male (67.0%) and were younger than the victims (median age 57.0). While low rates of mental illnesses were recorded (29.1% in victims; 17.4% in POIs), 'dementia' was the most common condition among POIs (55.7%) and victims (73.0%). 'Physical abuse' was the most common abuse type (80.2%) with 'bruising' the most common injury (36.8%). The most common relationship between perpetrator and victim was 'carer' (76.6%). DISCUSSION AND IMPLICATIONS These findings highlight the opportunity provided by police text-based data to provide insights into elder abuse within residential care facilities.
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Affiliation(s)
- Adrienne Withall
- School of Population Health, Faculty of Medicine, University of New South Wales, Kensington, Sydney, New South Wales, Australia
| | - George Karystianis
- School of Population Health, Faculty of Medicine, University of New South Wales, Kensington, Sydney, New South Wales, Australia
| | - Dayna Duncan
- School of Population Health, Faculty of Medicine, University of New South Wales, Kensington, Sydney, New South Wales, Australia
| | - Ye In Hwang
- School of Population Health, Faculty of Medicine, University of New South Wales, Kensington, Sydney, New South Wales, Australia
| | - Amanuel Hagos Kidane
- School of Population Health, Faculty of Medicine, University of New South Wales, Kensington, Sydney, New South Wales, Australia
| | - Tony Butler
- School of Population Health, Faculty of Medicine, University of New South Wales, Kensington, Sydney, New South Wales, Australia
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Turchin A, Florez Builes LF. Using Natural Language Processing to Measure and Improve Quality of Diabetes Care: A Systematic Review. J Diabetes Sci Technol 2021; 15:553-560. [PMID: 33736486 PMCID: PMC8120048 DOI: 10.1177/19322968211000831] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND Real-world evidence research plays an increasingly important role in diabetes care. However, a large fraction of real-world data are "locked" in narrative format. Natural language processing (NLP) technology offers a solution for analysis of narrative electronic data. METHODS We conducted a systematic review of studies of NLP technology focused on diabetes. Articles published prior to June 2020 were included. RESULTS We included 38 studies in the analysis. The majority (24; 63.2%) described only development of NLP tools; the remainder used NLP tools to conduct clinical research. A large fraction (17; 44.7%) of studies focused on identification of patients with diabetes; the rest covered a broad range of subjects that included hypoglycemia, lifestyle counseling, diabetic kidney disease, insulin therapy and others. The mean F1 score for all studies where it was available was 0.882. It tended to be lower (0.817) in studies of more linguistically complex concepts. Seven studies reported findings with potential implications for improving delivery of diabetes care. CONCLUSION Research in NLP technology to study diabetes is growing quickly, although challenges (e.g. in analysis of more linguistically complex concepts) remain. Its potential to deliver evidence on treatment and improving quality of diabetes care is demonstrated by a number of studies. Further growth in this area would be aided by deeper collaboration between developers and end-users of natural language processing tools as well as by broader sharing of the tools themselves and related resources.
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Affiliation(s)
- Alexander Turchin
- Brigham and Women’s Hospital, Boston,
MA, USA
- Alexander Turchin, MD, MS, Brigham and
Women’s Hospital, 221 Longwood Avenue, Boston, MA 02115, USA.
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Khan AH, Abbe A, Falissard B, Carita P, Bachert C, Mullol J, Reaney M, Chao J, Mannent LP, Amin N, Mahajan P, Pirozzi G, Eckert L. Data Mining of Free-Text Responses: An Innovative Approach to Analyzing Patient Perspectives on Treatment for Chronic Rhinosinusitis with Nasal Polyps in a Phase IIa Proof-of-Concept Study for Dupilumab. Patient Prefer Adherence 2021; 15:2577-2586. [PMID: 34848949 PMCID: PMC8611726 DOI: 10.2147/ppa.s320242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 11/05/2021] [Indexed: 11/23/2022] Open
Abstract
PURPOSE Patient perspective is an important and increasingly sought-after complement to clinical assessment. The aim of this study was to transcribe individual patients' experience of treatment in a dupilumab clinical trial through free-text responses with analysis using natural language processing (NLP) to obtain the unique perspective of patients on disease impact and unmet needs with existing treatment to inform future trial design. PATIENTS AND METHODS Patients with chronic rhinosinusitis with nasal polyps (CRSwNP) who were enrolled in a Phase IIa randomized controlled trial comparing dupilumab with placebo (NCT01920893) were invited to complete a self-assessment of treatment (SAT) tool at the end of treatment, asking, "What is your opinion on the treatment you had during the trial? What did you like or dislike about the treatment?" Free-text responses were analyzed for the overall cohort and according to treatment assignment using natural language processing including sentiment scoring. In a mixed-methods approach, quantitative patient-reported outcome (PRO) results were utilized to complement the qualitative analysis of free-text responses. RESULTS Of 60 patients enrolled in the study, 43 (71.6%) completed the SAT and responses from 37 patients were analyzed (placebo, n = 16; dupilumab, n = 21). Word analyses showed that the most common words were "smell," "improve," "staff," "great," "time," and "good." Across the whole cohort, "smell" was the most common symptom-related word. The words "smell" and "experience" were more likely to occur in patients treated with dupilumab. Patients treated with dupilumab also had more positive sentiment in their SAT responses than those who received placebo. The results from this qualitative analysis were reflected in quantitative PRO results. CONCLUSION "Smell" was important to patients with CRSwNP, highlighting its importance as a patient-centric efficacy outcome measure in the context of clinical trials in CRSwNP. TRIAL REGISTRATION ClinicalTrials.gov, NCT01920893. Registered 12 August 2013, https://www.clinicaltrials.gov/ct2/show/NCT01920893.
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Affiliation(s)
- Asif H Khan
- Sanofi, Chilly-Mazarin, France
- Correspondence: Asif H Khan Sanofi, 1 Avenue Pierre Brossolette, Chilly-Mazarin, 91380, FranceTel +33 1 60 49 77 77 Email
| | | | - Bruno Falissard
- Centre de recherche en epidémiologie et santé des populations (CESP), INSERM U1018, Paris, France
| | | | - Claus Bachert
- Upper Airways Research Laboratory, Ghent University, Ghent, Belgium
- CLINTEC, Karolinska Institutet, Stockholm, Sweden
| | - Joaquim Mullol
- Rhinology Unit & Smell Clinic, ENT Department, Hospital Clínic, Universitat de Barcelona; Clinical and Experimental Respiratory Immunoallergy, IDIBAPS; and CIBERES, Barcelona, Catalonia, Spain
| | | | | | | | - Nikhil Amin
- Regeneron Pharmaceuticals, Inc., Tarrytown, NY, USA
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Karystianis G, Adily A, Schofield PW, Wand H, Lukmanjaya W, Buchan I, Nenadic G, Butler T. Surveillance of Domestic Violence Using Text Mining Outputs From Australian Police Records. Front Psychiatry 2021; 12:787792. [PMID: 35222105 PMCID: PMC8863744 DOI: 10.3389/fpsyt.2021.787792] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 12/01/2021] [Indexed: 11/23/2022] Open
Abstract
In Australia, domestic violence reports are mostly based on data from the police, courts, hospitals, and ad hoc surveys. However, gaps exist in reporting information such as victim injuries, mental health status and abuse types. The police record details of domestic violence events as structured information (e.g., gender, postcode, ethnicity), but also in text narratives describing other details such as injuries, substance use, and mental health status. However, the voluminous nature of the narratives has prevented their use for surveillance purposes. We used a validated text mining methodology on 492,393 police-attended domestic violence event narratives from 2005 to 2016 to extract mental health mentions on persons of interest (POIs) (individuals suspected/charged with a domestic violence offense) and victims, abuse types, and victim injuries. A significant increase was observed in events that recorded an injury type (28.3% in 2005 to 35.6% in 2016). The pattern of injury and abuse types differed between male and female victims with male victims more likely to be punched and to experience cuts and bleeding and female victims more likely to be grabbed and pushed and have bruises. The four most common mental illnesses (alcohol abuse, bipolar disorder, depression schizophrenia) were the same in male and female POIs. An increase from 5.0% in 2005 to 24.3% in 2016 was observed in the proportion of events with a reported mental illness with an increase between 2005 and 2016 in depression among female victims. These findings demonstrate that extracting information from police narratives can provide novel insights into domestic violence patterns including confounding factors (e.g., mental illness) and thus enable policy responses to address this significant public health problem.
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Affiliation(s)
- George Karystianis
- School of Population Health, University of New South Wales (NSW), Sydney, NSW, Australia
| | - Armita Adily
- School of Population Health, University of New South Wales (NSW), Sydney, NSW, Australia
| | | | - Handan Wand
- The Kirby Institute, University of New South Wales, Sydney, NSW, Australia
| | - Wilson Lukmanjaya
- School of Computer Science, University of Technology, Sydney, NSW, Australia
| | - Iain Buchan
- Institute of Population Health, University of Liverpool, Liverpool, United Kingdom
| | - Goran Nenadic
- School of Computer Science, University of Manchester, Manchester, United Kingdom
| | - Tony Butler
- School of Population Health, University of New South Wales (NSW), Sydney, NSW, Australia
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Karystianis G, Simpson A, Adily A, Schofield P, Greenberg D, Wand H, Nenadic G, Butler T. Prevalence of Mental Illnesses in Domestic Violence Police Records: Text Mining Study. J Med Internet Res 2020; 22:e23725. [PMID: 33361056 PMCID: PMC7790609 DOI: 10.2196/23725] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 09/17/2020] [Accepted: 11/23/2020] [Indexed: 01/22/2023] Open
Abstract
Background The New South Wales Police Force (NSWPF) records details of significant numbers of domestic violence (DV) events they attend each year as both structured quantitative data and unstructured free text. Accessing information contained in the free text such as the victim’s and persons of interest (POI's) mental health status could be useful in the better management of DV events attended by the police and thus improve health, justice, and social outcomes. Objective The aim of this study is to present the prevalence of extracted mental illness mentions for POIs and victims in police-recorded DV events. Methods We applied a knowledge-driven text mining method to recognize mental illness mentions for victims and POIs from police-recorded DV events. Results In 416,441 police-recorded DV events with single POIs and single victims, we identified 64,587 events (15.51%) with at least one mental illness mention versus 4295 (1.03%) recorded in the structured fixed fields. Two-thirds (67,582/85,880, 78.69%) of mental illnesses were associated with POIs versus 21.30% (18,298/85,880) with victims; depression was the most common condition in both victims (2822/12,589, 22.42%) and POIs (7496/39,269, 19.01%). Mental illnesses were most common among POIs aged 0-14 years (623/1612, 38.65%) and in victims aged over 65 years (1227/22,873, 5.36%). Conclusions A wealth of mental illness information exists within police-recorded DV events that can be extracted using text mining. The results showed mood-related illnesses were the most common in both victims and POIs. Further investigation is required to determine the reliability of the mental illness mentions against sources of diagnostic information.
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Affiliation(s)
- George Karystianis
- School of Population Health, University of New South Wales, Sydney, Australia
| | | | - Armita Adily
- School of Population Health, University of New South Wales, Sydney, Australia
| | - Peter Schofield
- Neuropsychiatry Service, Hunter New England Health, Newcastle, Australia
| | - David Greenberg
- School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Handan Wand
- Kirby Institute, University of New South Wales, Sydney, Australia
| | - Goran Nenadic
- School of Computer Science, University of Manchester, Manchester, United Kingdom
| | - Tony Butler
- School of Population Health, University of New South Wales, Sydney, Australia
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Menadue CB. Pandemics, epidemics, viruses, plagues, and disease: Comparative frequency analysis of a cultural pathology reflected in science fiction magazines from 1926 to 2015. ACTA ACUST UNITED AC 2020; 2:100048. [PMID: 34173491 PMCID: PMC7480741 DOI: 10.1016/j.ssaho.2020.100048] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/13/2020] [Accepted: 07/13/2020] [Indexed: 12/03/2022]
Abstract
Science fiction includes many dystopian narratives, often featuring epidemics, pandemics, plagues, viruses, and disease. As science fiction has grown in popularity and prevalence it appeals to an increasingly broad demographic, is employed in research communication and education, and as a genre it is frequently argued that it reflects contemporary cultural interests and concerns. To identify the relevance of science fiction as an indicator of popular trends relating to the pathologies of disease, a word frequency comparison of selected key words found in the Google Books 2012 English Corpus has been made to a representative corpus of science fiction magazines dating between 1926 and 2015. Selected issues were reviewed to identify concepts, situations, and outcomes that could readily be measured against real-world examples from current and recent pandemics. The findings indicate that science fiction does appear to mirror and magnify contemporary literary trends, and provides potentially revealing correlations to real-world historical events. In this regard, science fiction might be regarded as a form of ‘cultural pathology’ of popular interests related to the spread and impact of disease that may be valuable in gauging the degree to which society is engaged with these topics at any specific time. Science fiction topics tend to reflect real-world historical events. Comparison of English corpus Google Books word frequencies to science fiction. Science fiction investigates social, cultural and psychological concerns. Science fiction content indicates a ‘cultural pathology’ of popular interests.
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Smith Y, Garcia-Torres R, Coughlin SS, Ling J, Marin T, Su S, Young L. Effectiveness of Social Cognitive Theory-Based Interventions for Glycemic Control in Adults With Type 2 Diabetes Mellitus: Protocol for a Systematic Review and Meta-Analysis. JMIR Res Protoc 2020; 9:e17148. [PMID: 32673210 PMCID: PMC7495254 DOI: 10.2196/17148] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 05/27/2020] [Accepted: 06/14/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND For those living with type 2 diabetes mellitus (T2DM), failing to engage in self-management behaviors leads to poor glycemic control. Social cognitive theory (SCT) has been shown to improve health behaviors by altering cognitive processes and increasing an individual's belief in their ability to accomplish a task. OBJECTIVE We aim to present a protocol for a systematic review and meta-analysis to systematically identify, evaluate, and analyze the effect of SCT-based interventions to improve glycemic control in adults with T2DM. METHODS This protocol follows the 2009 Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. Data sources will include PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsychINFO, Cochrane Library, and Web of Science, and data will be reviewed with the use of customized text mining software. Studies examining SCT-based behavioral interventions for adults diagnosed with T2DM in randomized controlled trials located in the outpatient setting will be included. Intervention effectiveness will be compared with routine care. Screening and data collection will be performed in multiple stages with three reviewers as follows: (1) an independent review of titles/abstracts, (2) a full review, and (3) data collection with alternating teams of two reviewers for disputes to be resolved by a third reviewer. Study quality and risk of bias will be assessed by three reviewers using the Cochrane risk of bias tool. Standardized mean differences will be used to describe the intervention effect sizes with regard to self-efficacy and diabetes knowledge. The raw mean difference of HbA1c will be provided in a random effects model and presented in a forest plot. The expected limitations of this study are incomplete data, the need to contact authors, and analysis of various types of glycemic control measures accurately within the same data set. RESULTS This protocol was granted institutional review board exemption on October 7, 2019. PROSPERO registration (ID: CRD42020147105) was received on April 28, 2020. The review began on April 29, 2020. The results of the review will be disseminated through conference presentations, peer-reviewed journals, and meetings. CONCLUSIONS This systematic review will appraise the effectiveness of SCT-based interventions for adults diagnosed with T2DM and provide the most effective interventions for improving health behaviors in these patients. TRIAL REGISTRATION PROSPERO CRD42020147105; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=147105. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/17148.
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Affiliation(s)
- Yvonne Smith
- College of Nursing, Augusta University, Augusta, GA, United States
| | - Rosalia Garcia-Torres
- Family and Consumer Sciences, California State University, Northridge, CA, United States
| | - Steven S Coughlin
- Department of Population Health Sciences, Augusta University, Augusta, GA, United States
| | - Jiying Ling
- College of Nursing, Michigan State University, East Lansing, MI, United States
| | - Terri Marin
- College of Nursing, Augusta University, Augusta, GA, United States
| | - Shaoyong Su
- Georgia Prevention Institute, Augusta University, Augusta, GA, United States
| | - Lufei Young
- College of Nursing, Augusta University, Augusta, GA, United States
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Falissard B, Simpson EL, Guttman-Yassky E, Papp KA, Barbarot S, Gadkari A, Saba G, Gautier L, Abbe A, Eckert L. Qualitative Assessment of Adult Patients' Perception of Atopic Dermatitis Using Natural Language Processing Analysis in a Cross-Sectional Study. Dermatol Ther (Heidelb) 2020; 10:297-305. [PMID: 32006346 PMCID: PMC7090107 DOI: 10.1007/s13555-020-00356-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Indexed: 11/24/2022] Open
Abstract
INTRODUCTION Atopic dermatitis (AD) is an incurable, inflammatory skin disease characterized by skin barrier disruption and immune dysregulation. Although AD is considered a childhood disease, adult onset is possible, presenting with daily sleep disturbance and functional impairment associated with itch, neuropsychiatric issues (anxiety and depression), and reduced health-related quality of life. Although such aspects of adult AD disease burden have been measured through standardized assessments and based on population-level data, the understanding of the disease experienced at the patient level remains poor. This text-mining study assessed the impact of AD on the lives of adult patients as described from an experiential perspective. METHODS Natural language processing (NLP) was applied to qualitative patient response data from two large-scale international cross-sectional surveys conducted in the USA and countries outside of the USA (non-USA; Canada, France, Germany, Italy, Spain, and the UK). Descriptive analysis was conducted on patient responses to an open-ended question on how they felt about their AD and how the disease affected their life. Character length, word count, and stop word (common words) count were evaluated; centrality analysis identified concepts that were most strongly interlinked. RESULTS Patients with AD in all countries were most frequently impacted by itch, pain, and embarrassment across all levels of disease severity. Patients with moderate-to-severe AD were more likely than patients with mild AD to describe sleep disturbances, fatigue, and feelings of depression, anxiety, and a lack of hope that were directly associated with AD. Centrality analysis revealed sleep disturbance was strongly linked with itch. Collectively, these concepts revealed that patients with AD are impacted by both physical and emotional burdens that are intricately connected. CONCLUSIONS Qualitative data from NLP, being more patient-centric than data from clinical standardized measures, provide a more comprehensive view of the burden of AD to inform disease management.
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Affiliation(s)
| | - Eric L Simpson
- Department of Dermatology, Oregon Health and Science University, Portland, OR, USA
| | - Emma Guttman-Yassky
- Department of Dermatology and the Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kim A Papp
- Probity Medical Research, Waterloo, ON, Canada
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DES-ROD: Exploring Literature to Develop New Links between RNA Oxidation and Human Diseases. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2020; 2020:5904315. [PMID: 32308806 PMCID: PMC7142358 DOI: 10.1155/2020/5904315] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 02/21/2020] [Indexed: 12/27/2022]
Abstract
Normal cellular physiology and biochemical processes require undamaged RNA molecules. However, RNAs are frequently subjected to oxidative damage. Overproduction of reactive oxygen species (ROS) leads to RNA oxidation and disturbs redox (oxidation-reduction reaction) homeostasis. When oxidation damage affects RNA carrying protein-coding information, this may result in the synthesis of aberrant proteins as well as a lower efficiency of translation. Both of these, as well as imbalanced redox homeostasis, may lead to numerous human diseases. The number of studies on the effects of RNA oxidative damage in mammals is increasing by year due to the understanding that this oxidation fundamentally leads to numerous human diseases. To enable researchers in this field to explore information relevant to RNA oxidation and effects on human diseases, we developed DES-ROD, an online knowledgebase that contains processed information from 298,603 relevant documents that consist of PubMed abstracts and PubMed Central full-text articles. The system utilizes concepts/terms from 38 curated thematic dictionaries mapped to the analyzed documents. Researchers can explore enriched concepts, as well as enriched pairs of putatively associated concepts. In this way, one can explore mutual relationships between any combinations of two concepts from used dictionaries. Dictionaries cover a wide range of biomedical topics, such as human genes and proteins, pathways, Gene Ontology categories, mutations, noncoding RNAs, enzymes, toxins, metabolites, and diseases. This makes insights into different facets of the effects of RNA oxidation and the control of this process possible. The usefulness of the DES-ROD system is demonstrated by case studies on some known information, as well as potentially novel information involving RNA oxidation and diseases. DES-ROD is the first knowledgebase based on text and data mining that focused on the exploration of RNA oxidation and human diseases.
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Low DM, Bentley KH, Ghosh SS. Automated assessment of psychiatric disorders using speech: A systematic review. Laryngoscope Investig Otolaryngol 2020; 5:96-116. [PMID: 32128436 PMCID: PMC7042657 DOI: 10.1002/lio2.354] [Citation(s) in RCA: 138] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 12/31/2019] [Accepted: 01/17/2020] [Indexed: 12/31/2022] Open
Abstract
OBJECTIVE There are many barriers to accessing mental health assessments including cost and stigma. Even when individuals receive professional care, assessments are intermittent and may be limited partly due to the episodic nature of psychiatric symptoms. Therefore, machine-learning technology using speech samples obtained in the clinic or remotely could one day be a biomarker to improve diagnosis and treatment. To date, reviews have only focused on using acoustic features from speech to detect depression and schizophrenia. Here, we present the first systematic review of studies using speech for automated assessments across a broader range of psychiatric disorders. METHODS We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We included studies from the last 10 years using speech to identify the presence or severity of disorders within the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). For each study, we describe sample size, clinical evaluation method, speech-eliciting tasks, machine learning methodology, performance, and other relevant findings. RESULTS 1395 studies were screened of which 127 studies met the inclusion criteria. The majority of studies were on depression, schizophrenia, and bipolar disorder, and the remaining on post-traumatic stress disorder, anxiety disorders, and eating disorders. 63% of studies built machine learning predictive models, and the remaining 37% performed null-hypothesis testing only. We provide an online database with our search results and synthesize how acoustic features appear in each disorder. CONCLUSION Speech processing technology could aid mental health assessments, but there are many obstacles to overcome, especially the need for comprehensive transdiagnostic and longitudinal studies. Given the diverse types of data sets, feature extraction, computational methodologies, and evaluation criteria, we provide guidelines for both acquiring data and building machine learning models with a focus on testing hypotheses, open science, reproducibility, and generalizability. LEVEL OF EVIDENCE 3a.
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Affiliation(s)
- Daniel M. Low
- Program in Speech and Hearing Bioscience and Technology, Harvard Medical SchoolBostonMassachusetts
- Department of Brain and Cognitive SciencesMITCambridgeMassachusetts
| | - Kate H. Bentley
- Department of PsychiatryMassachusetts General Hospital/Harvard Medical SchoolBostonMassachusetts
- McGovern Institute for Brain Research, MITCambridgeMassachusetts
| | - Satrajit S. Ghosh
- Program in Speech and Hearing Bioscience and Technology, Harvard Medical SchoolBostonMassachusetts
- McGovern Institute for Brain Research, MITCambridgeMassachusetts
- Department of Otolaryngology, Head and Neck SurgeryHarvard Medical SchoolBostonMassachusetts
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Walsh CG, Chaudhry B, Dua P, Goodman KW, Kaplan B, Kavuluru R, Solomonides A, Subbian V. Stigma, biomarkers, and algorithmic bias: recommendations for precision behavioral health with artificial intelligence. JAMIA Open 2020; 3:9-15. [PMID: 32607482 PMCID: PMC7309258 DOI: 10.1093/jamiaopen/ooz054] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 07/29/2019] [Accepted: 10/30/2019] [Indexed: 12/22/2022] Open
Abstract
Effective implementation of artificial intelligence in behavioral healthcare delivery depends on overcoming challenges that are pronounced in this domain. Self and social stigma contribute to under-reported symptoms, and under-coding worsens ascertainment. Health disparities contribute to algorithmic bias. Lack of reliable biological and clinical markers hinders model development, and model explainability challenges impede trust among users. In this perspective, we describe these challenges and discuss design and implementation recommendations to overcome them in intelligent systems for behavioral and mental health.
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Affiliation(s)
- Colin G Walsh
- Biomedical Informatics, Medicine and Psychiatry, Vanderbilt University Medical Center, 2525 West End, Suite 1475, Nashville, TN, USA
| | - Beenish Chaudhry
- School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, Louisiana, USA
| | - Prerna Dua
- Department of Health Informatics and Information Management, Louisiana Tech University, Ruston, Louisiana, USA
| | - Kenneth W Goodman
- Institute for Bioethics and Health Policy, University of Miami, Miller School of Medicine, Miami, Florida, USA
| | - Bonnie Kaplan
- Yale Center for Medical Informatics, Yale Bioethics Center, Yale Information Society, Yale Solomon Center for Health Law & Policy, Yale University, New Haven, Connecticut, USA
| | - Ramakanth Kavuluru
- Division of Biomedical Informatics, Department of Internal Medicine, University of Kentucky, Lexington, Kentucky, USA
| | - Anthony Solomonides
- Outcomes Research and Biomedical Informatics, NorthShore University HealthSystem, Research Institute, Evanston, Illinois, USA
| | - Vignesh Subbian
- Department of Biomedical Engineering, Department of Systems and Industrial Engineering, The University of Arizona, Tucson, Arizona, USA
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Wu CS, Kuo CJ, Su CH, Wang SH, Dai HJ. Using text mining to extract depressive symptoms and to validate the diagnosis of major depressive disorder from electronic health records. J Affect Disord 2020; 260:617-623. [PMID: 31541973 DOI: 10.1016/j.jad.2019.09.044] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2019] [Revised: 07/29/2019] [Accepted: 09/08/2019] [Indexed: 10/26/2022]
Abstract
BACKGROUND Many studies have used Taiwan's National Health Insurance Research database (NHIRD) to conduct psychiatric research. However, the accuracy of the diagnostic codes for psychiatric disorders in NHIRD is not validated, and the symptom profiles are not available either. This study aimed to evaluate the accuracy of diagnostic codes and use text mining to extract symptom profile and functional impairment from electronic health records (EHRs) to overcome the above research limitations. METHODS A total of 500 discharge notes were randomly selected from a medical center's database. Three annotators reviewed the notes to establish gold standards. The accuracy of diagnostic codes for major psychiatric illness was evaluated. Text mining approaches were applied to extract depressive symptoms and function profiles and to identify patients with major depressive disorder. RESULTS The accuracy of the diagnostic code for major depressive disorder, schizophrenia, and dementia was acceptable but that of bipolar disorder and minor depression was less satisfactory. The performance of text mining approach to recognize depressive symptoms is satisfactory; however, the recall for functional impairment is lower resulting in lower F-scores of 0.774-0.753. Using the text mining approach to identify major depressive disorder, the recall was 0.85 but precision was only 0.69. CONCLUSIONS The accuracy of the diagnostic code for major depressive disorder in discharge notes was generally acceptable. This finding supports the utilization of psychiatric diagnoses in claims databases. The application of text mining to EHRs might help in overcoming current limitations in research using claims databases.
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Affiliation(s)
- Chi-Shin Wu
- Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan R.O.C; College of Medicine, National Taiwan University, Taipei, Taiwan R.O.C
| | - Chian-Jue Kuo
- Taipei City Psychiatric Center, Taipei City Hospital, Taipei, Taiwan R.O.C; Department of Psychiatry, School of Medicine, College of Medicine, Taipei Medical University, Taiwan R.O.C
| | - Chu-Hsien Su
- Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan R.O.C
| | - Shi-Heng Wang
- Department of Public Health and Department of Occupational Safety and Health, China Medical University, Taichung, Taiwan R.O.C
| | - Hong-Jie Dai
- Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan R.O.C; School of Post-Baccalaureate Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan R.O.C.
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Smink W, Sools AM, van der Zwaan JM, Wiegersma S, Veldkamp BP, Westerhof GJ. Towards text mining therapeutic change: A systematic review of text-based methods for Therapeutic Change Process Research. PLoS One 2019; 14:e0225703. [PMID: 31805093 PMCID: PMC6894756 DOI: 10.1371/journal.pone.0225703] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Accepted: 11/11/2019] [Indexed: 01/21/2023] Open
Abstract
Therapeutic Change Process Research (TCPR) connects within-therapeutic change processes to outcomes. The labour intensity of qualitative methods limit their use to small scale studies. Automated text-analyses (e.g. text mining) provide means for analysing large scale text patterns. We aimed to provide an overview of the frequently used qualitative text-based TCPR methods and assess the extent to which these methods are reliable and valid, and have potential for automation. We systematically reviewed PsycINFO, Scopus, and Web of Science to identify articles concerning change processes and text or language. We evaluated the reliability and validity based on replicability, the availability of code books, training data and inter-rater reliability, and evaluated the potential for automation based on the example- and rule-based approach. From 318 articles we identified four often used methods: Innovative Moments Coding Scheme, the Narrative Process Coding Scheme, Assimilation of Problematic Experiences Scale, and Conversation Analysis. The reliability and validity of the first three is sufficient to hold promise for automation. While some text features (content, grammar) lend themselves for automation through a rule-based approach, it should be possible to automate higher order constructs (e.g. schemas) when sufficient annotated data for an example-based approach are available.
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Affiliation(s)
- Wouter Smink
- Department of Psychology, Health & Technology, University of Twente, Enschede, Overijssel, The Netherlands
- Department of Research Methodology, Measurement & Data Analysis, University of Twente, Enschede, Overijssel, The Netherlands
| | - Anneke M. Sools
- Department of Research Methodology, Measurement & Data Analysis, University of Twente, Enschede, Overijssel, The Netherlands
| | | | - Sytske Wiegersma
- Department of Research Methodology, Measurement & Data Analysis, University of Twente, Enschede, Overijssel, The Netherlands
| | - Bernard P. Veldkamp
- Department of Research Methodology, Measurement & Data Analysis, University of Twente, Enschede, Overijssel, The Netherlands
| | - Gerben J. Westerhof
- Department of Psychology, Health & Technology, University of Twente, Enschede, Overijssel, The Netherlands
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Spasić I, Owen D, Smith A, Button K. KLOSURE: Closing in on open-ended patient questionnaires with text mining. J Biomed Semantics 2019; 10:24. [PMID: 31711536 PMCID: PMC6849171 DOI: 10.1186/s13326-019-0215-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Background Knee injury and Osteoarthritis Outcome Score (KOOS) is an instrument used to quantify patients’ perceptions about their knee condition and associated problems. It is administered as a 42-item closed-ended questionnaire in which patients are asked to self-assess five outcomes: pain, other symptoms, activities of daily living, sport and recreation activities, and quality of life. We developed KLOG as a 10-item open-ended version of the KOOS questionnaire in an attempt to obtain deeper insight into patients’ opinions including their unmet needs. However, the open–ended nature of the questionnaire incurs analytical overhead associated with the interpretation of responses. The goal of this study was to automate such analysis. We implemented KLOSURE as a system for mining free–text responses to the KLOG questionnaire. It consists of two subsystems, one concerned with feature extraction and the other one concerned with classification of feature vectors. Feature extraction is performed by a set of four modules whose main functionalities are linguistic pre-processing, sentiment analysis, named entity recognition and lexicon lookup respectively. Outputs produced by each module are combined into feature vectors. The structure of feature vectors will vary across the KLOG questions. Finally, Weka, a machine learning workbench, was used for classification of feature vectors. Results The precision of the system varied between 62.8 and 95.3%, whereas the recall varied from 58.3 to 87.6% across the 10 questions. The overall performance in terms of F–measure varied between 59.0 and 91.3% with an average of 74.4% and a standard deviation of 8.8. Conclusions We demonstrated the feasibility of mining open-ended patient questionnaires. By automatically mapping free text answers onto a Likert scale, we can effectively measure the progress of rehabilitation over time. In comparison to traditional closed-ended questionnaires, our approach offers much richer information that can be utilised to support clinical decision making. In conclusion, we demonstrated how text mining can be used to combine the benefits of qualitative and quantitative analysis of patient experiences.
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Affiliation(s)
- Irena Spasić
- School of Computer Science & Informatics, Cardiff University, Cardiff, UK.
| | - David Owen
- School of Computer Science & Informatics, Cardiff University, Cardiff, UK
| | - Andrew Smith
- School of Psychology, Cardiff University, Cardiff, UK
| | - Kate Button
- School of Healthcare Sciences, Cardiff University, Cardiff, UK
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Essack M, Salhi A, Stanimirovic J, Tifratene F, Bin Raies A, Hungler A, Uludag M, Van Neste C, Trpkovic A, Bajic VP, Bajic VB, Isenovic ER. Literature-Based Enrichment Insights into Redox Control of Vascular Biology. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2019; 2019:1769437. [PMID: 31223421 PMCID: PMC6542245 DOI: 10.1155/2019/1769437] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 04/11/2019] [Accepted: 05/02/2019] [Indexed: 02/07/2023]
Abstract
In cellular physiology and signaling, reactive oxygen species (ROS) play one of the most critical roles. ROS overproduction leads to cellular oxidative stress. This may lead to an irrecoverable imbalance of redox (oxidation-reduction reaction) function that deregulates redox homeostasis, which itself could lead to several diseases including neurodegenerative disease, cardiovascular disease, and cancers. In this study, we focus on the redox effects related to vascular systems in mammals. To support research in this domain, we developed an online knowledge base, DES-RedoxVasc, which enables exploration of information contained in the biomedical scientific literature. The DES-RedoxVasc system analyzed 233399 documents consisting of PubMed abstracts and PubMed Central full-text articles related to different aspects of redox biology in vascular systems. It allows researchers to explore enriched concepts from 28 curated thematic dictionaries, as well as literature-derived potential associations of pairs of such enriched concepts, where associations themselves are statistically enriched. For example, the system allows exploration of associations of pathways, diseases, mutations, genes/proteins, miRNAs, long ncRNAs, toxins, drugs, biological processes, molecular functions, etc. that allow for insights about different aspects of redox effects and control of processes related to the vascular system. Moreover, we deliver case studies about some existing or possibly novel knowledge regarding redox of vascular biology demonstrating the usefulness of DES-RedoxVasc. DES-RedoxVasc is the first compiled knowledge base using text mining for the exploration of this topic.
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Affiliation(s)
- Magbubah Essack
- King Abdullah University of Science and Technology, Computational Bioscience Research Center, Thuwal, Saudi Arabia
| | - Adil Salhi
- King Abdullah University of Science and Technology, Computational Bioscience Research Center, Thuwal, Saudi Arabia
| | - Julijana Stanimirovic
- Vinca Institute, University of Belgrade, Laboratory for Molecular Endocrinology and Radiobiology, Belgrade, Serbia
| | - Faroug Tifratene
- King Abdullah University of Science and Technology, Computational Bioscience Research Center, Thuwal, Saudi Arabia
| | - Arwa Bin Raies
- King Abdullah University of Science and Technology, Computational Bioscience Research Center, Thuwal, Saudi Arabia
| | - Arnaud Hungler
- King Abdullah University of Science and Technology, Computational Bioscience Research Center, Thuwal, Saudi Arabia
| | - Mahmut Uludag
- King Abdullah University of Science and Technology, Computational Bioscience Research Center, Thuwal, Saudi Arabia
| | - Christophe Van Neste
- King Abdullah University of Science and Technology, Computational Bioscience Research Center, Thuwal, Saudi Arabia
| | - Andreja Trpkovic
- Vinca Institute, University of Belgrade, Laboratory for Molecular Endocrinology and Radiobiology, Belgrade, Serbia
| | - Vladan P. Bajic
- Vinca Institute, University of Belgrade, Laboratory for Molecular Endocrinology and Radiobiology, Belgrade, Serbia
| | - Vladimir B. Bajic
- King Abdullah University of Science and Technology, Computational Bioscience Research Center, Thuwal, Saudi Arabia
| | - Esma R. Isenovic
- Vinca Institute, University of Belgrade, Laboratory for Molecular Endocrinology and Radiobiology, Belgrade, Serbia
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Kim YM. Discovering major opioid-related research themes over time: A text mining technique. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2019; 2019:751-760. [PMID: 31259032 PMCID: PMC6568063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The recent health crisis concerning opioid overdose has prompted watershed levels of publications. This study explores how opioid-related research themes have changed since 2000, using a text mining technique. The textual data were obtained from PubMed, and the research periods were divided into three periods. While a few topics appear throughout each period, many new health problems emerged as opioid abuse problems magnified. Topics such as HIV, methadone maintenance treatment, and world health organization appear consistently but diminish over time, while topics like injecting drugs, neonatal abstinence syndrome, and public health concerns are rapidly increasing. Recent widespread opioid abuse problems led to new research topics, including prescription drug monitoring programs, veteran's health issue, posttraumatic stress disorder, HCV, opioid-related deaths, and emergency department visits. Examination of scholarly publications reveals that the expanded use of opioids worsened opioid abuse problems and accelerated the emergence of new health problems.
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Sheikhalishahi S, Miotto R, Dudley JT, Lavelli A, Rinaldi F, Osmani V. Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review. JMIR Med Inform 2019; 7:e12239. [PMID: 31066697 PMCID: PMC6528438 DOI: 10.2196/12239] [Citation(s) in RCA: 204] [Impact Index Per Article: 40.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Revised: 03/04/2019] [Accepted: 03/24/2019] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the secondary use of electronic health records (EHRs), where patient data are analyzed to conduct clinical and translational research. Methods based on machine learning to process EHRs are resulting in improved understanding of patient clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, a wealth of clinical histories remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is contingent on the development of natural language processing (NLP) methods to automatically transform clinical text into structured clinical data that can guide clinical decisions and potentially delay or prevent disease onset. OBJECTIVE The goal of the research was to provide a comprehensive overview of the development and uptake of NLP methods applied to free-text clinical notes related to chronic diseases, including the investigation of challenges faced by NLP methodologies in understanding clinical narratives. METHODS Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed and searches were conducted in 5 databases using "clinical notes," "natural language processing," and "chronic disease" and their variations as keywords to maximize coverage of the articles. RESULTS Of the 2652 articles considered, 106 met the inclusion criteria. Review of the included papers resulted in identification of 43 chronic diseases, which were then further classified into 10 disease categories using the International Classification of Diseases, 10th Revision. The majority of studies focused on diseases of the circulatory system (n=38) while endocrine and metabolic diseases were fewest (n=14). This was due to the structure of clinical records related to metabolic diseases, which typically contain much more structured data, compared with medical records for diseases of the circulatory system, which focus more on unstructured data and consequently have seen a stronger focus of NLP. The review has shown that there is a significant increase in the use of machine learning methods compared to rule-based approaches; however, deep learning methods remain emergent (n=3). Consequently, the majority of works focus on classification of disease phenotype with only a handful of papers addressing extraction of comorbidities from the free text or integration of clinical notes with structured data. There is a notable use of relatively simple methods, such as shallow classifiers (or combination with rule-based methods), due to the interpretability of predictions, which still represents a significant issue for more complex methods. Finally, scarcity of publicly available data may also have contributed to insufficient development of more advanced methods, such as extraction of word embeddings from clinical notes. CONCLUSIONS Efforts are still required to improve (1) progression of clinical NLP methods from extraction toward understanding; (2) recognition of relations among entities rather than entities in isolation; (3) temporal extraction to understand past, current, and future clinical events; (4) exploitation of alternative sources of clinical knowledge; and (5) availability of large-scale, de-identified clinical corpora.
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Affiliation(s)
- Seyedmostafa Sheikhalishahi
- eHealth Research Group, Fondazione Bruno Kessler Research Institute, Trento, Italy
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Riccardo Miotto
- Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Joel T Dudley
- Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Alberto Lavelli
- NLP Research Group, Fondazione Bruno Kessler Research Institute, Trento, Italy
| | - Fabio Rinaldi
- Institute of Computational Linguistics, University of Zurich, Zurich, Switzerland
| | - Venet Osmani
- eHealth Research Group, Fondazione Bruno Kessler Research Institute, Trento, Italy
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