Artificial Intelligence for Unstructured Healthcare Data: Application to Coding of Patient Reporting of Adverse Drug Reactions.
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ABSTRACT:
SUBMITTER: Letinier L
PROVIDER: S-EPMC8359992 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:AIMS:To develop a systematic generic method of enabling patients to report symptoms which they believe to be due to a particular prescribed drug. METHODS:A piloted body system-based questionnaire was distributed to patients registered with 79 medical practices in Grampian prescribed one of nine recently marketed 'black triangle' drugs. These comprised four antidepressants, three antiepileptics and two analgesics. This requested respondents to identify any symptoms experienced over the previous year which they thought could be due to the 'black triangle' drug they had used. A sample of medical records was examined to compare symptoms recorded with those reported by patients. A classification system was developed for the study to enable the assessment of symptoms reported for their potential relationship to patients' drug therapy. All symptoms reported were classified, taking into account information provided by patients on their concomitant drugs and diseases. A specialist pharmacist independently re-classified a sample of the symptoms to validate the process. RESULTS:A 36.3% response rate was obtained (837/2307) with 742 respondents (88.6%) reporting at least one symptom. The median per patient was 6.0 (range 0--71), with almost half (406, 48.5%) reporting fewer than five symptoms. Most symptoms (71.0%) were classified as being probably or possibly related to the drugs studied. Agreement between researcher and specialist on the classification of 75.3% of 716 symptoms was obtained (Kappa=0.563). Responses from patients prescribed antidepressant drugs were more likely to include symptoms potentially caused by these drugs (74.5% of all symptoms reported) than those from patients prescribed analgesics (67.4%) or antiepileptics (65.1%, chi2 = 23.858, d.f. = 2, P < 0.001). Patients reporting large numbers of symptoms were more likely to report some which were classed as unlikely to be an ADR or unattributable (chi2 = 80.587, d.f. = 3, P < 0.001). Of the 742 reporting symptoms in questionnaires, 402 (54.2%) claimed to have reported some or all of these to their doctor. Only 162 (22.6%) of 716 patient-reported symptoms were documented in the primary care medical records of 103 patients prescribed tramadol or venlafaxine. CONCLUSIONS:Respondents were clearly willing to report symptoms, the majority of which were classed as possibly/probably related to the drugs studied. The results suggest that patients do not report all symptoms they suspect to be ADRs to their GP and that GPs do not record all symptoms which may be reported to them. The method could help to identify problems which patients perceive as being related to their drug therapy and contribute to increased ADR reporting.
| S-EPMC1874308 | biostudies-literature
Project description:BACKGROUND: Healthcare professional's knowledge and attitudes to adverse drug reaction (ADR) and ADR reporting play vital role to report any cases of ADR. Positive attitudes may favour ADR reporting by healthcare professionals. This study was aimed to investigate the attitudes towards and ways to improve adverse drug reaction (ADR) reporting among healthcare professionals working at four Regional Pharmacovigilance Centres (RPCs) of Nepal. METHODS: A cross sectional study was done by survey using a self-administered structured questionnaire. The questionnaire was distributed to 450 healthcare professionals working at four RPCs. RESULTS: The overall response rate was 74.0%. There were 74.8% of healthcare professionals who had seen patient experiencing an ADR; however, only 20.1% had reported. Reporting form not available (48.1%) and other colleagues not reporting ADR cases (46.9%) would significantly discourage the ADR reporting among healthcare professionals working at four RPCs. Healthcare professionals perceived that seriousness of the reaction (75.6%); unusual reaction (64.6%); reaction to new product (71.2%); new reaction to existing product (70.2%); and confidence in diagnosis of ADR (60.8%) were important factors on the decision to report ADR. Awareness among healthcare professionals (85.9%), training (76.0%), collaboration (67.0%), and involve pharmacist for ADR reporting (63.1%) were mostly recognized ways to improve reporting. Regular newsletter on current awareness in drug safety (71.2%), information on new ADR (65.8%), and international drug safety information (64.0%) were the identified feedbacks they would like to receive from the Nepal pharmacovigilance programme. CONCLUSION: Healthcare professionals working at four RPCs of Nepal have positive attitudes towards ADR reporting. Awareness among healthcare professionals, training and collaboration would likely improve reporting provided they would receive appropriate feedback from the national pharamcovigilance programme.
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Project description:While there is significant enthusiasm in the medical community about the use of artificial intelligence (AI) technologies in healthcare, few research studies have sought to assess patient perspectives on these technologies. We conducted 15 focus groups examining patient views of diverse applications of AI in healthcare. Our results indicate that patients have multiple concerns, including concerns related to the safety of AI, threats to patient choice, potential increases in healthcare costs, data-source bias, and data security. We also found that patient acceptance of AI is contingent on mitigating these possible harms. Our results highlight an array of patient concerns that may limit enthusiasm for applications of AI in healthcare. Proactively addressing these concerns is critical for the flourishing of ethical innovation and ensuring the long-term success of AI applications in healthcare.
| S-EPMC8455556 | biostudies-literature
Project description:IntroductionAdverse drug reaction reports are usually manually assessed by pharmacovigilance experts to detect safety signals associated with drugs. With the recent extension of reporting to patients and the emergence of mass media-related sanitary crises, adverse drug reaction reports currently frequently overwhelm pharmacovigilance networks. Artificial intelligence could help support the work of pharmacovigilance experts during such crises, by automatically coding reports, allowing them to prioritise or accelerate their manual assessment. After a previous study showing first results, we developed and compared state-of-the-art machine learning models using a larger nationwide dataset, aiming to automatically pre-code patients' adverse drug reaction reports.ObjectivesWe aimed to determine the best artificial intelligence model identifying adverse drug reactions and assessing seriousness in patients reports from the French national pharmacovigilance web portal.MethodsReports coded by 27 Pharmacovigilance Centres between March 2017 and December 2020 were selected (n = 11,633). For each report, the Portable Document Format form containing free-text information filled by the patient, and the corresponding encodings of adverse event symptoms (in Medical Dictionary for Regulatory Activities Preferred Terms) and seriousness were obtained. This encoding by experts was used as the reference to train and evaluate models, which contained input data processing and machine-learning natural language processing to learn and predict encodings. We developed and compared different approaches for data processing and classifiers. Performance was evaluated using receiver operating characteristic area under the curve (AUC), F-measure, sensitivity, specificity and positive predictive value. We used data from 26 Pharmacovigilance Centres for training and internal validation. External validation was performed using data from the remaining Pharmacovigilance Centres during the same period.ResultsInternal validation: for adverse drug reaction identification, Term Frequency-Inverse Document Frequency (TF-IDF) + Light Gradient Boosted Machine (LGBM) achieved an AUC of 0.97 and an F-measure of 0.80. The Cross-lingual Language Model (XLM) [transformer] obtained an AUC of 0.97 and an F-measure of 0.78. For seriousness assessment, FastText + LGBM achieved an AUC of 0.85 and an F-measure of 0.63. CamemBERT (transformer) + Light Gradient Boosted Machine obtained an AUC of 0.84 and an F-measure of 0.63. External validation for both adverse drug reaction identification and seriousness assessment tasks yielded consistent and robust results.ConclusionsOur artificial intelligence models showed promising performance to automatically code patient adverse drug reaction reports, with very similar results across approaches. Our system has been deployed by national health authorities in France since January 2021 to facilitate pharmacovigilance of COVID-19 vaccines. Further studies will be needed to validate the performance of the tool in real-life settings.
| S-EPMC9112264 | biostudies-literature
Project description:Purpose:Next-generation sequencing technologies are actively applied in clinical oncology. Bioinformatics pipeline analysis is an integral part of this process; however, humans cannot yet realize the full potential of the highly complex pipeline output. As a result, the decision to include a variant in the final report during routine clinical sign-out remains challenging. Methods:We used an artificial intelligence approach to capture the collective clinical sign-out experience of six board-certified molecular pathologists to build and validate a decision support tool for variant reporting. We extracted all reviewed and reported variants from our clinical database and tested several machine learning models. We used 10-fold cross-validation for our variant call prediction model, which derives a contiguous prediction score from 0 to 1 (no to yes) for clinical reporting. Results:For each of the 19,594 initial training variants, our pipeline generates approximately 500 features, which results in a matrix of > 9 million data points. From a comparison of naive Bayes, decision trees, random forests, and logistic regression models, we selected models that allow human interpretability of the prediction score. The logistic regression model demonstrated 1% false negativity and 2% false positivity. The final models' Youden indices were 0.87 and 0.77 for screening and confirmatory cutoffs, respectively. Retraining on a new assay and performance assessment in 16,123 independent variants validated our approach (Youden index, 0.93). We also derived individual pathologist-centric models (virtual consensus conference function), and a visual drill-down functionality allows assessment of how underlying features contributed to a particular score or decision branch for clinical implementation. Conclusion:Our decision support tool for variant reporting is a practically relevant artificial intelligence approach to harness the next-generation sequencing bioinformatics pipeline output when the complexity of data interpretation exceeds human capabilities.
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Project description:Objectives The aim of this study was to evaluate the quality of reporting of randomised controlled trials (RCTs) of artificial intelligence (AI) in healthcare against Consolidated Standards of Reporting Trials—AI (CONSORT-AI) guidelines. Design Systematic review. Data sources We searched PubMed and EMBASE databases for studies reported from January 2015 to December 2021. Eligibility criteria We included RCTs reported in English that used AI as the intervention. Protocols, conference abstracts, studies on robotics and studies related to medical education were excluded. Data extraction The included studies were graded using the CONSORT-AI checklist, comprising 43 items, by two independent graders. The results were tabulated and descriptive statistics were reported. Results We screened 1501 potential abstracts, of which 112 full-text articles were reviewed for eligibility. A total of 42 studies were included. The number of participants ranged from 22 to 2352. Only two items of the CONSORT-AI items were fully reported in all studies. Five items were not applicable in more than 85% of the studies. Nineteen per cent (8/42) of the studies did not report more than 50% (21/43) of the CONSORT-AI checklist items. Conclusions The quality of reporting of RCTs in AI is suboptimal. As reporting is variable in existing RCTs, caution should be exercised in interpreting the findings of some studies.
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Project description:(1) Background: AI-based solutions could become crucial for the prediction of pregnancy disorders and complications. This study investigated the evidence for applying artificial intelligence methods in obstetric pregnancy risk assessment and adverse pregnancy outcome prediction. (2) Methods: Authors screened the following databases: Pubmed/MEDLINE, Web of Science, Cochrane Library, EMBASE, and Google Scholar. This study included all the evaluative studies comparing artificial intelligence methods in predicting adverse pregnancy outcomes. The PROSPERO ID number is CRD42020178944, and the study protocol was published before this publication. (3) Results: AI application was found in nine groups: general pregnancy risk assessment, prenatal diagnosis, pregnancy hypertension disorders, fetal growth, stillbirth, gestational diabetes, preterm deliveries, delivery route, and others. According to this systematic review, the best artificial intelligence application for assessing medical conditions is ANN methods. The average accuracy of ANN methods was established to be around 80-90%. (4) Conclusions: The application of AI methods as a digital software can help medical practitioners in their everyday practice during pregnancy risk assessment. Based on published studies, models that used ANN methods could be applied in APO prediction. Nevertheless, further studies could identify new methods with an even better prediction potential.
| S-EPMC9690973 | biostudies-literature
Project description:BackgroundArtificial intelligence (AI) enables remote patient monitoring (RPM) which reduces costs by triaging patients to optimize hospitalization and avoid complications. The FDA regulates AI in medical devices and aims to ensure patient safety, effectiveness, and transparent AI solutions.ObjectivesIdentify and summarize FDA approved RPM devices to provide information for the US medical device industry based on previous approvals and the markets' needs.MethodsWe searched publicly available databases on FDA-approved RPM devices. Selection criteria were established to classify a solution as AI. Technical information was analyzed on pre-identified 16 parameters for the qualified solutions.ResultsA total of 47 RPM devices were reviewed, among which 12.8% were classified as a De Novo product and the remaining devices fell under the 510(K) FDA category. The cardiovascular (74%) AI RPM solutions dominated the US market, followed by ECG-based arrhythmia detection algorithms (59.4%), and Hemodynamics and Vital Sign monitoring algorithms (21.9%). The trend observed in the FDA rejected devices was their inability to be classified into clinically relevant categories (Criteria 2 and 3).ConclusionThe market needs more innovative RPM solutions under the De Novo category, as there are very few. The transparency is low on the technical aspect of AI algorithms. The market needs AI algorithms that can effectively classify patients rather than merely improve device functionality.
| S-EPMC10158563 | biostudies-literature
Project description:ObjectiveTo assess extent and determinants of past-month recognition of suspected adverse drug reactions (ADR) and past-year ADR reporting among healthcare professionals (HCPs) in Uganda.SettingGeographically diverse health facilities (public, private for-profit, private not-for-profit).ParticipantsOf 2000 questionnaires distributed, 1345 were completed: return rate of 67%.Primary and secondary outcome measuresPer cent HCPs who suspected ADR in the past month; reported ADR in the past year.ResultsNurses were the majority (59%, 792/1345). Only half the respondents had heard about pharmacovigilance: 39% of nurses (295/763; 95% CI 35% to 42%), 70% otherwise (383/547; 95% CI 66% to 74%). One fifth (268/1289 or 21%; 95% CI 19% to 23%) had suspected an ADR in the previous 4 weeks, 111 of them were nurses; 15% (190/1296) had reported a suspected ADR in the past year, 103 of them were nurses. Past-month ADR suspicion was more likely by non-nurses (OR=1.7, 95% CI 1.16 to 2.40) and with medical research involvement (OR=1.5, 95% CI 1.05 to 2.15) but past-month receipt of patient ADR-complaint predominated (OR=19, 95% CI 14 to 28). Past-year ADR reporting was higher by hospital staff (OR=1.9, 95% CI 1.18 to 3.10), especially in medicine (OR=2.3, 95% CI 1.08 to 4.73); but lower from private for-profit health facilities (OR=0.5, 95% CI 0.28 to 0.77) and by older staff (OR=0.6, 95% CI 0.43 to 0.91); more likely by HCPs who had ever encountered a fatal ADR (OR=2.9, 95% CI 1.94 to 4.25), knew to whom to report (OR=1.7, 95% CI 1.18 to 2.46), or suggested how to improve ADR reporting (OR=1.6, 95% CI 1.04 to 2.49). Two attitudinal factors were important: diffidence and lethargy.ConclusionsOne in five HCPs suspected an ADR in the past-month and one in seven reported ADR in the previous year. Empowering patients could strengthen ADR detection and reporting in Africa.
| S-EPMC4244492 | biostudies-literature