Machine Learning Techniques for Supporting Decision Making in Differential Diagnosis

David Zachary Hafner, Alejandro Molina Villegas, Edwyn Javier Aldana Bobadilla, Melesio Crespo Sánchez

Research output: Contribution to conferenceOther

Abstract

The general objective is to complete a pilot study into the feasibility of using computational algorithms of natural language processing (NPL) to analyze the free speech of psychiatric patients in order to detect language and prosody-based variables. The first step involves gathering data, the second entails selecting proper NLP techniques, and the third deploying software tools to instantiate metrics and models. The data will be obtained from de-identified information from the Indiana psychiatric illness interview, guided clinical interviews, structured clinical interviews, dynamic interviews, and thematic apperception tests.
As Latent Semantic Analysis has aided in predicting the risk of future psychotic episodes in an at risk population (Bedi et al., 2015), we hypothesize that other techniques of NLP will help to identify linguistic features for various psychiatric states found in mood disorders, personality disorders, and the spectrum of psychosis. We will explore the extent to which these computational tools are able to function simultaneously on the level of content – semantic incoherence, mentalization, fear of understanding, doing and undoing-, as well as at the level of form: literality, derailment, thought blocking, perseveration, and underlying grammatical structure.
In our times, technology, data gathering and decision support models are more and more integrated into society, with artists such as Gibson and Vinge foretelling of a society where technological advances rework social links, forms of communication, and increase body plasticity. In the rapidly expanding field of Computer Science on the approach towards AIs, we consider the integration of psychoanalytic knowledge with machine learning algorithms to be quite important.
Original languageEnglish
Publication statusPublished - 25 Oct 2019
EventAssociation for the Psychoanalysis of Culture & Society 2019 Annual Conference: Displacement: Precarity & Community - Rutgers University Inn and Conference Center, New Brunswick, United States
Duration: 25 Oct 201927 Oct 2019
https://www.apcsweb.net/annual-conference/

Conference

ConferenceAssociation for the Psychoanalysis of Culture & Society 2019 Annual Conference
Abbreviated titleAPCS 2019
CountryUnited States
CityNew Brunswick
Period25/10/1927/10/19
Internet address

Fingerprint

Learning systems
Decision making
Semantics
Derailments
Linguistics
Computer science
Learning algorithms
Plasticity
Communication
Processing
Psychiatry

Cite this

Hafner, D. Z., Molina Villegas, A., Aldana Bobadilla, E. J., & Crespo Sánchez, M. (2019). Machine Learning Techniques for Supporting Decision Making in Differential Diagnosis. Association for the Psychoanalysis of Culture & Society 2019 Annual Conference, New Brunswick, United States.
Hafner, David Zachary ; Molina Villegas, Alejandro ; Aldana Bobadilla, Edwyn Javier ; Crespo Sánchez, Melesio. / Machine Learning Techniques for Supporting Decision Making in Differential Diagnosis. Association for the Psychoanalysis of Culture & Society 2019 Annual Conference, New Brunswick, United States.
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Hafner, DZ, Molina Villegas, A, Aldana Bobadilla, EJ & Crespo Sánchez, M 2019, 'Machine Learning Techniques for Supporting Decision Making in Differential Diagnosis' Association for the Psychoanalysis of Culture & Society 2019 Annual Conference, New Brunswick, United States, 25/10/19 - 27/10/19, .

Machine Learning Techniques for Supporting Decision Making in Differential Diagnosis. / Hafner, David Zachary; Molina Villegas, Alejandro; Aldana Bobadilla, Edwyn Javier; Crespo Sánchez, Melesio.

2019. Association for the Psychoanalysis of Culture & Society 2019 Annual Conference, New Brunswick, United States.

Research output: Contribution to conferenceOther

TY - CONF

T1 - Machine Learning Techniques for Supporting Decision Making in Differential Diagnosis

AU - Hafner, David Zachary

AU - Molina Villegas, Alejandro

AU - Aldana Bobadilla, Edwyn Javier

AU - Crespo Sánchez, Melesio

PY - 2019/10/25

Y1 - 2019/10/25

N2 - The general objective is to complete a pilot study into the feasibility of using computational algorithms of natural language processing (NPL) to analyze the free speech of psychiatric patients in order to detect language and prosody-based variables. The first step involves gathering data, the second entails selecting proper NLP techniques, and the third deploying software tools to instantiate metrics and models. The data will be obtained from de-identified information from the Indiana psychiatric illness interview, guided clinical interviews, structured clinical interviews, dynamic interviews, and thematic apperception tests.As Latent Semantic Analysis has aided in predicting the risk of future psychotic episodes in an at risk population (Bedi et al., 2015), we hypothesize that other techniques of NLP will help to identify linguistic features for various psychiatric states found in mood disorders, personality disorders, and the spectrum of psychosis. We will explore the extent to which these computational tools are able to function simultaneously on the level of content – semantic incoherence, mentalization, fear of understanding, doing and undoing-, as well as at the level of form: literality, derailment, thought blocking, perseveration, and underlying grammatical structure.In our times, technology, data gathering and decision support models are more and more integrated into society, with artists such as Gibson and Vinge foretelling of a society where technological advances rework social links, forms of communication, and increase body plasticity. In the rapidly expanding field of Computer Science on the approach towards AIs, we consider the integration of psychoanalytic knowledge with machine learning algorithms to be quite important.

AB - The general objective is to complete a pilot study into the feasibility of using computational algorithms of natural language processing (NPL) to analyze the free speech of psychiatric patients in order to detect language and prosody-based variables. The first step involves gathering data, the second entails selecting proper NLP techniques, and the third deploying software tools to instantiate metrics and models. The data will be obtained from de-identified information from the Indiana psychiatric illness interview, guided clinical interviews, structured clinical interviews, dynamic interviews, and thematic apperception tests.As Latent Semantic Analysis has aided in predicting the risk of future psychotic episodes in an at risk population (Bedi et al., 2015), we hypothesize that other techniques of NLP will help to identify linguistic features for various psychiatric states found in mood disorders, personality disorders, and the spectrum of psychosis. We will explore the extent to which these computational tools are able to function simultaneously on the level of content – semantic incoherence, mentalization, fear of understanding, doing and undoing-, as well as at the level of form: literality, derailment, thought blocking, perseveration, and underlying grammatical structure.In our times, technology, data gathering and decision support models are more and more integrated into society, with artists such as Gibson and Vinge foretelling of a society where technological advances rework social links, forms of communication, and increase body plasticity. In the rapidly expanding field of Computer Science on the approach towards AIs, we consider the integration of psychoanalytic knowledge with machine learning algorithms to be quite important.

M3 - Other

ER -

Hafner DZ, Molina Villegas A, Aldana Bobadilla EJ, Crespo Sánchez M. Machine Learning Techniques for Supporting Decision Making in Differential Diagnosis. 2019. Association for the Psychoanalysis of Culture & Society 2019 Annual Conference, New Brunswick, United States.