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Editorial

Natural language processing – relevance to patient outcomes and real-world evidence

, &
Pages 5-9 | Received 07 Jul 2023, Accepted 23 Oct 2023, Published online: 30 Oct 2023

References

  • Liddy ED. Natural language processing. In: editors, Levine-Clark M, and McDonald J, Eds. Fourth ed. Encyclopedia of library and Information Sciences. Boca Raton FL: CRC Press; 2018. p. 3–4.
  • Nadkarni PM, Ohno-Machado L, Chapman WW. Natural language processing: an introduction. J Am Med Inform Assoc. 2011;18(5):544–551. doi: 10.1136/amiajnl-2011-000464
  • Laparra E, Mascio A, Velupillai S, et al. A review of recent work in transfer learning and domain adaptation for natural language processing of electronic health records. Yearb Med Inform. 2021;30(1):239–244. doi: 10.1055/s-0041-1726522
  • Malte A, Ratadiya P. Evolution of transfer learning in natural language processing. arXiv Published Online First: 2019. doi: 10.48550/arxiv.1910.07370
  • Jurafsky D, Martin JH. Speech and language processing. 2nd ed. Upper Saddle River, NJ, USA: Prentice-Hall Inc.; 2008.
  • Wang Y, Sohn S, Liu S, et al. A clinical text classification paradigm using weak supervision and deep representation. BMC Med Inform Decis Mak. 2019;19:1. doi: 10.1186/s12911-018-0723-6
  • Kalchbrenner N, Grefenstette E, Blunsom P. A convolutional neural network for modelling sentences. Proc 52nd Annual Meeting Assoc Comput Linguist. 2014;1:655–665.
  • Mithun S, Jha AK, Sherkhane UB, et al. Development and validation of deep learning and BERT models for classification of lung cancer radiology reports. IMU. 2023;40:101294. doi: 10.1016/j.imu.2023.101294
  • Choi E, Bahadori MT, Schuetz A, et al. Doctor AI: predicting clinical events via recurrent neural networks. JMLR Workshop Conf Proc. 2016;56:301–318.
  • Suresh H, Hunt N, Johnson A, et al. Clinical intervention prediction and understanding with deep neural networks. Proc 2nd Mach Learn Healthcare Conf. 2017;68:322–337.
  • Zhang XS, Tang F, Dodge HH, et al. MetaPred: meta-learning for clinical risk prediction with limited patient electronic health records. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining; New York, NY, USA. Association for Computing Machinery; 2019. p. 2487–2495.
  • Rasmy L, Xiang Y, Xie Z, et al. Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. NPJ Digit Med. 2021;4:86. doi: 10.1038/s41746-021-00455-y
  • Cho H, Lee H. Biomedical named entity recognition using deep neural networks with contextual information. BMC Bioinf. 2019;20(1):735. doi: 10.1186/s12859-019-3321-4
  • Du N, Chen K, Kannan A, et al. Extracting symptoms and their status from clinical conversations. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics 2019: p. 915–925. Association for Computational Linguistics, Florence, Italy
  • Kersloot MG, Lau F, Abu-Hanna A, et al. Automated SNOMED CT concept and attribute relationship detection through a web-based implementation of cTAKES. J Biomed Semant. 2019;10(1):14. doi: 10.1186/s13326-019-0207-3
  • Peterson KJ, Liu H. Automating the transformation of free-text clinical problems into SNOMED CT expressions. AMIA Jt Summits Transl Sci Proc. 2020;2020:497–506.
  • Kim E, Rubinstein SM, Nead KT, et al. The evolving use of electronic health records (EHR) for research. Semin Radiat Oncol. 2019;29(4):354–361. doi: 10.1016/j.semradonc.2019.05.010
  • Ford E, Curlewis K, Squires E, et al. The potential of research drawing on clinical free text to bring benefits to patients in the United Kingdom: a systematic review of the literature. Front Digit Health. 2021;3:606599. doi: 10.3389/fdgth.2021.606599
  • Wu H, Wang M, Wu J, et al. A survey on clinical natural language processing in the United Kingdom from 2007 to 2022. npj Digital Med. 2022;5(1):186. doi: 10.1038/s41746-022-00730-6
  • Stewart R, Soremekun M, Perera G, et al. The South London and Maudsley NHS Foundation Trust Biomedical research Centre (SLAM BRC) case register: development and descriptive data. BMC Psychiatry. 2009;9(1):51. doi: 10.1186/1471-244X-9-51
  • Perera G, Broadbent M, Callard F, et al. Cohort profile of the South London and Maudsley NHS Foundation Trust Biomedical research Centre (SLaM BRC) case register: current status and recent enhancement of an electronic mental health record-derived data resource. BMJ Open. 2016;6(3):e008721. doi: 10.1136/bmjopen-2015-008721
  • Khapre S, Stewart R, Taylor C. An evaluation of symptom domains in the 2 years before pregnancy as predictors of relapse in the perinatal period in women with severe mental illness. Eur Psychiatry. 2021;64(1):e26. doi: 10.1192/j.eurpsy.2021.18
  • Kadra G, Stewart R, Shetty H, et al. Long-term antipsychotic polypharmacy prescribing in secondary mental health care and the risk of mortality. Acta Psychiatr Scand. 2018;138(2):123–132. doi: 10.1111/acps.12906
  • Chen J, Perera G, Shetty H, et al. Body mass index and mortality in patients with schizophrenia spectrum disorders: a cohort study in a South London catchment area. Gen Psych. 2022;35(5):e100819. doi: 10.1136/gpsych-2022-100819
  • Bishara D, Perera G, Harwood D, et al. Centrally acting anticholinergic drugs used for urinary conditions associated with worse outcomes in dementia. J Am Med Dir Assoc. 2021;22(12):2547–2552. doi: 10.1016/j.jamda.2021.08.011
  • Patel R, Wilson R, Jackson R, et al. Association of cannabis use with hospital admission and antipsychotic treatment failure in first episode psychosis: an observational study. BMJ Open. 2016;6(3):e009888. doi: 10.1136/bmjopen-2015-009888
  • Bendayan R, Kraljevic Z, Shaari S, et al. Mapping multimorbidity in individuals with schizophrenia and bipolar disorders: evidence from the South London and Maudsley NHS Foundation Trust Biomedical research Centre (SLAM BRC) case register. BMJ Open. 2022;12(1):e054414. doi: 10.1136/bmjopen-2021-054414
  • Parmar M, Ma R, Attygalle S, et al. Associations between loneliness and acute hospitalisation outcomes among patients receiving mental healthcare in South London: a retrospective cohort study. Soc Psychiatry Psychiatr Epidemiol. 2022;57(2):397–410. doi: 10.1007/s00127-021-02079-9
  • Botelle R, Bhavsar V, Kadra-Scalzo G, et al. Can natural language processing models extract and classify instances of interpersonal violence in mental healthcare electronic records: an applied evaluative study. BMJ Open. 2022;12(2):e052911. doi: 10.1136/bmjopen-2021-052911
  • Morris RM, Sellwood W, Edge D, et al. Ethnicity and impact on the receipt of cognitive-behavioural therapy in people with psychosis or bipolar disorder: an English cohort study. BMJ Open. 2020;10:e034913. doi: 10.1136/bmjopen-2019-034913
  • Jackson RG, Patel R, Jayatilleke N, et al. Natural language processing to extract symptoms of severe mental illness from clinical text: the clinical record interactive search comprehensive data extraction (CRIS-CODE) project. BMJ Open. 2017;7(1):e012012. doi: 10.1136/bmjopen-2016-012012
  • Jackson R, Kartoglu I, Stringer C, et al. CogStack - experiences of deploying integrated information retrieval and extraction services in a large National health Service Foundation Trust hospital. BMC Med Inform Decis Mak. 2018;18(1):47. doi: 10.1186/s12911-018-0623-9
  • Dr Rob Harland at the KHP Annual Conference 2021 - YouTube. (accessed 2023 Jun 23) https://www.youtube.com/watch?v=k6BcDfAJ0R4&feature=youtu.be
  • Perera G, Soremekun M, Breen G, et al. The psychiatric case register: noble past, challenging present, but exciting future. Br J Psychiatry. 2009;195:191–193. doi: 10.1192/bjp.bp.109.068452
  • Kendrick T, Stuart B, Newell C, et al. Changes in rates of recorded depression in English primary care 2003-2013: time trend analyses of effects of the economic recession, and the GP contract quality outcomes framework (QOF). J Affect Disord. 2015;180:68–78. doi: 10.1016/j.jad.2015.03.040
  • Garg N, Schiebinger L, Jurafsky D, et al. Word embeddings quantify 100 years of gender and ethnic stereotypes. Proc Natl Acad Sci, USA. 2018;115:E3635–44. doi: 10.1073/pnas.1720347115
  • Don’t Walk OJ B, Reyes Nieva H, Lee S-J, et al. A scoping review of ethics considerations in clinical natural language processing. JAMIA Open. 2022;5:ooac039. doi: 10.1093/jamiaopen/ooac039
  • Weissman GE, Ungar LH, Harhay MO, et al. Construct validity of six sentiment analysis methods in the text of encounter notes of patients with critical illness. J Biomed Informat. 2019;89:114–121. doi: 10.1016/j.jbi.2018.12.001
  • Shah ND, Steyerberg EW, Kent DM. Big data and predictive analytics: recalibrating expectations. JAMA. 2018;320(1):27–28. doi: 10.1001/jama.2018.5602
  • Navarro CLA, Damen JAA, Takada T, et al. Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review. BMJ. 2021;375:n2281. doi: 10.1136/bmj.n2281

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